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Exploring new methods for increasing safety and reliability of autonomous vehicles

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When we think of getting on the road in our cars, our first thoughts may not be that fellow drivers are particularly safe or careful — but human drivers are more reliable than one may expect. For each fatal car crash in the United States, motor vehicles log a whopping hundred million miles on the road.

Human reliability also plays a role in how autonomous vehicles are integrated in the traffic system, especially around safety considerations. Human drivers continue to surpass autonomous vehicles in their ability to make quick decisions and perceive complex environments: Autonomous vehicles are known to struggle with seemingly common tasks, such as taking on- or off-ramps, or turning left in the face of oncoming traffic. Despite these enormous challenges, embracing autonomous vehicles in the future could yield great benefits, like clearing congested highways; enhancing freedom and mobility for non-drivers; and boosting driving efficiency, an important piece in fighting climate change.

MIT engineer Cathy Wu envisions ways that autonomous vehicles could be deployed with their current shortcomings, without experiencing a dip in safety. “I started thinking more about the bottlenecks. It’s very clear that the main barrier to deployment of autonomous vehicles is safety and reliability,” Wu says.

One path forward may be to introduce a hybrid system, in which autonomous vehicles handle easier scenarios on their own, like cruising on the highway, while transferring more complicated maneuvers to remote human operators. Wu, who is a member of the Laboratory for Information and Decision Systems (LIDS), a Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering (CEE) and a member of the MIT Institute for Data, Systems, and Society (IDSS), likens this approach to air traffic controllers on the ground directing commercial aircraft.

In a paper published April 12 in IEEE Transactions on Robotics , Wu and co-authors Cameron Hickert and Sirui Li (both graduate students at LIDS) introduced a framework for how remote human supervision could be scaled to make a hybrid system efficient without compromising passenger safety. They noted that if autonomous vehicles were able to coordinate with each other on the road, they could reduce the number of moments in which humans needed to intervene.

Humans and cars: finding a balance that’s just right

For the project, Wu, Hickert, and Li sought to tackle a maneuver that autonomous vehicles often struggle to complete. They decided to focus on merging, specifically when vehicles use an on-ramp to enter a highway. In real life, merging cars must accelerate or slow down in order to avoid crashing into cars already on the road. In this scenario, if an autonomous vehicle was about to merge into traffic, remote human supervisors could momentarily take control of the vehicle to ensure a safe merge. In order to evaluate the efficiency of such a system, particularly while guaranteeing safety, the team specified the maximum amount of time each human supervisor would be expected to spend on a single merge. They were interested in understanding whether a small number of remote human supervisors could successfully manage a larger group of autonomous vehicles, and the extent to which this human-to-car ratio could be improved while still safely covering every merge.

With more autonomous vehicles in use, one might assume a need for more remote supervisors. But in scenarios where autonomous vehicles coordinated with each other, the team found that cars could significantly reduce the number of times humans needed to step in. For example, a coordinating autonomous vehicle already on a highway could adjust its speed to make room for a merging car, eliminating a risky merging situation altogether.

The team substantiated the potential to safely scale remote supervision in two theorems. First, using a mathematical framework known as queuing theory, the researchers formulated an expression to capture the probability of a given number of supervisors failing to handle all merges pooled together from multiple cars. This way, the researchers were able to assess how many remote supervisors would be needed in order to cover every potential merge conflict, depending on the number of autonomous vehicles in use. The researchers derived a second theorem to quantify the influence of cooperative autonomous vehicles on surrounding traffic for boosting reliability, to assist cars attempting to merge.

When the team modeled a scenario in which 30 percent of cars on the road were cooperative autonomous vehicles, they estimated that a ratio of one human supervisor to every 47 autonomous vehicles could cover 99.9999 percent of merging cases. But this level of coverage drops below 99 percent, an unacceptable range, in scenarios where autonomous vehicles did not cooperate with each other.

“If vehicles were to coordinate and basically prevent the need for supervision, that’s actually the best way to improve reliability,” Wu says.

Cruising toward the future

The team decided to focus on merging not only because it’s a challenge for autonomous vehicles, but also because it’s a well-defined task associated with a less-daunting scenario: driving on the highway. About half of the total miles traveled in the United States occur on interstates and other freeways. Since highways allow higher speeds than city roads, Wu says, “If you can fully automate highway driving … you give people back about a third of their driving time.”

If it became feasible for autonomous vehicles to cruise unsupervised for most highway driving, the challenge of safely navigating complex or unexpected moments would remain. For instance, “you [would] need to be able to handle the start and end of the highway driving,” Wu says. You would also need to be able to manage times when passengers zone out or fall asleep, making them unable to quickly take over controls should it be needed. But if remote human supervisors could guide autonomous vehicles at key moments, passengers may never have to touch the wheel. Besides merging, other challenging situations on the highway include changing lanes and overtaking slower cars on the road.

Although remote supervision and coordinated autonomous vehicles are hypotheticals for high-speed operations, and not currently in use, Wu hopes that thinking about these topics can encourage growth in the field.

“This gives us some more confidence that the autonomous driving experience can happen,” Wu says. “I think we need to be more creative about what we mean by ‘autonomous vehicles.’ We want to give people back their time — safely. We want the benefits, we don’t strictly want something that drives autonomously.”

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A review on autonomous vehicles: progress, methods and challenges.

1. Introduction

1.1. motivation, 1.2. contribution, 1.3. comparison, 1.4. organization, 2. methods and materials, 2.1. research questions, 2.2. conducting the research, 2.3. screening of papers, 2.4. grouping using keywords, 2.5. mapping the process, 3. background of autonomous vehicles, 3.1. common terms related to autonomous vehicles, 3.2. levels of autonomy, 3.3. sensors used in autonomous vehicles, 3.4. architectures and algorithms, 4. autonomous driving: key technologies, 4.1. environment perception.

• Region selection;
• Feature extraction;
• Classification.

4.2. Pedestrian Detection

• Transforming 3D LiDAR data to a 2D image. This ensures that the accuracy during detection does not get affected by any variation in illumination;
• Building a new dataset for accurately detecting pedestrians outside the field of view of the camera. This helps in improving the safety factor;
• Clustering and filtering the dataset to make the detection of pedestrians more prominent and separate objects from the background;
• A CNN based on PVANET is proposed for increasing the accuracy of pedestrian detection.

4.3. Path Planning

4.4. vehicle cyber security, 4.5. motion control, 5. psychology, 6. challenges, 7. future scope, 8. conclusions, author contributions, conflicts of interest.

• Zhang, T.; Zhang, L.; Zhao, L.; Huang, X.; Hou, Y. Catalytic Effects in the Cathode of Li-S Batteries: Accelerating polysulfides redox conversion. EnergyChem 2020 , 2 , 100036. [ Google Scholar ] [ CrossRef ]
• Li, W.; Guo, X.; Geng, P.; Du, M.; Jing, Q.; Chen, X.; Zhang, G.; Li, H.; Xu, Q.; Braunstein, P.; et al. Rational Design and General Synthesis of Multimetallic Metal–Organic Framework Nano-Octahedra for Enhanced Li–S Battery. Adv. Mater. 2021 , 33 , 2105163. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Geng, P.; Wang, L.; Du, M.; Bai, Y.; Li, W.; Liu, Y.; Chen, S.; Braunstein, P.; Xu, Q.; Pang, H. MIL-96-Al for Li–S Batteries: Shape or Size? Adv. Mater. 2021 , 34 , 2107836. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zheng, S.; Li, Q.; Xue, H.; Pang, H.; Xu, Q. A highly alkaline-stable metal oxide@metal–organic framework composite for high-performance electrochemical energy storage. Natl. Sci. Rev. 2019 , 7 , 305–314. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• WHO. Global Status Report on Road Safety 2018 ; World Health Organization: Geneva, Switzerland, 2018. [ Google Scholar ]
• Yaqoob, I.; Khan, L.U.; Kazmi, S.M.A.; Imran, M.; Guizani, N.; Hong, C.S. Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges. IEEE Netw. 2019 , 34 , 174–181. [ Google Scholar ] [ CrossRef ]
• Yurtsever, E.; Lambert, J.; Carballo, A.; Takeda, K. A Survey of Autonomous Driving: Common Practices and Emerging Technologies. IEEE Access 2020 , 8 , 58443–58469. [ Google Scholar ] [ CrossRef ]
• Gandia, R.M.; Antonialli, F.; Cavazza, B.H.; Neto, A.M.; de Lima, D.A.; Sugano, J.Y.; Nicolai, I.; Zambalde, A.L. Autonomous vehicles: Scientometric and bibliometric review. Transp. Rev. 2018 , 39 , 9–28. [ Google Scholar ] [ CrossRef ]
• Hussain, R.; Zeadally, S. Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Commun. Surv. Tutorials 2018 , 21 , 1275–1313. [ Google Scholar ] [ CrossRef ]
• Faisal, A.; Kamruzzaman, M.; Yigitcanlar, T.; Currie, G. Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy. J. Transp. Land Use 2019 , 12 , 45–72. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• SAE International. Taxonomy and Definitions for Terms Related to Driving Automation Systems for on-Road Motor Vehicles J3016 ; SAE International: Warrendale, PA, USA, 2018; Volume J3016, p. 35. [ Google Scholar ] [ CrossRef ]
• Miglani, A.; Kumar, N. Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Veh. Commun. 2019 , 20 , 100184. [ Google Scholar ] [ CrossRef ]
• Dai, Y.; Lee, S.-G. Perception, Planning and Control for Self-Driving System Based on On-board Sensors. Adv. Mech. Eng. 2020 , 12 , 1687814020956494. [ Google Scholar ] [ CrossRef ]
• Ahmed, M.; Seraj, R.; Islam, S.M.S. The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics 2020 , 9 , 1295. [ Google Scholar ] [ CrossRef ]
• Li, C.; Wang, R.; Li, J.; Fei, L. Face Detection Based on YOLOv3. In Recent Trends in Intelligent Computing, Communication and Devices ; Advances in Intelligent Systems and Computing; Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I., Eds.; Springer: Singapore, 2020; Volume 1006. [ Google Scholar ] [ CrossRef ]
• Zhao, J.; Liang, B.; Chen, Q. The key technology toward the self-driving car. Int. J. Intell. Unmanned Syst. 2018 , 6 , 2–20. [ Google Scholar ] [ CrossRef ]
• Gupta, A.; Anpalagan, A.; Guan, L.; Khwaja, A.S. Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array 2021 , 10 , 100057. [ Google Scholar ] [ CrossRef ]
• Jung, Y.; Seo, S.-W.; Kim, S.-W. Curb Detection and Tracking in Low-Resolution 3D Point Clouds Based on Optimization Framework. IEEE Trans. Intell. Transp. Syst. 2019 , 21 , 3893–3908. [ Google Scholar ] [ CrossRef ]
• Li, G.; Yang, Y.; Qu, X. Deep Learning Approaches on Pedestrian Detection in Hazy Weather. IEEE Trans. Ind. Electron. 2019 , 67 , 8889–8899. [ Google Scholar ] [ CrossRef ]
• Bachute, M.R.; Subhedar, J.M. Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms. Mach. Learn. Appl. 2021 , 6 , 100164. [ Google Scholar ] [ CrossRef ]
• Chen, G.; Mao, Z.; Yi, H.; Li, X.; Bai, B.; Liu, M.; Zhou, H. Pedestrian detection based on panoramic depth map transformed from 3d-lidar data. Period. Polytech. Electr. Eng. Comput. Sci. B 2020 , 64 , 274–285. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bu, F.; Le, T.; Du, X.; Vasudevan, R.; Johnson-Roberson, M. Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection Based on Planar LiDAR and Monocular Images. IEEE Robot. Autom. Lett. 2019 , 5 , 1626–1633. [ Google Scholar ] [ CrossRef ]
• Zhang, S.; Yang, J.; Schiele, B. Occluded pedestrian detection through guided attention in cnns. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 6995–7003. [ Google Scholar ]
• Chiang, K.-W.; Tsai, G.-J.; Li, Y.-H.; Li, Y.; El-Sheimy, N. Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment. Remote Sens. 2020 , 12 , 1564. [ Google Scholar ] [ CrossRef ]
• Sommer, F.; Dürrwang, J.; Kriesten, R. Survey and Classification of Automotive Security Attacks. Information 2019 , 10 , 148. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ring, M.; Dürrwang, J.; Sommer, F.; Kriesten, R. Survey on vehicular attacks-building a vulnerability database. In Proceedings of the 2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Yokohama, Japan, 5–7 November 2015; pp. 208–212. [ Google Scholar ]
• Chowdhury, A.; Karmakar, G.; Kamruzzaman, J.; Jolfaei, A.; Das, R. Attacks on Self-Driving Cars and Their Countermeasures: A Survey. IEEE Access 2020 , 8 , 207308–207342. [ Google Scholar ] [ CrossRef ]
• Rana, M.M. IoT-based electric vehicle state estimation and control algorithms under cyber attacks. IEEE Internet Things J. 2019 , 7 , 874–881. [ Google Scholar ] [ CrossRef ]
• Liu, Q.; Mo, Y.; Mo, X.; Lv, C.; Mihankhah, E.; Wang, D. Secure pose estimation for autonomous vehicles under cyber attacks. In Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 9–12 June 2019; pp. 1583–1588. [ Google Scholar ]
• Yin, G.; Li, J.; Jin, X.; Bian, C.; Chen, N. Integration of Motion Planning and Model-Predictive-Control-Based Control System for Autonomous Electric Vehicles. Transport 2015 , 30 , 353–360. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dang, D.; Gao, F.; Hu, Q. Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling. Appl. Sci. 2020 , 10 , 3180. [ Google Scholar ] [ CrossRef ]
• Liu, S.; Zheng, K.; Zhao, L.; Fan, P. A driving intention prediction method based on hidden Markov model for autonomous driving. Comput. Commun. 2020 , 157 , 143–149. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• García Cuenca, L.; Puertas, E.; Fernandez Andrés, J.; Aliane, N. Autonomous driving in roundabout maneuvers using reinforcement learning with Q-learning. Electronics 2019 , 8 , 1536. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, X.; Qiao, B.; Su, C. Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change. Appl. Sci. 2020 , 10 , 1626. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ortega, J.; Lengyel, H.; Szalay, Z. Overtaking maneuver scenario building for autonomous vehicles with PreScan software. Transp. Eng. 2020 , 2 , 100029. [ Google Scholar ] [ CrossRef ]
• Wasala, A.; Byrne, D.; Miesbauer, P.; O’Hanlon, J.; Heraty, P.; Barry, P. Trajectory based lateral control: A Reinforcement Learning case study. Eng. Appl. Artif. Intell. 2020 , 94 , 103799. [ Google Scholar ] [ CrossRef ]
• Gambino, A.; Sundar, S.S. Acceptance of self-driving cars: Does their posthuman ability make them more eerie or more desirable? In Proceedings of the Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 4–9 May 2019; pp. 1–6. [ Google Scholar ]
• Hong, J.W.; Wang, Y.; Lanz, P. Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. Int. J. Hum. Comput. Interact. 2020 , 36 , 1768–1774. [ Google Scholar ] [ CrossRef ]
• Lee, J.D.; Kolodge, K. Exploring Trust in Self-Driving Vehicles Through Text Analysis. Hum. Factors J. Hum. Factors Ergon. Soc. 2019 , 62 , 260–277. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hong, J.W.; Cruz, I.; Williams, D. AI, you can drive my car: How we evaluate human drivers vs. self-driving cars. Comput. Hum. Behav. 2021 , 125 , 106944. [ Google Scholar ] [ CrossRef ]
• Kumar, N.; Rodrigues, J.J.P.C.; Chilamkurti, N. Bayesian Coalition Game as-a-Service for Content Distribution in Internet of Vehicles. IEEE Internet Things J. 2014 , 1 , 544–555. [ Google Scholar ] [ CrossRef ]
• Kumar, N.; Chilamkurti, N.; Park, J.H. ALCA: Agent learning–based clustering algorithm in vehicular ad hoc networks. Pers. Ubiquitous Comput. 2012 , 17 , 1683–1692. [ Google Scholar ] [ CrossRef ]
• Lee, M.; Atkison, T. VANET applications: Past, present, and future. Veh. Commun. 2020 , 28 , 100310. [ Google Scholar ] [ CrossRef ]
• Kumar, N.; Kaur, K.; Misra, S.C.; Iqbal, R. An intelligent RFID-enabled authentication scheme for healthcare applications in vehicular mobile cloud. Peer-to-Peer Netw. Appl. 2015 , 9 , 824–840. [ Google Scholar ] [ CrossRef ]
• Kumar, N.; Misra, S.; Obaidat, M.S. Collaborative Learning Automata-Based Routing for Rescue Operations in Dense Urban Regions Using Vehicular Sensor Networks. IEEE Syst. J. 2014 , 9 , 1081–1090. [ Google Scholar ] [ CrossRef ]

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Parekh, D.; Poddar, N.; Rajpurkar, A.; Chahal, M.; Kumar, N.; Joshi, G.P.; Cho, W. A Review on Autonomous Vehicles: Progress, Methods and Challenges. Electronics 2022 , 11 , 2162. https://doi.org/10.3390/electronics11142162

Parekh D, Poddar N, Rajpurkar A, Chahal M, Kumar N, Joshi GP, Cho W. A Review on Autonomous Vehicles: Progress, Methods and Challenges. Electronics . 2022; 11(14):2162. https://doi.org/10.3390/electronics11142162

Parekh, Darsh, Nishi Poddar, Aakash Rajpurkar, Manisha Chahal, Neeraj Kumar, Gyanendra Prasad Joshi, and Woong Cho. 2022. "A Review on Autonomous Vehicles: Progress, Methods and Challenges" Electronics 11, no. 14: 2162. https://doi.org/10.3390/electronics11142162

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Slowly rolling out onto city streets, self-driving cars are testing their driving chops in Silicon Valley, Helsinki, London and a few dozen other isolated locations around the globe – and the expectation is that their numbers will only swell.

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Exploring the implications of autonomous vehicles: a comprehensive review

Kareem othman.

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Corresponding author.

Received 2021 Jun 19; Accepted 2022 Feb 5; Issue date 2022.

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

Over the last few years, a large emphasis has been devoted to autonomous vehicles (AVs), as vehicle automation promises a large number of benefits such as: improving mobility and minimization of energy and emissions. Additionally, AVs represent a major tool in the fight against pandemics as autonomous vehicles can be used to transport people while maintaining isolation and sterilization. Thus, manufacturers are racing to introduce AVs as fast as possible. However, laws and regulations are not yet ready for this change and the legal sector is following the development of autonomous vehicles instead of taking the lead. This paper provides a comprehensive review of the previous studies in the transportation field that involve AVs with the aim of exploring the implications of AVs on the safety, public behaviour, land use, economy, society and environment, public health, and benefits of autonomous vehicles in fighting pandemics.

Keywords: Autonomous vehicles, Implications, Public behaviour, Implications on society, Implications on land use, Pandemic

Introduction

Automation of vehicles has always attracted researchers: starting with the vehicle-to-vehicle communication system in the 1920s using radio waves [ 1 ], then the development of the vehicle’s electromagnetic guidance in the 1930s and 1940s, or adding magnets to vehicles for the testing of smart highways during the 1950s and 1960s [ 2 ]. In 1980, Mercedes-Benz partnered with Bundeswehr University in Munich and invented the first autonomous vehicle in the world that opened the way towards thinking about legislation adaptation [ 3 ]. Since this point in time, many companies have started working on developing autonomous vehicles [ 4 ].

Based on the National Highway and Transportation Safety Administration, there are 5 levels of AV functionality: level 0: no automation, level 1: automation of one control function such as lane keep assist or autonomous control, level 2: automation of two control functions, level 3: limited self-driving but expect the driver to take control at any time with adequate warning, level 4: drivers are not expected to take control at any time of the trip, and level 5: full self-driving with no human control [ 5 , 6 ].

In the last decade, autonomous vehicles (AVs) have undergone tremendous improvement as both research and industry are putting significant efforts into developing AVs [ 7 ]. For example, Google launched the Google self-driving car project in 2009 with the vision of providing fully AVs by 2020 [ 8 ]. Uber partnered with Volvo and announced the development of the third version of its self-driving vehicle and they will start testing it by 2020 [ 9 ]. In 2014, Apple launched the AV project “Project Titan” with the vision of providing AVs by 2016; however, many issues such as leadership issue had an impact on the project and now it is expected that Apple car will be in the market between 2023 and 2025 [ 10 ]. Additionally, many start-up companies launched with the aim of developing AVs. In 2014, Zoox was founded to provide electric and autonomous vehicles and its value reached 3.2 $ billion by 2018 [ 11 ]. Moreover, cities allowed testing and deployment of AVs on public roads. For example, in 2018, 29 of the US states allowed testing AVs on their roads. [ 12 , 13 ]. Such pilot studies are mainly intended to test self-driving technology and public attitude and behaviour. However, till the moment there is no large-scale implementation of AV fleet in a given country. Additionally, there are many obstacles that might hinder the introduction of the AVs such as laws and regulations, public acceptance, ethical issues, and the development of the technology itself.

This paper reviews previous studies in the area of autonomous vehicles with the aim of revealing the implications of autonomous vehicles on safety, public behaviour, land use, economy, society, environment, public health.

Methodology and work cited

This state of the art reviews the existing literature on the topic of implications of automated vehicles. The databases and search engines used were Scopus, Web of Science, ScienceDirect, SPRINGER LINK, IEEE Xplore, and TRID. The keywords used were: implications of automated vehicles; autonomous vehicles; self-driving cars; road capacity and automated vehicles; autonomous vehicles and society; autonomous vehicles and economy; autonomous vehicles simulation; agent-based autonomous vehicles; autonomous mobility; and shared autonomous vehicles. Only reports in English were included from 2010 onwards. The obtained studies were screened based on their relevance and topics. Additional papers were obtained from the references of the screened papers. Scopus results indicated the sources, “Transportation Research Part C: Emerging Technologies”, “Transportation Research Record”, “Transportation Research Part A: Policy and Practice”, and “Transportation Research Procedia”, as the most frequent resource used in this study.

Research contribution

Research in AVs has gained significant attention from researchers around the world and research in the implications of AVs is not an exception. There are a large number of studies in the literature that discuss the implications of AVs. Table 1 summarizes some of these studies. Most of these studies focus on the implications of AVs on one branch only, such as the implications of AVs on the environment, and ignore the other implications, which might be misleading. Thus, in this paper, a holistic review or a comprehensive review is conducted, and a holistic analysis is conducted in order to discuss the overall implications of AVs and reveal the intertwined relations between the studied factors. This holistic review provides the chance to explore the implications of AVs in new novel areas such as:

Implications of AVs on the public transit.

Implications of COVID-19 on the public attitude towards AVs.

AVs in developing countries and the challenges that faces AVs in developing countries.

Finally, this study provides a figure that holistically summarizes the strengths, weaknesses, opportunities, threats of AV technology (Figure-5).

Selected AV review papers and their scope

Tables 2 and 3 summarize the results of previous studies that focus on the implications of AVs.

Implications of AVs on public behaviour

Implications of AVs on land use, economy, environment, and society

Implications of AVs on vehicle ownership (fleet size) and vehicle utilization

The adoption of AVs promises many changes. One of these changes is the reduction in the vehicle ownership or increase in the vehicle utilization. Results of previous simulation models show that every single AV can replace a significant number of conventional vehicles, especially if it is used as a shared mode of transportation, which means that a lower fleet size can serve the same population with higher vehicle utilization [ 14 , 15 ]. Studies show significant increase in vehicle utilization from 5% in conventional vehicles [ 16 ] up to 75% [ 17 ]. This increase in vehicle utilization is beneficial as it means shorter life span and adaption of newer and better technology [ 18 ]. Simulations of AVs as a shared mode showed that every AV can replace more than 10 conventional vehicles [ 16 – 21 ]. However, using AVs privately showed a slight reduction in the overall vehicle ownership. For example, the simulation model in Germany by Moreno, Michalski, Llorca, and Moeckel (2018) showed that every AV can replace 2.5 conventional vehicles. This is because of the difference in assumptions between this study and all other studies [ 22 ]. All the previous studies assumed that all trips will be made by SAVs, but Moreno, Michalski, Llorca, and Moeckel (2018) study [ 22 ] mimicked the results of a survey that showed that 24% of respondents would use shared AVs. Consequently, it is more reasonable to rely on the later results (AV = 2.5 conventional vehicles) because it is not realistic to assume that 100% of the trips will be made by shared AVs (SAVs). Over time, the percentage of trips made in shared AVs might change as AV penetration increases and shared AVs prove their high performance and gains for society then the percentage of shared trips might increase and become closer to the percentages stated in the first studies (AV >  = 10 conventional vehicles). Similarly, Zhang, Guhathakurta, and Khalil (2018) showed that sharing AVs between the same household members reduces the vehicle ownership by 20%, which is also a significant reduction, but modest when compared to other studies [ 23 ].

In brief, AVs have the potential to reduce vehicle ownership significantly, even if it used privately. On the other hand, using AVs as a shared mode shows promising results with a significant reduction in the required fleet size to serve the same population. This reduction in fleet size is beneficial for the society and environment because it means lower emissions, better traffic conditions, much higher vehicle utilization, and shorter life span, which in turn means the adoption of newer and cleaner technology quickly.

Singapore provides a realistic example of the potential of AVs to reduce vehicle ownership. Pilot studies in Singapore started in 2015 and the city is known as the world's most AV-friendly country. The city allowed for tests in a wide range of autonomous vehicles for private and public use, that more than 10 companies are testing their vehicles in the city. Results showed that by 2018 (in three years) vehicle ownership reduced by 15% [ 24 ].

One major factor that influences the vehicle ownership is regulations. Regulatory action will be a significant determinant of how AVs could affect the ownership. For example, cities might allow AVs as shared mode only and prevent the private use. Currently, cities focus on testing AVs as a shared mode. For example, the following cities are testing autonomous shuttles or autonomous buses: Texas—USA, Wageningen—the Netherlands, Helsinki—Finland, Paris—France, Shenzhen—China, Sion—Switzerland, Edmonton—Canada. Using AVs as a shared mode of transportation guarantees the maximum benefits.

Quality of the service (waiting time and travel cost) provided by shared AVs (public transit vs. AVs)

Although shared AVs reduce the required fleet and significantly increase the vehicle utilization, it provides high-quality service for the public. One important factor for travellers in shared transportation modes is the average waiting time because people perceive their waiting time considerably much longer than the actual time. Different surveys provided different waiting time to in-vehicle time ratios [ 25 ]. For example, Wardman (2004) found that one-minute waiting is equivalent to 2.5 min of in-vehicle time [ 26 ]. Horowitz (1981) found that the ratio is 1.9 [ 27 ]. Abrantes and Wardman (2011) found that one-minute waiting is equivalent to 1.4 min in vehicle [ 28 ]. Consequently, any reduction in passenger waiting time has a significant influence on customer satisfaction. Currently, the average transit waiting time in the USA is approximately 40 min (31% of their commuter time) [ 29 , 30 ]. The average waiting time in Toronto, Canada, is 20 min [ 31 ].

For the case of shared AVs, results show that shared AVs can provide significantly lower waiting time (5 min on average) when compared with the current transit waiting time and with significantly lower trip costs as shown in Table 4 .

Summary of the studies investigating the waiting time and trip costs of SAVs

In conclusion, shared AVs have the potential to significantly reduce the average waiting time and trip costs when compared with the current transit service, which means that shared AVs will be a strong competitor to the transit service and might attract public transit users. Thus, transit agencies should be aware of this new coming disruption to the transportation system or else they will incur significant losses; in particular, AVs will be available sooner or later.

Implications of AVs on the public behaviour

One of the biggest advantages of AVs is that passengers will be able to be engaged in other activities, which in turn means that the trip time will not be considered as economic loss [ 34 ]. Additionally, AVs have the potential for ridesharing and shifting the personal transportation from personal use to shared use [ 35 , 36 ] as discussed in Sect.  4 (Implications of AVs on Vehicle ownership (Fleet size) and vehicle utilization). This reduction in fleet size means better traffic conditions and mitigation of congestion. On the other hand, AVs will motivate people to make longer trips, travel further, and make additional trips which in turn increase the VKT [ 34 , 37 ]. The increase in the VKT increases the emissions [ 36 ] and fuel consumption [ 38 ]. Results of AV simulation models show that AVs have the potential to significantly increase the VKT for a variety of reasons as shown in Table 5 .

Summary of the studies investigating the impact of AVs on the public behaviour

Additionally, while the mentioned simulation models show that AVs have the potential to increase the VKT, all these studies do not take into account the possibility that AVs might motivate people to make additional trips. Thus, it must be mentioned that the low waiting times and low trip costs of AVs might attract people to make additional trips (induced demand) and might discourage people from making trips using public transportation [ 37 ], which in turn means a significant increase in the VKT, and emissions. Moreover, this additional demand besides with the increase in the VKT might worsen the traffic conditions. Thus, AVs might not solve the congestion problem, but as mentioned before the time spent in the vehicle will not be considered as an economic loss [ 34 ] as passengers will spend their time in productive activities. In conclusion, while AVs have the potential to allow people to be engaged in productive activities during their trips (good influence from the economic perspective), AVs have the potential to motivate people to make additional trips which significantly increase the VKT and emissions (bad influence from the environmental perspective). Thus, while AVs might enhance the economic condition, they might destroy the environment. One major factor that has an impact on the public behaviour is regulations. Regulatory action will be a significant determinant on controlling the change in the public behaviour and in turn the impact of AVs. For example, regulations might allow AVs as a first mile-last mile solution to support public transit service, which guarantees the maximum benefits of AVs.

Implications of AVs on capacities of roads and intersections

AVs have the potential to reduce the distance between the vehicles (distance ahead) and to reduce the lane width [ 40 ] due to the high level of communication between vehicles and the elimination of human factors from the driving process, which in turn means a significant increase in capacity of roads as shown in Table 6 .

Summary of the studies investigating the impact of AVs on the road and intersection capacity

In conclusion, AVs have the potential to increase roads and intersection capacities. On the other hand, it is not expected that this increase can be achieved until a high level of market penetration [ 37 ] as with conventional driving the human factor will dominate for safety issues or for human feeling of safety as people will be sacred to see the vehicle drive close to the vehicle ahead or the vehicle beside. However, in AVs, people might be involved in other activities (sleeping, watching movies, or any other activity) and probably they will not see the surrounding roads, which might allow vehicles to drive close to each other.

Implications of AVs on the land use

AVs will change more than our streets, over time they could change the structure of cities, towns, and neighbourhoods. For example, AVs have the potential to reduce the number of parking spaces needed to serve the population. These freed-up spaces can be used for other purposes and allow for the construction of new developments. Results of the previous simulation models show that AVs have the potential to significantly reduce the parking demand and required parking lots as shown in Table 7 .

Summary of the studies investigating the impact of AVs on the land use

Furthermore, AVs might change the design of parking lots. Theoretical speaking, AVs will park themselves without the need for the door space which could enable 20% more free spaces. Moreover, AVs can block each other and let each other out when necessary as shown in Fig.  1 . A study by Audi suggested that a parking space can take 2.5 times the conventional vehicles using this blocking method [ 43 ]. Thus, AVs have the potential to significantly reduce the number of parking places required in the CBD areas [ 36 ], which reduces congestion of vehicles in search for a parking space [ 37 ], that consumes 4% of the energy (fuel) consumed [ 44 ]. Additionally, the freed-up parking spaces can be used for other purposes. For example, using the parking areas in the real estate industry can increase the value of land use by 5% [ 38 ]. In addition, AVs will not rely on on-street parking but will travel to the nearest off-street parking. Thus, AVs can increase the road capacity as these parking lanes can be used to serve the traffic. Moreover, this reduction in the parking demand might be also associated with changes in houses design as the end house parking spaces might not be needed anymore and can be used for other purposes. In other words, AVs can indirectly increase the areas of houses.

Traditional parking strategy for human-driven vehicles vs. the blocking parking strategy for AVs

Implications of AVs on the energy consumption and emissions

Transportation is the main source of pollution on our planet. Transportation contributed to 29% of greenhouse gas emissions in the USA in 2017. It was found that the transportation sector generates the largest share of greenhouse gas emissions that are primarily generated from the following sources: burning fossil fuel for cars, trucks, ships, trains, and planes. More than 90% of the fuel used in transportation is petroleum-based, which includes primarily gasoline and diesel [ 45 ]. Additionally, it was estimated that road transport contributes to 72% of the total transportation pollutions. Cars alone contribute to 44% of the 72% [ 46 ]. In Canada, transportation contributes to 25% of the total emissions [ 47 ]. Consequently, transportation is the main source of pollution and the search for a sustainable transportation system became critical, and a wide area of research. Previous simulation models show that AVs have the potential to provide a sustainable transportation system and significantly reduce energy and emissions as shown in Table 8 .

Summary of the studies investigating the impact of AVs on the environment

In summary, AVs enable high levels of communication between vehicles, which allow vehicles to travel in platoons. Platoons can reduce 15% of the fuel used [ 35 , 38 ] or up to 20% [ 14 ]. Additionally, AVs make it possible to apply the vehicle optimal traffic assignment to minimize the total travel time and in turn optimize the fuel consumed and reduce the emissions [ 36 ]. Moreover, AVs smooth starts and stops can save 15% of fuel consumption.

Implications of AVs on the economy

It is expected that the impact of AVs will extend beyond the simple crash, and fuel saving into every aspect of the economy. Businesses companies that are unable to adapt to this change may be hurt by the introduction of AVs. For example, AVs will increase the demand for sharing trips and so it will reduce the travel costs dramatically [ 37 ]. In the worst case, AVs might not solve the congestion problems as the additional demand and increase in VKT might offset the increase in the capacity. However, the drivers’ time will not be considered as an economic loss anymore as drivers can spend their trips in productive activity [ 34 , 38 ].

Kockelman and Clements (2017) studied the impact of AVs on the economy of the USA. For software and technology companies, AV software technology is expected to grow from 680$ million in 2025 to 15.8$ billion in 2040. Also, the required mapping process is expected to grow from 530$ million to 10.6$ billion in 2040. This contributes to revenue of 26.4$ billion in 15 years. Moreover, the impact on health care will be enormous. Based on NHTSA 2015, accidents account for 23$ billion as medical expenses in the USA. As a result, assuming 90% reduction in collision rate (due to the elimination of the human error) means a reduction of 20.7 $ billion each year, a reduction in the need for supplies and doctors, and better allocation of personnel to provide better services. Additionally, AVs will increase the value of land use as the land used as parking before can be converted into houses, parks, or other developments. The average value of a parking place in the USA is 6300$ with a total value of all parking spaces of 4.5 $ trillion. However, using the parking areas in the real estate industry will increase the land use value by 5%. The study concluded that Shared AVs have a great impact on the economy (1.2$ trillion in total or 3800$ per person per year) [ 38 ].

Compass Transportation and Technology (2018) evaluated the impact of AVs on the economy of the USA. Results show that the benefits to cost ratio of the AVs is 8:1. Additionally, an improvement of 10% in the transportation network is associated with a two per cent (or a bit more) improvement in overall economic productivity or improvement in GDP [ 42 ]. Securing America’s Future Energy (SAFE) (2018) studies that the impact of AVs on the economy of the USA. Results show that the reductions in collisions, the value of time, fuel consumption, and environmental benefits will contribute to benefits of almost 800$ billion by 2050 [ 42 ]. Additionally, KPMG (2015) examined the impact of AVs on the UK economy. Results show that the economic and social benefits of the AVs can reach 51 £ billion by 2030 (1% increase in GDP) and 121£ billion by 2040 [ 15 ].

Implications of AVs on the society (equity)

AVs have the potential to increase coverage and accessibility for aged and disabled individuals [ 14 , 36 , 42 , 44 , 48 ]. Additionally, AVs can increase the accessibility for people with limited transportation accessibility [ 34 ] such as rural areas or depressed regions [ 14 ].

AVs have the potential to radically change the conventional market. Many jobs will disappear, while new job professions [ 34 ] and new business opportunities [ 14 ] will be available. AVs open new opportunities in a variety of fields such as decision-making software, vehicle cybersecurity, and data opportunities. AVs will provide new opportunities for the digital media as commuters who are used to watch the road will switch to use the digital media features in their automobiles during their trips. Additionally, Internet shopping could receive a large bump from this added free time. It is estimated that AVs will provide 320,000 additional jobs in the UK [ 15 ]. On the other hand, this increase in entertainment flexibility for passengers might reduce the use of radio as people might prefer other activities to do while in the vehicle [ 38 ].

On the other side, people are likely to be replaced by AVs, which have a potentially significant impact on individuals with lower levels of education and income and consequently implications and concerns for equity [ 48 ]. AVs can cause serious loss for truck drivers as the technology would reduce the opportunity for the employment of millions of drivers [ 38 ]. On the other hand, a recent study by Gittleman and Monaco (2020) shows the risk on truck drivers is real, but the projections touted are overstated because companies will not be able to abandon all drivers as drivers do more than just driving and not all the tasks can be automated [ 49 ]. For example, checking for safety problems can be performed by sensors, but AVs cannot fix these issues so dealing with these detected issues requires human interaction.

Implications of AVs on the public health

Implications of AVs on public health could vary depending on many factors such as the type of use or ownership, automation level, and the type of engine such as internal combustion, and hybrid engines. While AVs might increase some risks such as pollution and sedentarism, AVs might reduce morbidity and fatalities from vehicle collisions and might help reshape cities to promote healthy environments. Public health can benefit from the proper regulations if these regulations are implemented before the introduction of AVs in the market. This section explores the benefits and risks of AVs on the public health.

Physical activity

Physical activity related to transportation has been suggested as a main source or strategy for increasing the daily physical activity level [ 50 – 52 ]. The benefits of this active transportation have been recognized in many cities around the world and have shown direct and indirect benefits such as the reduction the noise level and the improvement in the air quality. While it is difficult to predict the impact of AVs on the travel behaviour and their impact on the physical activities, AV simulation models suggest that AVs could increase the VKT to pick up passengers from their location and in turn reduce the physical activity [ 53 ]. Additionally, privately owned AVs might lead to a more dispersed urban growth pattern (sprawl), which in turn increases the average trip length and discourages people from walking or cycling [ 54 ]. On the other hand, if people are willing to share their trips, shared AVs could reduce the VKT when compared to private AVs. Thus, shared AVs are likely to increase the physical activity more than privately owned AVs because this approach needs to be complemented by walking, cycling, or using public transportation.

Air pollution and emissions

It was estimated that around 95% of the world’s population lives in areas that exceed the healthy air requirements provided by the World Health Organization (WHO) [ 55 ]. Motorized vehicles are one of the main sources of air pollution [ 56 , 57 ]. In 2015, it was estimated that 250,000 deaths were related to air pollution from road transportation [ 58 ]. Emissions related to transportation can be classified into two categories: exhaust emissions and non-exhaust emissions.

Exhaust emissions

Three main factors have a strong influence on the implications of AVs on the air pollution as follows: the implications of AVs on the VKT, the reliance on gasoline engines or electric vehicles, and the integration between AVs and public transportation or active transportation modes [ 51 ]. Simulation models show that AVs have the potential to increase the VKT and thus increase the air pollution exposure. Additionally, as gasoline and diesel engines pollute more than electric vehicles do, if AVs are not fully electric, travellers would be exposed to higher pollution levels, and higher air pollution exhaust emissions would then affect the public health. Finally, AVs can increase the air pollution level if AVs use patterns do not facilitate walking, cycling, and transit use. Regulations can play an essential role in the implications of AVs on the air pollution and account for these issues to reduce the negative externalities of motorized transport.

Non-exhaust emissions

Other sources of air pollution sources include brake and tyre wear, road surface wear, and resuspension of road dust. These emissions together might exceed the tailpipe emissions at least in terms of particulate matter [ 56 ]. Additionally, bake and tyre wear have higher oxidation potential when compared to other traffic-related sources, which can be translated into worse environmental conditions. Moreover, as the weight of electric vehicles is more than the weight of non-electric vehicles, electric vehicles have the potential to emit more non-exhaust emissions [ 59 ]. Thus, AVs have the potential to increase the non-exhaust emissions, even with a shift to electric vehicles, because of the increase of the VKT [ 60 ].

Noise associated with traffic has been addressed as a source of multiple health issues such as including sleep disturbance, annoyance, cardiovascular disease, and hypertension [ 61 , 62 ]. In Europe, it is estimated that noise causes 10,000 premature deaths per year [ 63 , 64 ]. AVs using internal combustion engines similar to conventional vehicles could continue to contribute more to road traffic noise. As in the case of air pollution, if AV use results in increasing the VKT, then the noise exposure level would increase [ 53 ]. On the other hand, the use of electric vehicles would reduce the noise level considerably. However, at speeds higher than 50 km/hr electric vehicles and conventional vehicles produce the same noise levels. For example, a Dutch study estimated that a fleet of fully electric vehicles could reduce the urban noise levels significantly by 3–4 dB and reduce annoyance effects by more than 30% [ 65 ]. Thus, electric AVs can reduce the noise level significantly.

Electromagnetic fields

Electric and magnetic fields (EMFs) can be defined as invisible areas of energy (also called radiation) that are generated by electricity. Low- to mid-frequency EMFs are in the non-ionizing radiation part of the electromagnetic spectrum and are not known to damage DNA or cells directly. Previous studies evaluated the relation between the exposure to non-ionizing EMFs and the risk of cancer with no conclusive results [ 66 ]. On the other hand, a recent study by the US National Toxicology Program on male rates showed a clear relation between the exposure to high levels of radiofrequency, such as that used in 2G and 3G cell phones, and the development of heart tumours [ 67 ]. AVs rely on multiple technologies that would increase the exposure to the EMFs, which in turn might worsen the public health.

Substance abuse

In 2019, almost 9.9 million people were reported driving under the influence of drugs [ 68 ]. Laws prohibit driving under the influence of drugs or alcohol. These laws together with the social norms have increased the public awareness and discourage people from abusing these substances while driving [ 69 ]. It is possible that the widespread of AVs would cause laxity in the public attitude towards drugs. In 2017, Australia’s National Transport Commission linked AV passengers with taxi passengers and showed that there might be exemptions from the legal restrictions for AV passengers [ 70 ]. Thus, a clear definition of the capabilities and requirements of AV passengers will need to be addressed and aligned with the future policies. Additionally, the efforts against alcohol and drug abuse should be maintained.

Driving has been addressed as a source of health issues. Recent studies show that driving for long hours causes a high level of stress [ 71 ]. It was found that stress has adverse impacts on the immune, cardiovascular, and nervous systems, among others [ 72 ]. AVs have the potential to decrease the mental workload and stress, thereby producing a more positive set of emotional responses. Thus, AVs could reduce the stress of driving, and enhance the public health.

AVs and pandemics

The global COVID-19 has made radical changes in our world, as people had to adapt to a new lifestyle. Experts believe that this pandemic is a turning point that will accelerate the new digital revolution. Although the pandemic has halted many AV pilot studies [ 73 , 74 ], it is expected that this crisis will accelerate the introduction of AVs as AVs can be useful in emergencies and pandemics as follows:

During the COVID-19 pandemic, China used autonomous vans for food and medical supplies delivery and sanitize streets [ 75 ]. In Beijing, a partnership between Neolix and Apollo was established with the aim of delivering food and medical supplies [ 76 ]. Similarly, in Florida, USA, the Mayo Clinic has started using the AV developed by Beep to transport COVID-19 tests from the testing site to the processing laboratory [ 77 ]. Thus, AVs have proved their value in fighting pandemics so, in the future, AVs can be used as a transportation mean to transport people to grocery stores, healthcare, and pharmacies, while maintaining isolation and sterilization [ 74 , 75 ].

Vayyar, which is a start-up company, is working on developing new smart vehicles that monitor the cleanliness and air quality that measure airborne contaminations for infected passengers. This feature is very useful as it helps in the early detection of infections and diseases [ 75 ].

Finally, AVs can be used for the transportation of people during pandemics and replace the public transit service which is a major source for the outbreak of diseases and viruses. In 2018, Goscé and Johansson studies how public transit affects the spread of viruses in London, and they found a correlation between the use of public transport and the spread of diseases [ 78 ].

As a result, AVs have proved their ability in addressing some of the biggest challenges confronting societies in pandemics. Thus, the previous use cases show that AVs represent a major tool in the fight against pandemics that AVs can provide effective, and safe mobility to help people to move to their essential activities.

Autonomous vehicles in developing countries

While most of the previous work focuses on studies from developed countries, this section sheds light on the implications of AVs and the challenges that face AV deployment in developing countries. However, it must be mentioned that rare studies discuss AVs in developing countries and most of these studies focus only on the public attitude towards AVs (discussed in detail in Sect.  14.2 ). For example, there is no simulation study for AVs in any developing country. One of the main reasons for this lack of studies is the poor infrastructure that does not support the navigation of AVs, which requires huge capital costs or investments to provide a safe infrastructure that supports AV navigation. In this section, a detailed analysis of the infrastructure challenges that face the deployment of AVs in developed countries will be discussed and analysed.

Public attitude towards AVs in developed countries and developing countries

One of the main studies that tested the public attitude towards AVs in an international level is the study by Kyriakidis, et al. (2015) [ 79 ]. This study used an Internet-based survey with 5000 responses from 109 countries to investigate the public acceptance, worries, and willingness to buy partially, highly, and fully automated vehicles. Comparing the results across different countries (in terms of accident statistics, education, and income) shows that people from developed countries are more pessimistic than people from developing countries towards AV adoption. Specifically, respondents from more developed countries are more worried about data transmitting as shown in Fig.  2 that shows the relationship between the Gross Domestic Product (GDP) and the level of comfort towards data transmission. This difference can be explained in terms of the perceived threat level. In general, people from developed countries have more sophisticated computer infrastructure for data misuse that these countries have multiple widely publicized cases, such as some Google cases [ 80 , 81 ] and Facebook cases [ 82 , 83 ], that makes citizens of developed countries may realistically believe that the threat of data misuse exists and is harmful to them. On the other hand, the fatality rates in developing counties are much higher than in developed countries that the current trends indicate that road traffic injuries will become the fifth leading cause of death by 2030, with the difference between high- and low-income countries further magnified [ 84 ]. Thus, according to Maslow's hierarchy of needs [ 85 ], people in low-income countries are mostly concerned with basic physiological and safety needs that make data privacy or transmission a least priority issue for them.

Average level of comfort in transmitting data to tax authorities versus gross domestic product (GDP) for different countries (adopted from [ 79 ])

Another study by Bazilinskyy et al. (2015) investigated the public attitude towards AVs by analysing textual comments resulting from three international previous surveys with 8,862 respondents from 112 countries and these responses were analysed to understand the difference in the public attitude across different countries. In this study, responses were categorized according to the GDP into three different categories: high-GDP, low-GDP, and medium-GDP. Figure  3 summarizes these results and as shown in the figure people from high-income countries were more likely to express a negative comment and less likely to express a positive comment about automated driving [ 86 ] which is compatible with the result of the previous survey by Kyriakidis, et al. (2015) [ 79 ].

Percentage of positive and negative comments for different GDP levels (adopted from [ 86 ])

A third and recent international survey by Moody et al. (2019) with 33,958 respondents from 51 countries investigated public perceptions of AV safety across a diverse sample of individuals from a wide variety of countries. Results show that although respondents from developed countries are more aware of AV technology, they are more pessimistic about the present and future safety of AVs [ 87 ]. Figure  4 shows the level of awareness and the public acceptance of AVs in the surveyed countries.

Different ( a ) level of awareness and ( b ) public acceptance of AVs in different countries (adopted from [ 87 ])

Surveys from developing countries

In general, the studies that focus on the public acceptance of AVs in developing countries are rare. A recent survey in Pakistan that evaluates the public attitude towards AVs by Shafique et al. (2021) shows that a small proportion of the respondents are not aware of this new technology. Additionally, the main concern of the respondent was not privacy, such as data transmission, but sharing the road space with driverless trucks was the main concern with 70% of the respondents were highly concerned about the idea of sharing the road with driverless trucks. On the other hand, 15% of the respondents were highly concerned about the privacy of the vehicle such as the continuous vehicle tracking [ 88 ]. Again, these results indicate that safety is the main concern for people in developing countries and privacy is not an issue for them similar to what the previous international surveys show. Additionally, respondents were very optimistic towards AVs as when the respondents were asked about the expected benefits of AVs, 98% of the respondents believe that AVs can reduce the number of accidents and 99% believe that AVs can reduce the severity of the crashes. Thus, this high level of trust in AVs’ ability to improve traffic safety combined with the non-concerned opinions regarding the privacy or data transmission makes people from developing countries very optimistic towards AVs. The previous statement can be concluded from an earlier survey by Sanaullah, et al. (2017) in Pakistan that shows that Pakistanis are highly interested in AVs. This survey shows that 75% of the respondents are highly interested in AVs and the level of interest in AVs increase with the increase in the level of automation as 5% of the respondents are interested in level-0 automation, 8% are interested in level-1 AVs, 27% are interested in level-2 AVs, 34% are interested in level-3 AVs, and 26% are interested in level-4 AVs. Additionally, this survey shows that most Pakistanis believe that AVs are safe and will improve the level of safety as 75% of the respondents believe that AVs will improve traffic safety and reduce fatalities [ 89 ], which is consistent with Shafique et al. (2021) survey results [ 88 ] mentioned above. Additionally, Escandon-Barbosa et al. (2021) survey investigated the impact of the risk perception on the willingness to pay AVs in Vietnam and Colombia. The results show that traffic safety is the main factor that affects the willingness to pay AVs followed by the financial risk [ 90 ].

Benefits of AVs for developing countries

The fatality rates in developing counties are much higher than in developed countries that the current trends indicate that road traffic injuries will become the fifth leading cause of death by 2030, with the difference between high- and low-income countries further magnified [ 84 ]. Statistic on fatal accidents in developing countries shows that the human factors are the main factor behind most of these accidents [ 91 ], which can be eliminated by AVs. Additionally, the deployment of AVs will be associated with a reduction in the vehicle ownership, accidents, emissions, and pollution. All these advantages will help people in developing countries to have healthier lives. Thus, AVs might be more critical for developing counties than developed countries as AVs will save the lives of many people. These benefits are associated with a high level of acceptance and interest in AVs [ 6 ] in developing countries than in developing countries as shown in all previous international surveys. However, AVs face multiple issues that might make it hard to test AVs in developing countries. These issues will be discussed in the next section.

Challenges for AV deployment in developing countries

Road signs and marking.

AVs need highly visible curves, speed limits, and other signage in order to safely complete the tasks of driving, navigation, and parking [ 92 – 94 ]. However, the current marking and signing technique does not support AV navigation. For example, the two major issues facing AV navigation are the faded road marking and the non-standard road signs [ 95 , 96 ]. These two issues are commonly found in developing counties and will confuse AVs.

Road marking is essential for AV navigation and localization especially for camera-based localization techniques [ 93 ]. Previous filed studies on AVs show that missing road marking might cause unsafe navigation of AVs. However, in many developing countries there is a lack of the necessary marking as shown in the study by Huq et al. (2021) in India [ 97 ]. This study shows that many roads in India lack the stop lines, pedestrian crosswalks, lane marking, and edge marking. This lack of marking makes it challenging for AVs to operate safely on these roads.

The variation in the road signage, which is normal in the roads of developing countries, is the second challenge facing AVs, so coding is a main principle for sending information in a recognizable and standardized way. Standard colour, shape, font, line spacing, and luminance contrast are major factors that should be considered even for human drivers. However, the historical standards for coding have changed but outdated signs remain on the road network. Thus, this variety in the signage standards introduces new challenges for AV navigation. In general, there are many factors that impact the process of detection and recognition of the signs for AVs such as [ 21 , 98 ]:

Inconsistent signs: the lack of consistency in the application of signs, and sign location can be problematic and causes uncertainty on how the vehicle will react in these conditions.

Obscured signage: in many scenarios, signs might be Obscured partially or fully because of many factors such as other vehicles, vegetation, and the existing roadside infrastructure. This issue requires research in order to ensure the adequate detection of the signs in all conditions.

Varying illumination: many factors might affect the visibility of AVs such as the weather conditions, the low lighting conditions, and low sun angle. Additionally, the degraded retroreflective material will affect the visibility of AVs during night.

Lack of signage.

Developing countries suffer from all the previous signage issues. For example, the study by Huq et al. (2021) in India shows that most roads in India do not have stop signs, pedestrian crossing signs, lane merging signs, and lane split signs, and in many cases, traffic signals are not used at the intersections, which is confusing for AVs [ 97 ].

Traffic management:

AVs are expected to depend on accurate road mapping to complete their journey safely [ 99 ]. However, traffic incidents are stochastic events that change the road layout and AVs must be able to navigate safely in all cases. Additionally, roadway maintenance work is expected, which results in changing the road layout and the locations where the vehicles are expected to travel. As a result, depending on accurate mapping might not be enough as lane closures, and traffic incidences might add new risks [ 100 ]. While there are multiple websites that provide accurate information regarding traffic incidents in developed countries, such as waze.com in the UK, there is no such website in any developing country. Even more complex, these information from the websites only contains details reading the incidence cause and time without accurate information on the incidents’ on-site conditions, which makes it hard for AVs to navigate safely even in developing countries [ 101 ]. As a result, this lack of real-time information might be problematic for the safe navigation of AVs where traffic incidents occurred in developing countries.

Research studies on the parking of AVs show that AVs can significantly reduce the number of the required parking lots, especially in the context of shared AVs as vehicles will be serving customers at different times, which reduces the number of required parking spots [ 15 , 101 ]. Additionally, the autonomous valet parking system will allow vehicles to park closer to each other, and thus, the parking lots will be able to serve more vehicles. This provides new opportunities for both the users and the infrastructure provider as the user will not have to search for a space to park and increase the number of vehicles using the parking area doe parking owners. AVs have the potential to free up significant parking spaces and allow these spaces to be used in other activities, AV parking faces multiple issues in developing countries. Firstly, AV parking requires remote control of the vehicle by the parking operator which might expose the vehicle to cybersecurity threats also safeguards might be required if the vehicle does not respond. Secondly, AV parking will need an electronic payment method as no occupant will be in the vehicle. Thirdly, a major issue in the current parking lots is that most of them are privately owned and do not have a consistent marking system so the AVs will struggle in such an environment. Finally, the current parking lots do not support AV safe operations as most of the current parks are underground parks where the GPS signals are lost or weak, which causes confusion for AVs [ 102 ].

Safer harbour area

In the future, when AVs will operate on the roads, passengers will be able to participate in other activities. However, this introduces new issues and risks in case of vehicle malfunctioning or deterioration in the surrounding environment. This case might need some human interaction and ask the passengers to take control of the vehicle, but it is possible that the driver is not ready to take control of the vehicle so AVs will need a safe area to use until the driver takes control. As a result, the use of safe harbour areas is important for AVs to offer the vehicles a safe area in case the AV cannot operate safely in the current surrounding environment or in case of any malfunctioning [ 103 ]. In developed countries, the hard shoulder area is used along the road as the emergency refuge area (ERA) and can be used as a safe harbour area for AVs [ 100 , 103 ]. In developed countries, the hard should is usually used for serving the running traffic, which makes it dangerous to be used as a safe harbour area for AVs.

Traffic Heterogeneity

As shown above, AVs have the potential to increase the lane capacity in developed countries without any infrastructure improvements. However, in developing countries, the lane capacity is greatly reduced by the heterogeneity of traffic as shown in Fig.  5 -b. Non-motorized and motorized vehicles pass together through the same lane creating a haphazard situation and reducing the effective lane width. This heterogeneity of traffic will confuse AVs.

Homogenous and heterogeneous traffic conditions [ 103 ]

Summary of the challenges for the deployment of AVs

The deployment of AVs in developing countries faces many issues such as the absolute lack of standardization in road infrastructure, poorly planned road network, lack of directional, informational and warning signals, and poor mapping of roads. All the currently existing algorithms which are being used and under research in the field of AV require a structured set of road infrastructure to work properly as in developed countries where everything is well structured. However, such a well-structured infrastructure network does not exist in most developing countries. Thus, it can be concluded that while people in developing countries are more optimistic towards AVs, the infrastructure challenges represent a major obstacle for the deployment of AVs.

Impacts of the public perception on the adoption of AVs

As mentioned before, public acceptance of AVs is considered a major factor for the success of AVs. Thus, this section briefly summarizes the impact of the public attitude and cultural dimensions on the adoption of AVs.

Impact of previous experience with AV features (awareness)

In order to investigate the impact of previous experience with AV features, Piao et al. (2016) conducted a survey and telephone interview in La Rochelle, France, after the end of a pilot AV project (autonomous shuttles) in the city. The results show that around 90% of the respondents are familiar with AV technology. Additionally, more than 65% of the respondents preferred AVs than human-driven vehicles. Additionally, 73% of the respondents with previous experience with AVs were optimistic about the adoption of AVs, while 55% of the respondents without previous experience with AV technology were optimistic about this new technology. Thus, previous experience with AVs has a significant influence on the public acceptance of AVs. A second approach to investigate the impact of previous experience with AV on the public attitude towards AVs is driving simulators [ 114 ]. Thus, Wintersberger, Riener and Frison (2016) used a driving simulator to understand the impact of previous experience with AVs on the public acceptance and questionnaires were conducted twice for every participant; one before and another after the trip to analyse the participants’ attitude towards AVs. This study concluded that respondents with previous experience with AV are more optimistic about adopting this technology [ 115 ].

Impact of the economic conditions on public acceptance of AVs

In order to investigate the impact of the economic conditions on the public acceptance of AV, Bazilinskyy, Kyriakidis and De Winter (2015) conducted an international survey with 8862 respondents from 112 countries. Results of this study show that people from low-income countries are more optimistic towards AVs than people from medium- or high-income countries. The survey shows that 40, 20, and 23% of the respondent from low-, medium-, and high-income countries are optimistic about the adoption of AVs. Additionally, a large proportion of the respondent from high-income countries were concerned about the data transmission (privacy) and the software failure [ 86 ]. These results are consistent with the results of the other international surveys mentioned in Sect. (15).

Willingness to pay:

The willingness to pay is a key factor for the success of any new technology, especially in the initial state where the cost of the new technology is night hawker, previous surveys show that most respondents are not willing to pay more for AVs as summarized in Table 9 .

summary of the selected studies that investigated the willingness to pay for AVs

Precepting of AVs for different age groups:

Although most studies arbitrarily assume that AVs have the potential to increase accessibility for aged individuals and consider this segment as the early adaptor of AVs [ 14 , 36 , 44 , 118 , 119 ], results of previous surveys that investigated the impact of the respondents’ age on the public attitude towards AVs as summarized in Tables 10 . Thus, the assumption that the aged will be of the early adaptors of AVs contradicts with the results of previous surveys and younger people might be the early adopters of AVs.

Summary of the selected studies that investigated the perception of AVs for different age groups

Perception of males and females:

Previous surveys show that males are always more optimistic towards AVs than females as summarized in Table 11 .

Summary of the selected studies that investigated the perception of AVs for males and females

Summary of the impact of the demographics on the public attitude towards AVs

Figure  6 summarizes the results of previous surveys mentioned in this study. (Table 12 ) The figure shows the minimum, maximum, and average percentages of the different demographic studies in this section. The figure shows that females are more concerned about AVs than males. Additionally, younger people are more optimistic towards AVs than older people. Furthermore, most people are not willing to pay more for the new technology. Moreover, people from high-income countries are more concerned about the security and privacy of AVs than people from low-income countries. Finally, previous experience with AVs increases the level of acceptance of AVs and attracts people towards AV technology.

Summary of the of the public perception of AVs

Risks and benefits of AVs

Implications of AVs on traffic safety

Safety of AVs is essential to their success in the market and society [ 122 ]. The main advantage of AVs is their quick response when compared to the human driver [ 123 ]. Moreover, AVs are programmed so that they can obey the rules of the roads, cannot be distracted by the phone [ 124 ]. For example, Papadoulis et al. (2019) investigated the impact of different levels of penetration of AVs on the safety of traffic using Vissim simulation models and evaluated the safety of every penetration rate using the Surrogate Safety Assessment Model (SSAM). Results of this study show that AVs can significantly improve traffic safety and the level of safety increase with the increase in the level of penetration of AVs. Specifically, the results show that AVs reduce traffic conflicts by up to 47, 80, 92 and 94% with 25, 50, 75 and 100% AV penetration rates, respectively [ 125 ]. Similarly, Ye, and Yamamoto (2019) investigated the impact of AVs with different penetration rates on the safety of traffic using a simulation model. The impact of the different penetration levels of AVs on the safety was assessed based on the frequency of dangerous situations and the value of time to collision in the mixed traffic flow under different AV penetration rates. Additionally, the acceleration rate and speed difference distribution of the traffic were used to understand the evolution of the network dynamics under different AV penetration rates. The results of this study show that traffic safety can be significantly improved with the increase of AV penetration rate. Additionally, the simulation models show that the portion of smooth driving increases with the increase in AV penetration [ 126 ]. Aside from simulation models, AVs are usually programmed to make decisions using machine learning and artificial intelligence (AI) techniques. The use of AI to improve the safety and make decisions in AVs has achieved some progress; however, this progress is not significant because of the complexity of the vehicle in terms of electrical and mechanical components and the variety of external conditions such as weather, road conditions, topography and traffic pattern [ 122 , 123 ]. Additionally, an important factor that has contributed to recently reported crashes in AVs is the transition from the conventional mode to the AVs [with its various levels]. Another behavioural aspect that is indirectly related to safety is the normal eye contact–feedback–proceed two-way communication between drivers in adjacent cars in conflicting situations; a behavioural component that is either absent in full AV penetration; or more confusing in conventional vs AV mixed conditions [ 123 ]. These challenges forced Waymo CEO John Krafcik in November 2018 to state that he does not believe that AV technology will ever be able to operate in all possible conditions without some human interaction [ 127 , 128 ]. Consequently, solving the safety issue requires a multidisciplinary effort between science, technology and manufacturing. Typically, an AV contains more than 50 processors and accelerators that run millions of codes to support the vision of the vehicle in order to make the appropriate decision. Additionally, the behaviour of AVs is unpredictable in all scenarios unless it is trained for these problems in real work; however, there are hundreds of scenarios that the vehicle might face [ 122 ]. Therefore, it is fair to state that the level of development and data to support the safety of autonomy is still in the early stages [ 129 ]. The data collected between 2014 and 2017 by the research team at the University of Illinois for 114 AVs that travelled almost 1.1 million kilometres showed that the human drivers are 4000 times less likely to have an accident [ 122 ]. One of the proposed solution is the approach followed by Tesla as in 2016 when Tesla announced that their vehicles are able to travel autonomously; however, this feature will operate in the shadow mode. In the shadow mode, the vehicle can make decisions but these decisions are not executed but the human driver’s decisions are the decisions executed instead [ 130 ]. This approach helps in gathering information about the decisions of the vehicles and compares these decisions with the human river actions in order to train the vehicle to take the actions that mimic the human driver’s actions. Additionally, this approach helps the manufacturer of AVs to understand how AVs learn and improve over time.

Conclusions

It has been three decades since Mercedes-Benz and Bundeswehr University in Munich invented the first AV in the world. Over the last decade, AV technology has seen rapid improvement as both research and industry are putting significant efforts into the development of AVs. This paper reviews the current state of the art of AV implications on public behaviour, land use, economy, society and environment, and public health. While AVs hold many benefits, they also hold many risks as summarized in Table 9 and Fig.  7 which summarizes the strengths, weaknesses, opportunities, and threats (SWOT analysis) of AV technology mentioned in this study. This paper shed the light on many strengths and weaknesses of AVs and the intertwined relationships between them as follows:

AVs have the potential to reduce vehicle ownership. The pilot studies in Singapore emphasize this ownership reduction, as results show 15% reduction in the vehicle ownership due to the spread of AVs in 2018 [ 24 ].

Shared AVs have the potential to provide excellent service to the public as AVs can significantly reduce the average waiting time and trip costs, but this might encourage people to make additional trips (induced demand) and might attract transit users, which in turn increases the VKT, emissions, and energy used. One positive sign is that most of the cities testing AVs are testing autonomous shuttles or autonomous buses with the aim of solving the first mile and last mile problem and increase the reliance on public transit.

AVs allow passengers to be engaged in other activities, which means that the trip time will not be considered as an economic loss. On the other hand, this will motivate people to make longer trips and travel further which in turn increases VKT, emissions, and energy consumed.

AVs have the potential to significantly reduce the parking demand by 80 to 90% as reported in the previous studies [ 16 , 19 , 39 ], which in turn reduces congestion of vehicles searching for a parking space. Additionally, the freed-up parking spaces can be used for other purposes. For example, using the parking areas in the real estate industry can increase the value of land use. Additionally, this reduction in the parking demand might be also associated with changes in house design as the end house parking spaces might not be needed anymore and can be used for other purposes.

AVs have the potential to reduce emissions and energy due to the platooning effect, smooth start and stops, reduction in the number of engine starts because of the reduction in required fleet size. Additionally, AVs open the way towards the application of the system optimal traffic assignment.

AVs have a great influence on the economy. It is expected that the impact of AVs will extend beyond the simple crash, and fuel saving into every aspect of the economy. It is expected that AVs will revolutionize the economy in ways that have not been seen before.

AVs open new opportunities in variety of fields such as decision-making software, vehicle cybersecurity, and data opportunities. On the other hand, people are likely to be replaced by AVs, which have a potentially significant impact on individuals with lower levels of education and consequently concerns for equity. AVs can cause a serious loss for truck drivers as the technology would reduce the opportunity for the employment of millions of drivers.

AVs have the potential to increase coverage and accessibility for aged, disabled individuals, and people with limited transportation such as people in rural areas.

Implications of AVs on public health could vary depending on many factors such as the type of use or ownership, automation level, and the type of engine such as internal combustion, and hybrid engines. While AVs might increase some risks such as pollution, sedentarism, and exposure to the electric and magnetic fields, AVs might provide some benefits such as the reduction in the stress of driving, and the reduction in the noise level. Public health can benefit from the proper regulations if these regulations are implemented before the introduction of AVs in the market.

In pandemics, AVs can be used for food and medical supplies delivery and sanitize streets. Additionally, AVs can be used as a transportation mean to transport people to grocery stores, healthcare, and pharmacies, while maintaining isolation and sterilization.

The fatality rates in developing counties are much higher than in developed countries that the current trends indicate that road traffic injuries will become the fifth leading cause of death by 2030, with the difference between high- and low-income countries further magnified. The main source of accidents in developing countries is the human drivers which can be eliminated by AVs. Thus, the value of AVs in developing counties is higher than the value of AVs in developed countries as AVs will save more lives. This high value of AVs is associated with a high level of acceptance and interest in AVs in developing countries than in developing countries as shown in all previous international surveys. However, AVs face multiple issues and challenges that might make it hard to deploy or test AVs in developing countries soon. These challenges include but not limited to: the poor signage and marking standards, the lack of most basic marking and signage, the poor parking design that does not support AV navigation in terms of marking or signage consistency and the required navigation technology in case of underground parking, and traffic Heterogeneity that might confuse AVs.

Although residents of developing counties are more optimistic towards AVs than residents of developed countries, the infrastructure and behavioural challenges will force developing countries to delay the adoption of AVs.

The broader implications of AVs will be dependent on how the technology is adopted in various transportation systems. Regulatory action will be a significant determinant of how AVs could affect our lives, as well as how AVs influence the public behaviour, land use, economy, public health, environment, and society.

SWOT analysis for AV technology

Direction for future research

Although there is a large number of studies that focuses on AVs, there are multiple research gap that requires further studies and analysis as follows:

Induced demand: AVs will allow people to be engaged in other activities during their trips, which will motivate people to create additional trips and travel further. Thus, the assumption that the travel demand is unchangeable, as stated in all simulation studies, is not true as AVs will be less expensive (as a shared public mode of transportation) and thereof induced demand is inevitable; the magnitude of which is yet to be succinctly and evidently studied/estimated by transportation planners. This gap is more important to be addressed now than ever as the anticipated travel demand implications might offset some of the perceived benefits of AVs as the added trips generated besides with the VKT increase might offset the benefits of AVs and cause a high level of congestion and emissions, which means that AVs might represent a huge risk on the network and the public health. Thus, further research studies are required to understand the impact of AVs on the travelling behaviour and estimate the expected increase in the trips generated because of the attractiveness of AVs.

Public behaviour: Further studies are required to understand the change in public choices. For example, is it realistic that people might prefer to live further from work and spend their trip sleeping? If so, how far or what is the increase in the trip length and total VKT? What is the impact on the origin–destination matrices? For example, what is the impact on the distribution of people in urban and rural areas?

Elder and disabled accessibility: AVs are expected to increase the accessibility for the elders and disabled. While the disabled are considered to be one of the early adaptors of the AVs, no study surveyed this group to understand their acceptance of the AVs. As a result, future studies are required for this group instead of making assumptions. Similarly, the elders are considered of the early adaptors of the AVs as AVs will increase their accessibility. However, previous surveys show that the elder group is the most pessimistic group towards AVs [ 114 , 120 , 121 ] which contradicts the theories that the elder will benefit more from the AVs. In other words, it is noteworthy that, although accessibility to the elderly and disabled was among the key drivers/benefits of AV selling point, little to no research focused on the disabled group to understand their acceptance of AVs. Additionally, although the elders were thought to be among the early adaptors of AVs to improve their accessibility; results showed that younger people are more interested in AVs which questions the hypothesis that the elder would benefit more from AVs.

Implications of AVs on developing countries: Further studies are required to understand and quantify the implications of AVs on the land use, public behaviour, emissions, and roads and intersection capacity of developing countries. To the moment, there is no single simulation model developed in any developing country to understand how AVs will affect people of developing countries. Additionally, further research studies are required to propose solutions for the issues and challenges facing AV deployment in developing countries.

Abbreviations

Autonomous vehicle

Shared autonomous vehicle

Vehicle kilometre travelled

Value of time

Authors' contributions

KO took part in literature search and review, research methodology, data preparation, data analysis, and manuscript writing and reviewing.

Declarations

Conflict of interest.

The authors declare that they have no competing interests.

• 1. The Milwaukee Sentinel (1926) Phantom Auto' will tour city. 8 December
• 2. The Victoria Advocate (1957) Power companies build for your new electric living. 24 Mars
• 3. Davidson P, Spinoulas A (2015) Autonomous Vehicles - What Could This Mean for the Future Of Transport? AITPM 2015 National Conference. Sydney
• 4. Berrada J, Leurent F (2017) Modeling Transportation Systems involving Autonomous Vehicles: A State of the Art. Transportation Research Procedia. In: 20th EURO Working Group on Transportation Meeting, EWGT 2017, Budapest, Hungary
• 5. Greenblatt JB, Shaheen S. Automated vehicles, on-demand mobility, and environmental impacts. Curr Susta Renew Energy Rep. 2015;2:74–81. doi: 10.1007/s40518-015-0038-5. [ DOI ] [ Google Scholar ]
• 6. Othman K. Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics. 2021 doi: 10.1007/s43681-021-00041-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 7. Mallozzi P, Pelliccione P, Knauss A, Berger C, Mohammadiha N (2019) Autonomous Vehicles: State of the Art, Future Trends, and Challenges. In: Automotive Systems and Software Engineering. Springer. 10.1007/978-3-030-12157-0_16
• 8. Hartmans A (2016) How Google's self-driving car project rose from a crazy idea to a top contender in the race toward a driverless future. https://www.businessinsider.com/google-driverless-car-history-photos-2016-10
• 9. O'Kane S (2019) Uber debuts a new self-driving car with more fail-safes. https://www.theverge.com/2019/6/12/18662626/uber-volvo-self-driving-car-safety-autonomous-factory-level
• 10. Staff M (2019) Apple's vehicle project, focused on building an autonomous driving system. https://www.macrumors.com/roundup/apple-car/#release_date
• 11. Taylo T (2018) Top 8 self-driving startups to watch in 2018 and 2019. http://techgenix.com/self-driving-startups/
• 12. National Conference of State Legislatures (2020) Autonomous Vehicles | Self-Driving Vehicles Enacted Legislation. https://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx
• 13. Laukkonen J (2020) Are Self-Driving Cars Legal in Your State? https://www.lifewire.com/are-self-driving-cars-legal-4587765
• 14. Securing America’s Future Energy (SAFE) (2018) America’s Workforce and the Self-Driving Future: Realizing Productivity Gains and Spurring Economic Growth
• 15. KPMG (2015) Connected and Autonomous Vehicles – The UK Economic Opportunity
• 16. International Transport Forum. Urban Mobility System Upgrade - How shared self-driving cars could change city traffic
• 17. Burns L, Jordan W, Scarborough B (2012) Transforming Personal Mobility. The Earth Institute - Columbia University
• 18. Fagnant D, Kockelman K. The travel and environmental implication of shared autonomous vehicles using agent-based model scenarios. Transport ResPart C: Emerg Technol. 2014;40:1–13. doi: 10.1016/j.trc.2013.12.001. [ DOI ] [ Google Scholar ]
• 19. Zhang W, Guhathakurta S, Fang J, Zhang G. Exploring the impact of shared autonomous vehicles on urban parking demand: an agent-based simulation approach. Sustain Cities Soc. 2015 doi: 10.1016/j.scs.2015.07.006. [ DOI ] [ Google Scholar ]
• 20. Bischoff J, Maciejewski M. Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia Comp Sci. 2016;83:237–244. doi: 10.1016/j.procs.2016.04.121. [ DOI ] [ Google Scholar ]
• 21. Abdelgawad H, Othman K (2020) Multifaceted Synthesis of Autonomous Vehicles’ Emerging Landscape. In: Connected and Autonomous Vehicles in Smart Cities, 1 st edn. CRC-Taylor&Francis, Boca Raton, Florida, USA 10.1201/9780429329401-3
• 22. Moreno A, Michalski A, Llorca C, Moeckel R. Shared autonomous vehicles effect on vehicle-km traveled and average trip duration. J Adv Transp. 2018;2018:1–10. doi: 10.1155/2018/8969353. [ DOI ] [ Google Scholar ]
• 23. Zhang W, Guhathakurta S, Khalil E. The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT. Transport Res Part C: Emerg Technol. 2018 doi: 10.1016/j.trc.2018.03.005. [ DOI ] [ Google Scholar ]
• 24. Apur (2018) Impacts and potential benefits of autonomous vehicles
• 25. Fan Y, Guthrie A, Levinson D. Waiting time perceptions at transit stops and stations: effects of basic amenities, gender, and security. Transport Res Part A: Policy and Pract. 2016;88:251–264. doi: 10.1016/j.tra.2016.04.012. [ DOI ] [ Google Scholar ]
• 26. Wardman M. Public transport values of time. Transp Policy. 2004;11(4):363–377. doi: 10.1016/j.tranpol.2004.05.001. [ DOI ] [ Google Scholar ]
• 27. Horowitz AJ. Subjective value of time in bus transit travel. Transportation. 1981;10:149–164. doi: 10.1007/BF00165263. [ DOI ] [ Google Scholar ]
• 28. Abrantes P, Wardman M. Meta-analysis of UK values of travel time: an update. Transp Res Part A: Policy and Pract. 2011;45:1–17. doi: 10.1016/j.tra.2010.08.003. [ DOI ] [ Google Scholar ]
• 29. Metro Magazine (2014) U.S. commuters wait approximately 40 mins. a day for public transit. https://www.metro-magazine.com/accessibility/news/292870/u-s-commuters-wait-approximately-40-mins-a-day-for-public-transit
• 30. Global news wire (2014) SURVEY: U.S. Commuters Wait Approximately 40 Minutes per Day for Public Transit, Costing Them 150 Hours per Year. https://www.globenewswire.com/news-release/2014/12/09/1126354/0/en/SURVEY-U-S-Commuters-Wait-Approximately-40-Minutes-per-Day-for-Public-Transit-Costing-Them-150-Hours-per-Year.html
• 31. Lamont J (2019) Moovit’s 2019 transit report shows long commutes in Canadian cities. Mobilesyrup. https://mobilesyrup.com/2020/01/15/moovit-2019-transit-report-canadian-cities-commute-statistics/
• 32. Azevedo L, et al. Micro simulation of demand and supply of autonomous mobility on demand. Transport Res Rec J Transport Res Board. 2016 doi: 10.3141/2564-03. [ DOI ] [ Google Scholar ]
• 33. Hörl S, Erath A, Axhausen K (2016) Simulation of autonomous taxis in a multi-modal traffic scenario with dynamic demand
• 34. The Polis Traffic Efficiency and Mobility Working Group (2018) Road Vehicle Automation and Cities and Regions
• 35. Barth M, Boriboonsomsin K, Wu G (2014) Vehicle Automation and Its Potential Impacts on Energy and Emissions. 10.1007/978-3-319-05990-7_10
• 36. Miller S, Heard BR. The environmental impact of autonomous vehicles depends on adoption patterns. Environ Sci Technol. 2016;50(12):6119–6121. doi: 10.1021/acs.est.6b02490. [ DOI ] [ PubMed ] [ Google Scholar ]
• 37. Metz D. Developing policy for urban autonomous vehicles: impact on congestion. Urban Science. 2018;2(2):33. doi: 10.3390/urbansci2020033. [ DOI ] [ Google Scholar ]
• 38. Clements LM, Kockelman KM. Economic effects of automated vehicles. Transp Res Rec. 2017;2606(1):106–114. doi: 10.3141/2606-14. [ DOI ] [ Google Scholar ]
• 39. Zhang W, Guhathakurta S. Parking spaces in the age of shared autonomous vehicles: how much parking will we need and where? Transp Res Rec. 2017;2651(1):80–91. doi: 10.3141/2651-09. [ DOI ] [ Google Scholar ]
• 40. Friedrich B (2016) The Effect of Autonomous Vehicles on Traffic. Autonomous Driving: 317–334
• 41. Wagner P (2016) Traffic Control and Traffic Management in a Transportation System with Autonomous Vehicles. Autonomous Driving: 301–316
• 42. Compass Transportation and Technology (2018) The Economic and Social Value of Autonomous Vehicles: Implications from Past Network-Scale Investments. prepared for: Securing America’s Future Energy (SAFE)
• 43. Catapult transport systems (2017) Future Proofing Infrastructure for Connected and Automated Vehicles. Technical Report
• 44. Brown A, Gonder J, Repac B (2014) An Analysis of Possible Energy Impacts of Automated Vehicle. 10.1007/978-3-319-05990-7_13
• 45. United states Environmental protection agency (2020) Sources of Greenhouse Gas Emissions” https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions
• 46. Europe Environment Agency (2019) Greenhouse gas emissions from transport in Europe
• 47. Development Engineering and Infrastructure Planning (2019) Sustainable Transportation. https://www.vaughan.ca/projects/projects_and_studies/sustainable_transportation/Pages/default.aspx
• 48. Clark B Y, Larco N, Mann R F (2017) The Impacts of Autonomous Vehicles and E-Commerce on Local Government Budgeting and Finance
• 49. Gittleman M, Monaco K. Truck-driving jobs: are they headed for rapid elimination? ILR Rev. 2020;73(1):3–24. doi: 10.1177/0019793919858079. [ DOI ] [ Google Scholar ]
• 50. Dons E, et al. Physical activity through sustainable transport approaches (PASTA): protocol for a multi-centre, longitudinal study. BMC Public Health. 2015;15(1):1126. doi: 10.1186/s12889-015-2453-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 51. Rojas-Rueda D, et al. The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study. BMJ. 2011;343:d4521. doi: 10.1136/bmj.d4521. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 52. Rojas-Rueda D, et al. Replacing car trips by increasing bike and public transport in the greater Barcelona metropolitan area: a health impact assessment study. Environ Int. 2012;49:100–109. doi: 10.1016/j.envint.2012.08.009. [ DOI ] [ PubMed ] [ Google Scholar ]
• 53. Soteropoulos A, Berger M, Ciari F. Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies. Transp Rev. 2019;39:29–49. doi: 10.1080/01441647.2018.1523253. [ DOI ] [ Google Scholar ]
• 54. Rojas-Rueda D, et al. Health impact assessment of increasing public transport and cycling use in Barcelona: a morbidity and burden of disease approach. Appendix Prev Med. 2013;57(5):573–579. doi: 10.1016/j.ypmed.2013.07.021. [ DOI ] [ PubMed ] [ Google Scholar ]
• 55. Health Effects Inst. (2018) State of Global Air/2018. A special report on global exposure to air pollution and its disease burden. Rep., Health Effects Inst., Boston. https://www.stateofglobalair.org/sites/default/files/soga-2018-report.pdf
• 56. Amato F, et al. Urban air quality: the challenge of traffic non-exhaust emissions. J Hazard Mater. 2014;275:31–36. doi: 10.1016/j.jhazmat.2014.04.053. [ DOI ] [ PubMed ] [ Google Scholar ]
• 57. Rojas-Rueda D, Turner MC. Commentary: diesel, cars, and public health. Epidemiology. 2016;27(2):159–162. doi: 10.1097/EDE.0000000000000427. [ DOI ] [ PubMed ] [ Google Scholar ]
• 58. Anenberg S et al. (2019) A global snapshot of the air pollution-related health impacts of transportation sector emissions in 2010 and 2015. Rep., Int. Counc. Clean Transp. (ICCT), Washington, DC. https://theicct.org/sites/default/files/publications/Global_health_impacts_transport_emissions_2010-2015_20190226.pdf
• 59. Timmers V, Achten P. Non-exhaust PM emissions from electric vehicles. Atmos Environ. 2016;134:10–17. doi: 10.1016/j.atmosenv.2016.03.017. [ DOI ] [ Google Scholar ]
• 60. Rojas-Rueda D, Nieuwenhuijsen MJ, Khreis H, Frumkin H. Autonomous Vehicles and Public Health. Annu Rev Public Health. 2020;41(1):329–345. doi: 10.1146/annurev-publhealth-040119-094035. [ DOI ] [ PubMed ] [ Google Scholar ]
• 61. Basner M, McGuire S. WHO environmental noise guidelines for the European Region: a systematic review on environmental noise and effects on sleep. Int J Environ Res Public Health. 2018;15(3):E519. doi: 10.3390/ijerph15030519. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 62. Brown AL, Van Kamp I. WHO environmental noise guidelines for the European Region: a systematic review of transport noise interventions and their impacts on health. Int J Environ Res Public Health. 2017;14:E873. doi: 10.3390/ijerph14080873. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 63. Brown AL. Effects of road traffic noise on health: from burden of disease to effectiveness of interventions. Procedia Environ Sci. 2015;30:3–9. doi: 10.1016/j.proenv.2015.10.001. [ DOI ] [ Google Scholar ]
• 64. WHO (World Health Organ.) Eur.Cent.Environ.Health (2011) Burden of disease from environmental noise: quantification of healthy life years lost in Europe. Rep.,WHO Reg. Off. Eur., Bonn, Ger. http://www.euro . who.int/__data/assets/pdf_file/0008/136466/e94888.pdf
• 65. Verheijen E, Jabben J (2010) Effect of electric cars on traffic noise and safety.RIVM Lett. Rep. 680300009/2010, Natl. Inst. Public Health Environ. (RIVM), Bilthoven, Neth.
• 66. Natl. Cancer Inst. (2019) Electromagnetic fields and cancer. Fact Sheet, Natl. Cancer Inst., Bethesda, MD. https://www.cancer.gov/about-cancer/causes-prevention/risk/radiation/electromagnetic-fieldsfact - sheet
• 67. Natl. Toxicol. Progr. (2018) The toxicology and carcinogenesis studies in Hsd:Sprague Dawley SD rats exposed to whole-body radio frequency radiation at a frequency (900 mhz) and modulations (GSM and CDMA) used by cell phones. NTP Tech. Rep. 595, Natl. Toxicol. Progr. (NTP), Research Triangle Park, NC. https://ntp.niehs.nih.gov/ntp/htdocs/lt_rpts/tr595_508.pdf?utm_source=direct&utm_ medium=prod&utm_campaign=ntpgolinks&utm_term=tr595 [ DOI ] [ PMC free article ] [ PubMed ]
• 68. NHTSA (Natl. Highw. Traffic Saf. Adm.) (2019) Drug-impaired driving. NHTSA. https://www.nhtsa.gov/risky-driving/drug-impaired-driving
• 69. Off. Surg. Gen (2016) Facing addiction in America: the Surgeon General’s report on alcohol, drugs, and health. Rep., US Dep. Health Hum. Serv., Washington, DC. https://addiction.surgeongeneral.gov/sites/default/files/surgeon-generals-report.pdf [ PubMed ]
• 70. Natl. Transp. Comm. (2018) Changing driving laws to support automated vehicles. Policy Pap., Natl. Transp. Comm., Melbourne. https://www.ntc.gov.au/sites/default/files/assets/files/NTC%20Policy%20Paper%20-%20Changing%20driving%20laws%20to%20support%20automated%20vehicles.pdf
• 71. Antoun M, Edwards KM, Sweeting J, Ding D. The acute physiological stress response to driving: a systematic review. PLoS One. 2017;12(10):e0185517. doi: 10.1371/journal.pone.0185517. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 72. Mariotti A. The effects of chronic stress on health new insights into the molecular mechanisms of brain body communication. Futur Sci OA. 2015;1(3):FS023. doi: 10.4155/fso.15.21. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 73. Crowe S, Argo A I, Aurora C, Pony A (2020) Uber halt self-driving tests due to COVID-19. The robot report.. https://www.therobotreport.com/argo-ai-aurora-cruise-pony-ai-uber-autonomous-vehicles-covid/
• 74. Tchir J (2020) Could physical distancing reignite our excitement for autonomous driving? The globe and mail. https://www.theglobeandmail.com/drive/mobility/article-could-social-distancing-reignite-our-excitement-for-autonomous-driving/
• 75. Grosbard E (2020) Autonomous vehicles could be crucial in responding to future pandemics. The robot report. https://www.thero botre port.com/auton omous -vehicles-vital -role-solvi ng-future-pande mics/
• 76. Demaitre E (2020) COVID-19 pandemic prompts more robot usage worldwide. The robot report. https :// www.thero botre port.com/covid -19-pande mic-promp ts-more-robot -usage -world wide/
• 77. Ford T (2020) Autonomous shuttles help transport COVID-19 tests at Mayo Clinic in Florida. Mayo Clinic. https://newsnetwork.mayoclinic.org/discussion/autonomous-shuttles-help-transport-covid-19-tests-at-mayo-clinic-in-jacksonville/
• 78. Goscé L, Johansson A. Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground. Environ Health. 2018;17:84. doi: 10.1186/s12940-018-0427-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 79. Kyriakidis M, Happee R, de Winter JCF. Public opinion on automated driving: results of an international questionnaire among 5000 respondents, transportation research part f: traffic psychology and behaviour, volume 32, 2015. ISSN. 2015;127–140:1369–8478. doi: 10.1016/j.trf.2015.04.014. [ DOI ] [ Google Scholar ]
• 80. Hong J. Considering privacy issues in the context of Google Glass. Commun ACM. 2013;56:10–11. doi: 10.1145/2524713.2524717. [ DOI ] [ Google Scholar ]
• 81. Segall JE. Google street view: walking the line of privacy-intrusion upon seclusion and publicity given to private facts in the digital age. Pittsburgh J Technol Law & Policy. 2010 doi: 10.5195/tlp.2010.51. [ DOI ] [ Google Scholar ]
• 82. Debatin B, Lovejoy JP, Horn AK, Hughes BN. Facebook and online privacy: attitudes, behaviors, and unintended consequences. J Comput-Mediat Commun. 2009;15:83–108. doi: 10.1111/j.1083-6101.2009.01494.x. [ DOI ] [ Google Scholar ]
• 83. Weinstein M (2013) Facebook Privacy Issues Is Privacy Dead. From http://www.huffingtonpost.com/tag/facebookprivacy-issues/
• 84. World Health Organization (2013) Global status report on road safety 2013: supporting a decade of action. Luxembourg
• 85. Maslow AH. A theory of human motivation. Psychol Rev. 1943;50:370–396. doi: 10.1037/h0054346. [ DOI ] [ Google Scholar ]
• 86. Bazilinskyy P, Kyriakidis M, de Winter J. An international crowdsourcing study into peoples statements on fully automated driving. Procedia Manufact. 2015 doi: 10.1016/j.promfg.2015.07.540. [ DOI ] [ Google Scholar ]
• 87. Moody J, Bailey N, Zhao J. Public perceptions of autonomous vehicle safety: an international comparison. Saf Sci. 2019 doi: 10.1016/j.ssci.2019.07.022. [ DOI ] [ Google Scholar ]
• 88. Shafique M A, Afzal M S, Ahmed A (2021) Public Perception regarding Autonomous Vehicles in Developing Countries: A Case study of Pakistan. Pakistan Journal of Engineering and Applied Sciences, University of Engineering and Technology Lahore. Vol. 28 January, 2021 (p. 1–6)
• 89. Sanaullah I, Hussain A, Chaudhry A, Case K, Enoch M (2017) Autonomous Vehicles in Developing Countries: A Case Study on User’s View Point in Pakistan. In: Stanton N., Landry S., Di Bucchianico G., Vallicelli A. (eds) Advances in Human Aspects of Transportation. Advances in Intelligent Systems and Computing, vol 484. Springer, Cham. 10.1007/978-3-319-41682-3_47
• 90. Escandon Barbosa D, et al. Adoption of new technologies in developing countries: the case of autonomous car between Vietnam and Colombia. Technology in Society. 2021;66:101674. doi: 10.1016/j.techsoc.2021.101674. [ DOI ] [ Google Scholar ]
• 91. Thomas A, Trost J. A study on implementing autonomous intra city public transport system in developing countries - India. Procedia Comp Sci. 2017;375–382:1877–509. doi: 10.1016/j.procs.2017.09.093. [ DOI ] [ Google Scholar ]
• 92. Johnson C (2017) Readiness of the road network for connected and autonomous vehicle. London: RAC Foundation
• 93. Kuutti S, Fallah S, Katsaros K, Dianati M, Mccullough F, Mouzakitis A. A survey of the state of the art localisation techniques and their potentials for autonomous vehicle applications. IEEE Int Things J. 2018 doi: 10.1109/JIOT.2018.2812300. [ DOI ] [ Google Scholar ]
• 94. Huggins R et al. (2017) Assessment of Key Road Operator Actions to Support Automated Vehicles. Austroads. Research Report AP-R543–17
• 95. Sage A (2016) Where’s the Lane? Self-driving Cars Confused by shabby US roadways. Available: https://www.reuters.com/article/us-autos-autonomous-infrastructure-insig/wheres-the-lane-s e lf-drivingcars-confused-by-shabby-u-s-roadways-idUSKCN0WX131
• 96. EuroRAP (2013) Roads that Cars Can Read: A quality standard for road markings and traffic signs on major rural roads. Available : www.eurorap.org/wp-content/uploads/2015/03/roads_that_ cars_can_read_2_spread1.pdf
• 97. Huq A, Kamol Debnath D, Nadia M, Nazmus S (2021) The evidence of critical issues in transportation infrastructures of Bangladesh to introduce connected and autonomous. Vehicles. 10.11159/iccste21.164
• 98. Infrastructure Partnerships Australia (2017) Automated vehicles: do we know which road to take. Internetinė prieiga
• 99. Schoettle B, Sivak M (2015) Motorists' preferences for different levels of vehicle automation. University of Michigan, Ann Arbor, Transportation Research Institute
• 100. Liu Y, Tight M, Sun Q, Kang R. A systematic review: road infrastructure requirement for connected and autonomous vehicles (CAVs) J Phys: Conf Ser. 2019;1187:042073. doi: 10.1088/1742-6596/1187/4/042073. [ DOI ] [ Google Scholar ]
• 101. Lyon B, Hudson N, Twycross M, Finn D, Porter S, Maklary Z, Waller T (2017) Automated vehicles: Do we know which road to take? Infrastructure Partnerships Australia
• 102. UK Autodrive (2018) Paving the Way: Building the Road Infrastructure of the Future for the Connected and Autonomous Vehicles. Available: http://www.ukautodrive.com/downloads/
• 103. SAE (2018) Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. J3016_201806
• 104. Kalaiselvi R, Ramachandraiah A. Honking noise corrections for traffic noise prediction models in heterogeneous traffic conditions like India. Appl Acoust. 2016;25–38:0003–682X. doi: 10.1016/j.apacoust.2016.04.003. [ DOI ] [ Google Scholar ]
• 105. Narayanan S, Chaniotakis M, Antoniou C. Shared autonomous vehicle services: a comprehensive review. Transport Res Part C Emerg Technol. 2020;111:255–293. doi: 10.1016/j.trc.2019.12.008. [ DOI ] [ Google Scholar ]
• 106. Kopelias P, Demiridi E, Vogiatzis K, Skabardonis A, Zafiropoulou V. Connected & autonomous vehicles – environmental impacts – a review. Sci Total Environ. 2019;712:135237. doi: 10.1016/j.scitotenv.2019.135237. [ DOI ] [ PubMed ] [ Google Scholar ]
• 107. Spence J, Kim Y, Lamboglia C, Lindeman C, Mangan A, McCurdy A, Stearns J, Wohlers B, Sivak A, Clark M. Potential impact of autonomous vehicles on movement behavior: a scoping review. Am J Prev Med. 2020 doi: 10.1016/j.amepre.2020.01.010. [ DOI ] [ PubMed ] [ Google Scholar ]
• 108. Sohrabi S, Khreis H, Lord D. Impacts of autonomous vehicles on public health: a conceptual model and policy recommendations. Sustain Cities Soc. 2020;63:102457. doi: 10.1016/j.scs.2020.102457. [ DOI ] [ Google Scholar ]
• 109. Hao M, Yamamoto T. Shared autonomous vehicles: a review considering car sharing and autonomous vehicles. Asian Transport Stud. 2018;5(1):47–63. [ Google Scholar ]
• 110. Gandia RM, Antonialli F, Cavazza BH, Neto AM, Lima DA, Sugano JY, Nicolai I, Zambalde AL. Autonomous vehicles: scientometric and bibliometric review. Transp Rev. 2019;39(1):9–28. doi: 10.1080/01441647.2018.1518937. [ DOI ] [ Google Scholar ]
• 111. Peng J, Hanbin H, Zhan F, Chen Y, Shi Y. Agent-based simulation of autonomous vehicles a systematic literature review. IEEE Access. 2020 doi: 10.1109/ACCESS.2020.2990295. [ DOI ] [ Google Scholar ]
• 112. Faisal AM, Yigitcanlar T, Kamruzzaman M, Currie G. Understanding autonomous vehicles: a systematic literature review on capability, impact, planning and policy. J Transport Land Use. 2019 doi: 10.5198/jtlu.2019.1405. [ DOI ] [ Google Scholar ]
• 113. Sun Y, Olaru D, Smith B, Greaves S, Collins A. Road to autonomous vehicles in Australia: an exploratory literature review. Road and Transport Res. 2017;26:34–47. [ Google Scholar ]
• 114. Piao J, McDonald M, Hounsell N, Graindorge M, Graindorge T, Malhene N. Public views towards implementation of automated vehicles in urban areas. Transport Res Procedia. 2016;14:2168–2177. doi: 10.1016/j.trpro.2016.05.232. [ DOI ] [ Google Scholar ]
• 115. Wintersberger P. et al. (2016). Automated Driving System, Male, or Female Driver. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - Automotive'UI. 16 51–58
• 116. Schoettle, B; Sivak, M. A (2014) Survey of Public Opinion about Autonomous and Self-Driving Vehicles in the U.S., the U.K., and Australia
• 117. Cunningham, M. et al. (2018) A survey of public opinion on automated vehicles in Australia and New Zealand. In: 28th ARRB International Conference, Brisbane, Queensland, Australia
• 118. Compass Transportation and Technology prepared for: Securing America’s Future Energy (SAFE) (2018) The Economic and Social Value of Autonomous Vehicles: Implications from Past Network-Scale Investments
• 119. Clark BY; Larco N, Mann RF (2017) The Impacts of Autonomous Vehicles and E-Commerce on Local Government Budgeting and Finance. 2017
• 120. Abraham H et al. (2017) Autonomous Vehicles and Alternatives to Driving: Trust, Preferences, and Effects of Age. Transportation Research Board 96th Annual Meeting
• 121. Richardson E Davies P (2018) The Changing Public's Perception of Self-Driving Cars
• 122. University of Illinois College of Engineering (2019) "Platform for testing of autonomous vehicle safety." ScienceDaily. ScienceDaily, 25 October 2019. www.sciencedaily.com/releases/2019/10/191025170813.htm
• 123. Sivak, and Schoettle, Road safety with self-driving vehicles: general limitations and road sharing with conventional vehicles (University of Michigan, Ann Arbor, Transportation Research Institute, 2015–01)
• 124. Walker (2020) “Are self-driving cars safe for our cities?” https://www.curbed.com/2016/9/21/12991696/driverless-cars-safety-pros-cons
• 125. Papadoulis A, et al. Evaluating the safety impact of connected and autonomous vehicles on motorways. Accid Analy & Prevent. 2019;124:12–22. doi: 10.1016/j.aap.2018.12.019. [ DOI ] [ PubMed ] [ Google Scholar ]
• 126. Ye L, Yamamoto T (2019) Evaluating the impact of connected and autonomous vehicles on traffic safety, Physica A: Statistical Mechanics and its Applications. 526 121009. 10.1016/j.physa.2019.04.245
• 127. Stewart (2019) “Self-driving cars have to be safer than regular cars. The question is how much” vox, record https://www.vox.com/recode/2019/5/17/18564501/self-driving-car-morals-safety-tesla-waymo
• 128. Tibken (2018) “Waymo CEO: Autonomous cars won't ever be able to drive in all conditions” cnet https://www.cnet.com/news/alphabet-google-waymo-ceo-john-krafcik-autonomous-cars-wont-ever-be-able-to-drive-in-all-conditions/
• 129. Lori (2019) “Are Self-Driving Cars Safe?” Verizon connect https://www.verizonconnect.com/resources/article/are-self-driving-cars-safe/
• 130. Nishimoto, A. (2016). All New Tesla Models Will Feature Level 5-Capable Autopilot Hardware. Automobile news. https://www.motortrend.com/news/new-tesla-models-will-feature-level-5-capable-autopilot-hardware/
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Waymo Research

Advancing the state of the art in autonomous driving

Check out our latest publications, and explore the Waymo Open Dataset , which we released to support cutting-edge autonomous driving research.

Research Papers

87  results found

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• Maya Kabkab,
• Yurong You,

Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting

• Bertrand Douillard,

Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking

• Alper Ayvaci,
• Dillon Cower,
• Congcong Li,

StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving

• Jinkyu Kim,
• Mayank Bansal,

Multi-Class 3D Object Detection with Single-Class Supervision

• Maoqing Yao,

KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction

• Qiujing Lu,
• Weiqiao Han,
• Jeffrey Ling,
• Minfa Wang,
• Haoyu Chen,
• Balakrishnan Varadarajan,
• Paul Covington

Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation

• Daewoo Kim,

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

• Xiaojie Shi,

Revisiting Multi-Scale Feature Fusion for Semantic Segmentation

• Tianjian Meng,
• Golnaz Ghiasi,
• Reza Mahjorian,
• Quoc V. Le,

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

• Yuning Chai,

GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting

Multi-modal 3d human pose estimation with 2d weak supervision in autonomous driving.

• Jingxiao Zheng,
• Visesh Chari,

Revisiting 3D Object Detection From an Egocentric Perspective

• Thomas Funkhouser,

MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction

• Ahmed Hefny,
• Chi Pang Lam,

4D-Net for Learned Multi-Modal Alignment

• AJ Piergiovanni,
• Michael S. Ryoo,
• Anelia Angelova

SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation

• Qiangeng Xu,

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps

• Charlie Nash,
• Xiaohan Jin,
• Jiyang Gao,
• Cordelia Schmid,
• Nir Shavit,

To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels

• Jiquan Ngiam,
• Vijay Vasudevan,

RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection

• Gamaleldin Elsayed,
• Alex Bewley,
• Cristian Sminchisescu,

Scene Transformer: A unified architecture for predicting multiple agent trajectories

• Zhengdong Zhang,
• Hao-Tien Lewis Chiang,
• Ashish Venugopal,
• David Weiss,
• Zhifeng Chen,
• Jonathon Shlens

SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping

• Austin Stone,
• Daniel Maurer,
• Anelia Angelova,
• Rico Jonschkowski

Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset

• Charles Qi,
• Aurelien Chouard,
• Alexander McCauley,

Identifying Driver Interactions via Conditional Behavior Prediction

• Ekaterina Tolstaya,

3D-MAN: 3D Multi-frame Attention Network for Object Detection

• Zetong Yang,
• Jiquan Ngiam

Offboard 3D Object Detection from Point Cloud Sequences

Pseudo-labeling for scalable 3d object detection, scalable scene flow from point clouds in the real world.

• Philipp Jund,
• Nichola Abdo,

Unsupervised Monocular Depth Learning in Dynamic Scenes

• Ariel Gordon,

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout

• Yanping Huang,
• Thang Luong,

TNT: Target-driveN Trajectory Prediction

Soda: multi-object tracking with soft data association.

• Tsung-Yi Lin,
• Ming-Hsuan Yang,

Can weight sharing outperform random architecture search? An investigation with TuNAS

• Gabriel Bender,
• Hanxiao Liu,
• Pieter-Jan Kindermans,

Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection

• Thomas Mensink,

Taskology: Utilizing Task Relations at Scale

• Sören Pirk,
• Jan Dlabal,
• Anthony Brohan,
• Ankita Pasad,
• Ariel Gordon

Attentional Bottleneck: Towards an Interpretable Deep Driving Network

• Mayank Bansal

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

• Cordelia Schmid

STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction

Surfelgan: synthesizing realistic sensor data for autonomous driving.

• Dumitru Erhan,

Streaming Object Detection for 3-D Point Clouds

• Brandon Yang,
• Christoph Sprunk,
• Ouais Alsharif,
• Zhifeng Chen

Improving 3D Object Detection through Progressive Population Based Augmentation

• Barret Zoph,
• Chunyan Bai,

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

• Xerxes Dotiwalla,
• Vijaysai Patnaik,
• Aleksei Timofeev,
• Maxim Krivokon,
• Aditya Joshi,
• Sheng Zhao,

End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds

• Tom Ouyang,
• Vijay Vasudevan

MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Starnet: targeted computation for object detection in point clouds.

• Patrick Nguyen,

ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

• Alex Krizhevsky,
• Abhijit Ogale

Autonomous Vehicles Factsheet

Autonomous vehicles (AVs) use technology to partially or entirely replace the human driver in navigating a vehicle while avoiding road hazards and responding to traffic conditions. 1 The Society of Automotive Engineers (SAE) has developed, and the U.S. National Highway Traffic Safety Administration (NHTSA) uses, a classification system with six levels based on the level of human intervention. 2

SAE Levels of Automation 2,3

Development of autonomous vehicles.

AV research started in the 1980s when universities began working on two types of AVs: one that required roadway infrastructure and one that did not. 1 The U.S. Defense Advanced Research Projects Agency (DARPA) has held “grand challenges” testing the performance of AVs on a 150-mi off-road course. 1 No vehicles successfully finished the 2004 Grand Challenge, but five completed the course in 2005. 1 In 2007, six teams finished the third challenge, which consisted of a 60-mi course navigating an urban environment obeying normal traffic laws. 1 In 2015, the University of Michigan built Mcity, the first facility built for testing AVs. Research is conducted there on the safety, efficiency, accessibility, and commercial viability of AVs. 4   Unmanned aircraft systems (UAS), or drones, are being deployed for commercial ventures such as last-mile package delivery, medical supply transportation, and inspection of critical infrastructure. 5

Autonomous Vehicle Technologies

AVs use combinations of technologies and sensors to sense the roadway, other vehicles, and objects on and along the roadway. 6

Autonomous Vehicle Technologies 1,7,8,9

Current and Projected Market

Key market leaders.

• In 2021, North America was preceived to be leading the AV race ahead of China. In 2023, this perception was evenly split, according to a McKinsey survey. 10
• Waymo has tested its vehicles by driving over 20M miles on roads and tens of billions of miles in simulation. 11 Teslas have driven over 3B miles in Autopilot mode since 2014. 12
• Other major players include Audi, BMW, Daimler, GM, Nissan, Volvo, Bosch, Continental, Mobileye, Valeo, Velodyne, Nvidia, Ford, as well as many other OEMs and technology companies. 6,13

Regulations, Liability, and Projected Timeline

• Regulation will impact the adoption of AVs. 14 In the U.S. there are no national standards or guidelines for AVs, allowing states to determine their own. 14 In 2018, Congress worked to pass the AV Start Act that would have implemented a framework for the testing, regulating, and deploying of AVs. The legislation failed to pass both houses. 15 As of February 2020, 29 states and D.C. have enacted legislation regarding the definition of AVs, their use, and liability. 16
• Product liability laws need to assign liability properly when AV crashes occur, as highlighted by the May 2016 Tesla Model S fatality. Liability will depend on multiple factors, especially whether the vehicle was being operated appropriately to its level of automation. 17,18
• Although researchers, OEMs, and industry experts have different projected timelines for AV market penetration and full adoption, the majority predict Level 5 AVs around 2030. 19,20

Current Limitations and Barriers

• There are several limitations and barriers that could impede adoption of AVs, including lack of buyer demand, data security, protection against cyberattacks, regulations compatible with driverless operation, resolved liability laws, societal attitude and behavior change regarding distrust and subsequent resistance to AV use, and the development of economically viable AV technologies. 6
• Weather can adversely affect sensor performance on AVs, potentially impeding adoption. Ford recognized this barrier and started conducting AV testing in the snow in 2016 at Mcity, utilizing technologies suited for poor weather. 13

Impacts and Solutions

• Although AVs alone are unlikely to have significant direct impacts on energy consumption and GHG emissions, if effectively paired with other technologies and new transportation models, significant indirect and synergistic effects on economics, the environment, and society are possible. 21,22
• One study found that when eco-driving, platooning, intersection connectivity and faster highway speeds are considered as direct effects of AVs, energy use and GHG emissions can be reduced by 9%. 23

Metrics and Associated Impacts

• Congestion is predicted to decrease, reducing fuel consumption by 0-4%. However, decreased congestion is likely to lead to increased vehicle-miles traveled (VMT), partially offsetting the fuel consumption benefit. 21
• Eco-Driving , a set of practices that reduce fuel consumption, is predicted to reduce energy consumption by up to 20%. 21 However, if AV algorithms do not prioritize efficiency, fuel efficiency may actually decrease. 24
• Performance , such as fast acceleration, is likely to become de-emphasized when comfort and productivity become travel priorities, potentially leading to a 5-23% reduction in fuel consumption. 21
• Improved Crash Avoidance , due to the increased safety features of AVs, may allow for the reduction of vehicle weight and size, decreasing fuel consumption 5-23%. 21
• Vehicle Right-Sizing , the ability to match the utility of a vehicle to a given need, has the potential to decrease energy consumption 21-45%, though the full benefits are only likely when paired with a ride-sharing on-demand model. 21
• Higher Highway Speeds are likely due to improved safety, increasing fuel consumption 7-30%. 21,25
• Travel Cost Reduction , due to decreased insurance cost and improvements in productivity and driving comfort, could result in increased travel, potentially increasing energy consumption 4% to 60%. 21
• New User Groups are likely to increase VMT and fuel consumption by 2-10%. 21  
• Changed Mobility Services , such as an increase in ride-sharing could reduce energy consumption 0-20%. 21,26

Although an accurate assessment of these interconnected impacts cannot currently be made, one study evaluated the potential impacts of four scenarios, each with unknown likelihoods. The most optimistic scenario projected a 40% decrease in road transport energy and the most pessimistic scenario projected a 105% increase in road transport energy. 21

Projected Fuel Consumption Impact Ranges 21,25

Potential benefits and costs.

• 42,795 people died in vehicle crashes in 2022. 27 94% of crashes are due to human error. AVs have the potential to remove/reduce human error and decrease deaths. 28
• AVs have the potential to reduce crashes by 90%, potentially saving approximately $190B per year. 29
• The U.S. AV market is expected to grow to over $75B in 2030, representing an increase of 350% from 2023. 30  
• The last-mile AV energy savings for public transportation were over 33% when compared to private vehicles. 31
• Potential benefits include: improvements in safety and public health, increased productivity, quality of life, mobility, accessibility, and travel, especially for the disabled and elderly; reduction of energy use, environmental impacts, congestion, and public and private costs associated with transportation; and increased adoption of car sharing. 1,14,32,33
• Potential costs include increased congestion, VMT, urban sprawl, total time spent traveling, and upfront costs of private car ownership leading to social equity issues; impacts on other modes of transportation; and increased concern with security, safety, and public health. 1,14,25,33,34

Center for Sustainable Systems, University of Michigan. 2024. "Autonomous Vehicles Factsheet." Pub. No. CSS16-18.

• Anderson, J., et al. (2016) Autonomous Vehicle Technology: A Guide for Policymakers. Rand Corporation, Santa Monica, CA.

http://www.rand.org/content/dam/rand/pubs/research_reports/RR400/RR443-2/RAND_RR443-2.pdf

• Society of Automotive Engineers (2021) Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles.

https://saemobilus.sae.org/content/J3016_202104/

• National Highway Traffic Safety Administration (NHTSA) (2018) Automated Vehicles 3.0 Preparing for the Future of Transportation.

https://www.transportation.gov/sites/dot.gov/files/docs/policy-initiatives/automated-vehicles/320711/preparing-future-transportation-automated-vehicle-30.pdf

• University of Michigan (2019) MCity Test Facility. 

https://mcity.umich.edu/our-work/mcity-test-facility/#

• Federal Aviation Administration (2020) Fact Sheet – The UAS Integration Pilot Program.

https://www.faa.gov/news/fact_sheets/news_story.cfm?newsId=23574

• Mosquet, X., et al. (2015) Revolution in the Driver's Seat: The Road to Autonomous Vehicles.

https://www.bcg.com/publications/2015/automotive-consumer-insight-revolution-drivers-seat-road-autonomous-vehicles.aspx

• Adapted from The Economist (2013) How does a self-driving car work?

http://media.economist.com/sites/default/files/imagecache/full-width/images/print-edition/20120901_TQC976_0.png

• Pedro, F. and U. Nunes (2012) Platooning with dsrc-based ivc-enabled autonomous vehicles - Adding infrared communications for ivc reliability improvement. Intelligent Vehicles Symposium (IV), IEEE. 

https://ieeexplore.ieee.org/document/6232206

• Bergenhem, C., et al. (2012) Overview of Platooning Systems. Proceedings of the 19th ITS World Congress, Oct 22-26, Vienna, Austria.

http://publications.lib.chalmers.se/records/fulltext/174621/local_174621.pdf

• McKinsey & Company (2024) Autonomous vehicles moving forward: Perspectives from industry leaders.

https://www.mckinsey.com/features/mckinsey-center-for-future-mobility/our-insights/autonomous-vehicles-moving-forward-perspectives-from-industry-leaders

• CNET (2020) Waymo Driverless Cars Have Driven 20 Million Miles On Public Roads.

https://www.cnet.com/news/waymo-driverless-cars-have-driven-20-million-miles-on-public-roads/

• Electrek (2020) Tesla Drops A Bunch Of New Autopilot Data, 3 Billion Miles And More.

https://electrek.co/2020/04/22/tesla-autopilot-data-3-billion-miles/

• Ford (2016) "Ford Conducts Industry-First Snow Tests of Autonomous Vehicles--Further Accelerating Development Program."

https://media.ford.com/content/fordmedia/fna/us/en/news/2016/01/11/ford-conducts-industry-first-snow-tests-of-autonomous-vehicles.html#:~:text=11%2C%202016%20%E2%80%93%20Ford%20is%20conducting,to%20millions%20of%20customers%20worldwide .

• Fagnant, D., and K. Kockelman (2015) Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181.

http://www.sciencedirect.com/science/article/pii/S0965856415000804

• The National Law Review (2019) Autonomous Vehicle Federal Regulation

https://www.natlawreview.com/article/autonomous-vehicle-federal-regulation

• National Conference of State Legislatures (2020) Autonomous Vehicles.

http://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx

• Gurney, J. (2013) Sue my car not me: Products liability and accidents involving autonomous vehicles." Journal of Law, Technology & Policy, 2(2013): 247-277.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2352108

• Tesla (2016) A Tragic Loss. Blog.

https://www.teslamotors.com/blog/tragic-loss

• PWC (2015) Connected Car Study 2015: Racing ahead with autonomous cars and digital innovation.

https://www.pwc.at/de/publikationen/connected-car-study-2015.pdf

• Underwood, S. (2014) Automated, Connected, and Electric Vehicle Systems: Expert Forecast and Roadmap for Sustainable Transportation.

http://graham.umich.edu/media/files/LC-IA-ACE-Roadmap-Expert-Forecast-Underwood.pdf

• Wadud, Z. et al. (2016) "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles." Transportation Research Part A 86: 1-18.

http://www.sciencedirect.com/science/article/pii/S0965856415002694

• Keoleian, G., et al. (2016) Road Map of Autonomous Vehicle Service Deployment Priorities in Ann Arbor. CSS16-21.

http://css.umich.edu/sites/default/files/publication/CSS16-21.pdf

• Gawron, J., et al. (2018) “Life Cycle Assessment of Connected and Automated Vehicles: Sensing and Computing Subsystem and Vehicle Level Effects.” Environmental Science & Technology 52(5):3249–3256.

http://css.umich.edu/publication/life-cycle-assessment-connected-and-automated-vehicles-sensing-and-computing-subsystem

• Mersky, A. and C. Samaras (2016) "Fuel economy testing of autonomous vehicles." Transportation Research Part C 65: 31-48.

http://www.sciencedirect.com/science/article/pii/S0968090X16000024

• Brown, A., et al. (2014) "An analysis of possible energy impacts of automated vehicle." Road Vehicle Automation. Springer International Publishing: 137-153.

http://link.springer.com/chapter/10.1007%2F978-3-319-05990-7_13

• Burns, L., et al. (2013) Transforming Personal Mobility. The Earth Institute Columbia University.

http://wordpress.ei.columbia.edu/mobility/files/2012/12/Transforming-Personal-Mobility-Aug-10-2012.pdf

• NHTSA (2023) Traffic Safety Facts.

https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813448#:~:text=NHTSA%20has%20released%20the%202022,%2C%20DOT%20HS%20813%20428 ).

• NHTSA (2018) Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. 

https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812506

• Bertoncello, M. and D. Wee (2015) Ten ways autonomous driving could redefine the automotive world. McKinsey & Company.

http://www.mckinsey.com/industries/automotive-and-assembly/our-insights/ten-ways-autonomous-driving-could-redefine-the-automotive-world

• Research and Markets (2024) United States Autonomous Vehicles Market, Size, Forecast 2024-2030, Industry Trends, Share, Growth, Insight, Impact of Inflation, Company Analysis. 

https://www.researchandmarkets.com/report/united-states-autonomous-car-market?utm_source=CI&utm_medium=PressRelease&utm_code=hh544h&utm_campaign=1944080+-+United+States+Autonomous+Vehicles+Research+Report+2024%3a+A+%2478.63+Billion+Market+by+2030+Driven+by+Advancements+in+Linked+Car+Technology&utm_exec=chdomspi

• Moorthy, A., et al. (2017) "Shared Autonomous Vehicles as a Sustainable Solution to the Last Mile Problem: A Case Study of Ann Arbor-Detroit Area" SAE International Journal of Passenger Cars: 10(2).

https://www.researchgate.net/publication/315917148_Shared_Autonomous_Vehicles_as_a_Sustainable_Solution_to_the_Last_Mile_Problem_A_Case_Study_of_Ann_Arbor-Detroit_Area

• Cordts, Paige, et al. (2021) "Mobility challenges and perceptions of autonomous vehicles for individuals with physical disabilities." Disability and health journal 14.4 (2021): 101131.

https://www.lta.gov.sg/ltaacademy/doc/J14Nov_p12Rodoulis_AVcities.pdf

• Howard, D. and D. Dai (2014) Public Perceptions of Self-Driving Cars: The Case of Berkeley, California.

https://www.ocf.berkeley.edu/~djhoward/reports/Report%20-%20Public%20Perceptions%20of%20Self%20Driving%20Cars.pdf

• Taiebat, M., et al. (2019) "Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound." Applied Energy 247: 297-308.

http://css.umich.edu/publication/forecasting-impact-connected-and-automated-vehicles-energy-use-microeconomic-study

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• A-Z Publications

Annual Review of Control, Robotics, and Autonomous Systems

Volume 1, 2018, review article, planning and decision-making for autonomous vehicles.

• Wilko Schwarting 1 , Javier Alonso-Mora 2 , and Daniela Rus 1
• View Affiliations Hide Affiliations Affiliations: 1 Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA; email: [email protected] , [email protected] 2 Department of Cognitive Robotics, Delft University of Technology, 2628 Delft, The Netherlands; email: [email protected]
• Vol. 1:187-210 (Volume publication date May 2018) https://doi.org/10.1146/annurev-control-060117-105157
• Copyright © 2018 by Annual Reviews. All rights reserved

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles.

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Literature Cited

• 1.  Fed. Highw. Adm. 2015 . U.S. driving increases for sixth straight year, new federal data show Press Release, Fed. Highw. Adm., US Dep. Transp Washington, DC: https://www.fhwa.dot.gov/pressroom/fhwa1711.cfm [Google Scholar]
• 2.  Assoc. Safe Intl. Road Travel (ASIRT). 2017 . Home page. http://www.asirt.org
• 3.  Natl. Saf. Counc. (NSC). 2015 . NSC motor vehicle fatality estimates 2012–2015 Rep., Stat. Dep., NSC, Itasca, IL. http://www.nsc.org/NewsDocuments/2016/mv-fatality-report-1215.pdf [Google Scholar]
• 4.  Natl. Highw. Traffic Saf. Adm. (NHTSA). 2015 . Traffic safety facts 2015 Rep., NHTSA, US Dep. Transp Washington, DC: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812384 [Google Scholar]
• 5.  SAE Intl. 2016 . Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles Stand. J3016, SAE Intl Warrendale, PA: [Google Scholar]
• 6.  Russell HEB , Harbott LK , Nisky I , Pan S , Okamura AM , Gerdes JC 2016 . Motor learning affects car-to-driver handover in automated vehicles. Sci. Robot. 1 : eaah5682 [Google Scholar]
• 7.  Maurer M , Gerdes JC , Lenz B , Winner H 2016 . Autonomous Driving: Technical, Legal and Social Aspects Berlin: Springer [Google Scholar]
• 8.  Buehler M , Iagnemma K , Singh S 2007 . The 2005 DARPA Grand Challenge: The Great Robot Race Berlin: Springer [Google Scholar]
• 9.  Buehler M , Iagnemma K , Singh S 2009 . The DARPA Urban Challenge: Autonomous Vehicles in City Traffic Berlin: Springer [Google Scholar]
• 10.  Urmson C , Anhalt J , Bagnell D , Baker C , Bittner R et al. 2008 . Autonomous driving in urban environments: Boss and the Urban Challenge. J. Field Robot. 25 : 425– 66 [Google Scholar]
• 11.  Leonard J , How J , Teller S , Berger M , Campbell S et al. 2008 . A perception-driven autonomous urban vehicle. J. Field Robot. 25 : 727– 74 [Google Scholar]
• 12.  Furgale P , Schwesinger U , Rufli M , Derendarz W , Grimmett H et al. 2013 . Toward automated driving in cities using close-to-market sensors: an overview of the V-Charge Project. 2013 IEEE Intelligent Vehicles Symposium (IV) 809– 16 New York: IEEE [Google Scholar]
• 13.  Ulbrich S , Reschka A , Rieken J , Ernst S , Bagschik G et al. 2017 . Towards a functional system architecture for automated vehicles. arXiv:1703.08557
• 14.  De Luca A , Oriolo G , Samson C 1998 . Feedback control of a nonholonomic car-like robot. Robot Motion Planning and Control JP Laumond 171– 253 Berlin: Springer [Google Scholar]
• 15.  Gillespie TD 1997 . Vehicle Dynamics Warrendale, PA: Soc. Automot. Eng. [Google Scholar]
• 16.  Pacejka H 2012 . Tire and Vehicle Dynamics Oxford, UK: Elsevier, 3rd ed.. [Google Scholar]
• 17.  Rajamani R 2012 . Vehicle Dynamics and Control New York: Springer, 2nd ed.. [Google Scholar]
• 18.  Hoffmann GM , Tomlin CJ , Montemerlo M , Thrun S 2007 . Autonomous automobile trajectory tracking for off-road driving: controller design, experimental validation and racing. 2007 American Control Conference 2296– 301 New York: IEEE [Google Scholar]
• 19.  Falcone P , Borrelli F , Asgari J , Tseng HE , Hrovat D 2007 . Predictive active steering control for autonomous vehicle systems. IEEE Trans. Control Syst. Technol. 15 : 566– 80 [Google Scholar]
• 20.  Kapania NR , Gerdes JC 2015 . Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling. Vehicle Syst. Dyn. 53 : 1687– 704 [Google Scholar]
• 21.  Nelles O 2001 . Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models Berlin: Springer [Google Scholar]
• 22.  Seegmiller N , Rogers-Marcovitz F , Miller G , Kelly A 2013 . Vehicle model identification by integrated prediction error minimization. Int. J. Robot. Res. 32 : 912– 31 [Google Scholar]
• 23.  Anderson SJ , Karumanchi SB , Iagnemma K , Walker JM 2013 . The intelligent copilot: a constraint-based approach to shared-adaptive control of ground vehicles. IEEE Intell. Transp. Syst. Mag. 5 : 45– 54 [Google Scholar]
• 24.  Abbink DA , Mulder M , Boer ER 2011 . Haptic shared control: smoothly shifting control authority?. Cogn. Technol. Work 14 : 19– 28 [Google Scholar]
• 25.  Alonso-Mora J , Gohl P , Watson S , Siegwart R , Beardsley P 2014 . Shared control of autonomous vehicles based on velocity space optimization. 2014 IEEE International Conference on Robotics and Automation (ICRA) 1639– 45 New York: IEEE [Google Scholar]
• 26.  Shia VA , Gao Y , Vasudevan R , Campbell KD , Lin T et al. 2014 . Semiautonomous vehicular control using driver modeling. IEEE Trans. Intell. Transp. Syst. 15 : 2696– 709 [Google Scholar]
• 27.  Erlien SM , Fujita S , Gerdes JC 2016 . Shared steering control using safe envelopes for obstacle avoidance and vehicle stability. IEEE Trans. Intell. Transp. Syst. 17 : 441– 51 [Google Scholar]
• 28.  Schwarting W , Alonso-Mora J , Paull L , Karaman S , Rus D 2017 . Parallel autonomy in automated vehicles: safe motion generation with minimal intervention. 2017 IEEE International Conference on Robotics and Automation (ICRA) 1928– 35 New York: IEEE [Google Scholar]
• 28a.  Schwarting W , Alonso-Mora J , Paull L , Karaman S , Rus D 2018 . Safe nonlinear trajectory generation for parallel autonomy with a dynamic vehicle model. IEEE Trans. Intell. Transp. Syst. https://doi.org/10.1109/TITS.2017.2771351 [Crossref] [Google Scholar]
• 29.  Katrakazas C , Quddus M , Chen WH , Deka L 2015 . Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transp. Res. C 60 : 416– 42 [Google Scholar]
• 30.  Paden B , Cap M , Yong SZ , Yershov D , Frazzoli E 2016 . A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1 : 33– 55 [Google Scholar]
• 31.  Ferguson D , Howard TM , Likhachev M 2008 . Motion planning in urban environments. J. Field Robot. 25 : 939– 60 [Google Scholar]
• 32.  Pivtoraiko M , Knepper RA , Kelly A 2009 . Differentially constrained mobile robot motion planning in state lattices. J. Field Robot. 26 : 308– 33 [Google Scholar]
• 33.  Werling M , Kammel S , Ziegler J , Gröll L 2012 . Optimal trajectories for time-critical street scenarios using discretized terminal manifolds. Int. J. Robot. Res. 31 : 346– 59 [Google Scholar]
• 34.  LaValle SM , Kuffner JJ 2001 . Randomized kinodynamic planning. Int. J. Robot. Res. 20 : 378– 400 [Google Scholar]
• 35.  Karaman S , Frazzoli E 2011 . Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30 : 846– 94 [Google Scholar]
• 36.  Liniger A , Domahidi A , Morari M 2014 . Optimization-based autonomous racing of 1:43 scale RC cars. Opt. Control Appl. Methods 36 : 628– 47 [Google Scholar]
• 37.  Andersen H , Schwarting W , Naser F , Eng YH , Ang MH Jr. et al. 2017 . Trajectory optimization for autonomous overtaking with visibility maximization. 2017 IEEE International Conference on Intelligent Transportation Systems (ITSC) New York: IEEE. In press [Google Scholar]
• 38.  Kuwata Y , Teo J , Fiore G , Karaman S , Frazzoli E , How JP 2009 . Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17 : 1105– 18 [Google Scholar]
• 39.  Tumova J , Hall GC , Karaman S , Frazzoli E , Rus D 2013 . Least-violating control strategy synthesis with safety rules. HSCC '13: Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control 1– 10 New York: ACM [Google Scholar]
• 40.  Vasile CI , Tumova J , Karaman S , Belta C , Rus D 2017 . Minimum-violation scLTL motion planning for mobility-on-demand. 2017 IEEE International Conference on Robotics and Automation (ICRA) 1481– 88 New York: IEEE [Google Scholar]
• 41.  Janai J , Güney F , Behl A , Geiger A 2017 . Computer vision for autonomous vehicles: problems, datasets and state-of-the-art. arXiv : 1704.05519 [Google Scholar]
• 42.  Geiger A , Lenz P , Stiller C , Urtasun R 2013 . Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32 : 1231– 37 [Google Scholar]
• 43.  Cordts M , Omran M , Ramos S , Rehfeld T , Enzweiler M et al. 2016 . The Cityscapes dataset for semantic urban scene understanding. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3213– 23 New York: IEEE [Google Scholar]
• 44.  Lowe DG 1999 . Object recognition from local scale-invariant features. Seventh IEEE International Conference on Computer Vision (ICCV) 2 1150– 57 New York: IEEE [Google Scholar]
• 45.  Lowe DG 2004 . Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60 : 91– 110 [Google Scholar]
• 46.  Leutenegger S , Chli M , Siegwart RY 2011 . BRISK: Binary Robust Invariant Scalable Keypoints. 2011 IEEE International Conference on Computer Vision (ICCV) 2548– 55 New York: IEEE [Google Scholar]
• 47.  Bay H , Ess A , Tuytelaars T , Gool LV 2008 . Speeded-Up Robust Features (SURF). Comput. Vis. Image Understand. 110 : 346– 59 [Google Scholar]
• 48.  Bay H , Tuytelaars T , Van Gool L 2006 . SURF: Speeded Up Robust Features. Computer Vision – ECCV 2006 A Leonardis, H Bischof, A Pinz 404– 17 Berlin: Springer [Google Scholar]
• 49.  Rublee E , Rabaud V , Konolige K , Bradski G 2011 . ORB: an efficient alternative to SIFT or SURF. 2011 IEEE International Conference on Computer Vision (ICCV) 2564– 71 New York: IEEE [Google Scholar]
• 50.  Mur-Artal R , Tardós JD 2017 . ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33 : 1255– 62 [Google Scholar]
• 51.  Zhang J , Singh S 2015 . Visual-LIDAR odometry and mapping: low-drift, robust, and fast. 2015 IEEE International Conference on Robotics and Automation (ICRA) 2174– 81 New York: IEEE [Google Scholar]
• 52.  Forster C , Zhang Z , Gassner M , Werlberger M , Scaramuzza D 2017 . SVO: semidirect visual odometry for monocular and multicamera systems. IEEE Trans. Robot. 33 : 249– 65 [Google Scholar]
• 53.  Engel J , Stckler J , Cremers D 2015 . Large-scale direct SLAM with stereo cameras. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 1935– 42 New York: IEEE [Google Scholar]
• 54.  Bar Hillel A , Lerner R , Levi D , Raz G 2014 . Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 25 : 727– 45 [Google Scholar]
• 55.  Russakovsky O , Deng J , Su H , Krause J , Satheesh S et al. 2015 . ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115 : 211– 52 [Google Scholar]
• 56.  Ren S , He K , Girshick R , Sun J 2017 . Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39 : 1137– 49 [Google Scholar]
• 57.  He K , Zhang X , Ren S , Sun J 2016 . Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770– 78 New York: IEEE [Google Scholar]
• 58.  Zhao H , Shi J , Qi X , Wang X , Jia J 2017 . Pyramid scene parsing network. arXiv : 1612.01105 [Google Scholar]
• 59.  Paszke A , Chaurasia A , Kim S , Culurciello E 2016 . ENet: a deep neural network architecture for real-time semantic segmentation. arXiv : 1606.02147 [Google Scholar]
• 60.  Zhao H , Qi X , Shen X , Shi J , Jia J 2017 . ICNet for real-time semantic segmentation on high-resolution images. arXiv : 1704.08545 [Google Scholar]
• 61.  Ros G , Sellart L , Materzynska J , Vazquez D , Lopez AM 2016 . The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3234– 43 New York: IEEE [Google Scholar]
• 62.  Johnson-Roberson M , Barto C , Mehta R , Sridhar SN , Rosaen K , Vasudevan R 2017 . Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?. 2017 IEEE International Conference on Robotics and Automation (ICRA) 746– 53 New York: IEEE [Google Scholar]
• 63.  Richter SR , Vineet V , Roth S , Koltun V 2016 . Playing for data: ground truth from computer games. Computer Vision – ECCV 2016 B Leibe, J Matas, N Sebe, M Welling 102– 18 Cham, Switz.: Springer [Google Scholar]
• 64.  Herranz L , Jiang S , Li X 2016 . Scene recognition with CNNs: objects, scales and dataset bias. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 571– 79 New York: IEEE [Google Scholar]
• 65.  Gal Y , Ghahramani Z 2016 . Dropout as a Bayesian approximation: representing model uncertainty in deep learning. ICML '16: 33rd International Conference on Machine Learning MF Balcan, KQ Weinberger 1050– 59 New York: PMLR [Google Scholar]
• 66.  McAllister R , Gal Y , Kendall A , van der Wilk M , Shah A et al. 2017 . Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) 4745– 53 Calif.: IJCAI [Google Scholar]
• 67.  Chen C , Seff A , Kornhauser A , Xiao J 2015 . DeepDriving: learning affordance for direct perception in autonomous driving. 2015 IEEE International Conference on Computer Vision (ICCV) 2722– 30 New York: IEEE [Google Scholar]
• 68.  Caltagirone L , Bellone M , Svensson L , Wahde M 2017 . LIDAR-based driving path generation using fully convolutional neural networks. arXiv : 1703.08987 [Google Scholar]
• 69.  Barnes D , Maddern W , Posner I 2017 . Find your own way: weakly-supervised segmentation of path proposals for urban autonomy. 2017 IEEE International Conference on Robotics and Automation (ICRA) 203– 10 New York: IEEE [Google Scholar]
• 70.  Pomerleau DA 1989 . ALVINN: an autonomous land vehicle in a neural network. Advances in Neural Information Processing Systems 1 DS Touretzky 305– 13 San Francisco: Morgan Kaufmann [Google Scholar]
• 71.  Muller U , Ben J , Cosatto E , Flepp B , Cun YL 2006 . Off-road obstacle avoidance through end-to-end learning. Advances in Neural Information Processing Systems 18 Y Weiss, PB Schölkopf, JC Platt 739– 46 Cambridge, MA: MIT Press [Google Scholar]
• 72.  Bojarski M , Del Testa D , Dworakowski D , Firner B , Flepp B et al. 2016 . End to end learning for self-driving cars. arXiv : 1604.07316 [Google Scholar]
• 73.  Bojarski M , Yeres P , Choromanska A , Choromanski K , Firner B et al. 2017 . Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv : 1704.07911 [Google Scholar]
• 74.  Gurghian A , Koduri T , Bailur SV , Carey KJ , Murali VN 2016 . DeepLanes: end-to-end lane position estimation using deep neural networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 38– 45 New York: IEEE [Google Scholar]
• 75.  Xu H , Gao Y , Yu F , Darrell T 2016 . End-to-end learning of driving models from large-scale video datasets. arXiv : 1612.01079 [Google Scholar]
• 76.  Zhang J , Cho K 2016 . Query-efficient imitation learning for end-to-end autonomous driving. arXiv : 1605.06450 [Google Scholar]
• 77.  Ross S , Gordon GJ , Bagnell D 2011 . A reduction of imitation learning and structured prediction to no-regret online learning. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics 627– 35 New York: PMLR [Google Scholar]
• 78.  Pfeiffer M , Schaeuble M , Nieto J , Siegwart R , Cadena C 2017 . From perception to decision: a data-driven approach to end-to-end motion planning for autonomous ground robots. 2017 IEEE International Conference on Robotics and Automation (ICRA) 1527– 33 New York: IEEE [Google Scholar]
• 79.  Chen YF , Everett M , Liu M , How JP 2017 . Socially aware motion planning with deep reinforcement learning. arXiv : 1703.08862 [Google Scholar]
• 80.  Richter C , Roy N 2017 . Safe visual navigation via deep learning and novelty detection. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 64 N.p.: Robot. Sci. Syst. Found. [Google Scholar]
• 81.  Wolf P , Hubschneider C , Weber M , Bauer A , Hrtl J et al. 2017 . Learning how to drive in a real world simulation with deep Q-networks. 2017 IEEE Intelligent Vehicles Symposium (IV) 244– 50 New York: IEEE [Google Scholar]
• 82.  You Y , Pan X , Wang Z , Lu C 2017 . Virtual to real reinforcement learning for autonomous driving. arXiv : 1704.03952 [Google Scholar]
• 83.  Lillicrap TP , Hunt JJ , Pritzel A , Heess N , Erez T et al. 2015 . Continuous control with deep reinforcement learning. arXiv : 1509.02971 [Google Scholar]
• 84.  Montemerlo M , Becker J , Bhat S , Dahlkamp H , Dolgov D et al. 2008 . Junior: the Stanford entry in the Urban Challenge. J. Field Robot. 25 : 569– 97 [Google Scholar]
• 85.  Trautman P , Ma J , Murray RM , Krause A 2015 . Robot navigation in dense human crowds: statistical models and experimental studies of human-robot cooperation. Int. J. Robot. Res. 34 : 335– 56 [Google Scholar]
• 86.  Toit NED , Burdick JW 2012 . Robot motion planning in dynamic, uncertain environments. IEEE Trans. Robot. 28 : 101– 15 [Google Scholar]
• 87.  Sadigh D , Sastry S , Seshia SA , Dragan AD 2016 . Planning for autonomous cars that leverage effects on human actions. Robotics: Science and Systems XII D Hsu, N Amato, S Berman, S Jacobs, chap. 29 N.p.: Robot. Sci. Syst. Found. [Google Scholar]
• 88.  Kretzschmar H , Spies M , Sprunk C , Burgard W 2016 . Socially compliant mobile robot navigation via inverse reinforcement learning. Int. J. Robot. Res. 35 : 1289– 307 [Google Scholar]
• 89.  Düring M , Pascheka P 2014 . Cooperative decentralized decision making for conflict resolution among autonomous agents. 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings 154– 61 New York: IEEE [Google Scholar]
• 90.  Ulbrich S , Grossjohann S , Appelt C , Homeier K , Rieken J , Maurer M 2015 . Structuring cooperative behavior planning implementations for automated driving. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC) 2159– 65 New York: IEEE [Google Scholar]
• 91.  Bahram M , Lawitzky A , Friedrichs J , Aeberhard M , Wollherr D 2016 . A game-theoretic approach to replanning-aware interactive scene prediction and planning. IEEE Trans. Veh. Technol. 65 : 3981– 92 [Google Scholar]
• 92.  Lenz D , Kessler T , Knoll A 2016 . Tactical cooperative planning for autonomous highway driving using Monte-Carlo tree search. 2016 IEEE Intelligent Vehicles Symposium (IV) 447– 53 New York: IEEE [Google Scholar]
• 93.  Schwarting W , Pascheka P 2014 . Recursive conflict resolution for cooperative motion planning in dynamic highway traffic. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) , 1039– 44 New York: IEEE [Google Scholar]
• 94.  Li N , Oyler DW , Zhang M , Yildiz Y , Kolmanovsky I , Girard AR 2017 . Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems. IEEE Trans. Control Syst. Technol. In press. https://doi.org/10.1109/TCST.2017.2723574 [Crossref] [Google Scholar]
• 95.  Wei J , Dolan JM , Litkouhi B 2013 . Autonomous vehicle social behavior for highway entrance ramp management. 2013 IEEE Intelligent Vehicles Symposium (IV) 201– 7 New York: IEEE [Google Scholar]
• 96.  Evestedt N , Ward E , Folkesson J , Axehill D 2016 . Interaction aware trajectory planning for merge scenarios in congested traffic situations. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) 465– 72 New York: IEEE [Google Scholar]
• 97.  Hoermann S , Stumper D , Dietmayer K 2017 . Probabilistic long-term prediction for autonomous vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV) 237– 43 New York: IEEE [Google Scholar]
• 98.  Dong C , Dolan JM , Litkouhi B 2017 . Intention estimation for ramp merging control in autonomous driving. 2017 IEEE Intelligent Vehicles Symposium (IV) 1584– 89 New York: IEEE [Google Scholar]
• 99.  Hubmann C , Becker M , Althoff D , Lenz D , Stiller C 2017 . Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV) 1671– 78 New York: IEEE [Google Scholar]
• 100.  Liu W , Kim SW , Pendleton S , Ang MH 2015 . Situation-aware decision making for autonomous driving on urban road using online POMDP. 2015 IEEE Intelligent Vehicles Symposium (IV) 1126– 33 New York: IEEE [Google Scholar]
• 101.  Ulbrich S , Maurer M 2015 . Towards tactical lane change behavior planning for automated vehicles. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC) 989– 95 New York: IEEE [Google Scholar]
• 102.  Galceran E , Cunningham AG , Eustice RM , Olson E 2017 . Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: theory and experiment. Auton. Robots 41 : 1367– 82 [Google Scholar]
• 102a.  Zhou B , Schwarting W , Rus D , Alonso-Mora J 2018 . Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving. 2018 IEEE International Conference on Robotics and Automation (ICRA). New York: IEEE. In press [Google Scholar]
• 103.  Brechtel S , Gindele T , Dillmann R 2014 . Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs. 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) 392– 99 New York: IEEE [Google Scholar]
• 104.  Shalev-Shwartz S , Shammah S , Shashua A 2016 . Safe, multi-agent, reinforcement learning for autonomous driving. arXiv : 1610.03295 [Google Scholar]
• 105.  Vallon C , Ercan Z , Carvalho A , Borrelli F 2017 . A machine learning approach for personalized autonomous lane change initiation and control. 2017 IEEE Intelligent Vehicles Symposium (IV) 1590– 95 New York: IEEE [Google Scholar]
• 106.  Lenz D , Diehl F , Le MT , Knoll A 2017 . Deep neural networks for Markovian interactive scene prediction in highway scenarios. 2017 IEEE Intelligent Vehicles Symposium (IV) 685– 92 New York: IEEE [Google Scholar]
• 107.  Lee N , Choi W , Vernaza P , Choy CB , Torr PH , Chandraker M 2017 . DESIRE: distant future prediction in dynamic scenes with interacting agents. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2165– 74 New York: IEEE [Google Scholar]
• 108.  Sadigh D , Sastry SS , Seshia SA , Dragan A 2016 . Information gathering actions over human internal state. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 66– 73 New York: IEEE [Google Scholar]
• 109.  Sadigh D , Dragan A , Sastry S , Seshia S 2017 . Active preference-based learning of reward functions. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 53 N.p.: Robot. Sci. Syst. Found. [Google Scholar]
• 110.  Huang SH , Held D , Abbeel P , Dragan AD 2017 . Enabling robots to communicate their objectives. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 59 N.p.: Robot. Sci. Syst. Found. [Google Scholar]
• 111.  Abbeel P , Ng AY 2004 . Apprenticeship learning via inverse reinforcement learning. ICML '04: Proceedings of the Twenty-First International Conference on Machine Learning chap. 1 New York: ACM [Google Scholar]
• 112.  Abbeel P , Dolgov D , Ng AY , Thrun S 2008 . Apprenticeship learning for motion planning with application to parking lot navigation. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 1083– 90 New York: IEEE [Google Scholar]
• 113.  Ziebart BD , Maas AL , Bagnell JA , Dey AK 2008 . Maximum entropy inverse reinforcement learning. 23rd AAAI Conference on Artificial Intelligence 1433– 38 Menlo Park, CA: AAAI Press [Google Scholar]
• 114.  Kuderer M , Gulati S , Burgard W 2015 . Learning driving styles for autonomous vehicles from demonstration. 2015 IEEE International Conference on Robotics and Automation (ICRA) 2641– 46 New York: IEEE [Google Scholar]
• 115.  Pfeiffer M , Schwesinger U , Sommer H , Galceran E , Siegwart R 2016 . Predicting actions to act predictably: cooperative partial motion planning with maximum entropy models. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2096– 101 New York: IEEE [Google Scholar]
• 116.  Herman M , Fischer V , Gindele T , Burgard W 2015 . Inverse reinforcement learning of behavioral models for online-adapting navigation strategies. 2015 IEEE International Conference on Robotics and Automation (ICRA) 3215– 22 New York: IEEE [Google Scholar]
• 117.  Ratliff ND , Bagnell JA , Zinkevich MA 2006 . Maximum margin planning. Proceedings of the 23rd International Conference on Machine Learning 729– 36 New York: ACM [Google Scholar]
• 118.  Silver D , Bagnell JA , Stentz A 2010 . Learning from demonstration for autonomous navigation in complex unstructured terrain. Int. J. Robot. Res. 29 : 1565– 92 [Google Scholar]
• 119.  Silver D , Bagnell JA , Stentz A 2013 . Learning autonomous driving styles and maneuvers from expert demonstration. Experimental Robotics: The 13th International Symposium on Experimental Robotics J Desai, G Dudek, O Khatib, V Kumar 371– 86 Heidelberg, Ger.: Springer [Google Scholar]
• 120.  Levine S , Koltun V 2012 . Continuous inverse optimal control with locally optimal examples. Proceedings of the 29th International Conference on International Conference on Machine Learning 475– 82 Madison, WI: Omnipress [Google Scholar]
• 121.  Majumdar A , Singh S , Mandlekar A , Pavone M 2017 . Risk-sensitive inverse reinforcement learning via coherent risk models. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 69 N.p.: Robot. Sci. Syst. Found. [Google Scholar]
• 122.  Wulfmeier M , Ondruska P , Posner I 2015 . Maximum entropy deep inverse reinforcement learning. arXiv : 1507.04888 [Google Scholar]
• 123.  Wulfmeier M , Rao D , Wang DZ , Ondruska P , Posner I 2017 . Large-scale cost function learning for path planning using deep inverse reinforcement learning. Int. J. Robot. Res. 10 : 1073– 87 [Google Scholar]
• 124.  Kuefler A , Morton J , Wheeler T , Kochenderfer M 2017 . Imitating driver behavior with generative adversarial networks. arXiv : 1701.06699 [Google Scholar]
• 125.  Ho J , Ermon S 2016 . Generative adversarial imitation learning. Advances in Neural Information Processing Systems 29 DD Lee, M Sugiyama, UV Luxburg, I Guyon, R Garnett 4565– 73 New York: Curran Assoc. [Google Scholar]
• 126.  Kalra N , Paddock S 2016 . Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Rep. RR-1478-RC, Rand Corp Santa Monica, CA: http://www.rand.org/pubs/research_reports/RR1478.html [Google Scholar]
• 127.  Nilsson P , Hussien O , Balkan A , Chen Y , Ames AD et al. 2016 . Correct-by-construction adaptive cruise control: two approaches. IEEE Trans. Control Syst. Technol. 24 : 1294– 307 [Google Scholar]
• 128.  Kim ES , Arcak M , Seshia SA 2015 . Compositional controller synthesis for vehicular traffic networks. 2015 54th IEEE Conference on Decision and Control (CDC) 6165– 71 New York: IEEE [Google Scholar]
• 129.  Wongpiromsarn T 2010 . Formal methods for design and verification of embedded control systems: application to an autonomous vehicle . PhD Thesis, Calif. Inst. Technol Pasadena, CA: [Google Scholar]
• 130.  Loos SM , Platzer A , Nistor L 2011 . Adaptive cruise control: hybrid, distributed, and now formally verified. FM 2011: Formal Methods M Butler, W Schulte 42– 56 Berlin: Springer [Google Scholar]
• 131.  Althoff M , Dolan JM 2014 . Online verification of automated road vehicles using reachability analysis. IEEE Trans. Robot. 30 : 903– 18 [Google Scholar]
• 132.  Schürmann B , Heß D , Eilbrecht J , Stursberg O , Köster F , Althoff M 2017 . Ensuring drivability of planned motions using formal methods. In 2017 20th IEEE Intelligent Transportation Systems Conference (ITSC) New York: IEEE. In press [Google Scholar]
• 133.  Liebenwein L , Schwarting W , Vasile CI , DeCastro J , Alonso-Mora J et al. 2018 . Compositional and contract-based verification for autonomous driving on road networks. Robotics Research: The 18th International Symposium ISRR Forthcoming [Google Scholar]
• 134.  Katz G , Barrett C , Dill DL , Julian K , Kochenderfer MJ 2017 . Reluplex: an efficient SMT solver for verifying deep neural networks. Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24–28, 2017, Proceedings, Part I R Majumdar, V Kunčak 97– 117 Cham, Switz.: Springer [Google Scholar]
• 135.  Seshia SA , Sadigh D , Sastry SS 2016 . Towards verified artificial intelligence. arXiv : 1606.08514 [Google Scholar]
• 136.  Pavone M , Smith S , Frazzoli E , Rus D 2012 . Robotic load balancing for mobility-on-demand systems. Int. J. Robot. Res. 31 : 839– 54 [Google Scholar]
• 137.  Zhang R , Pavone M 2016 . Control of robotic mobility-on-demand systems: a queueing-theoretical perspective. Int. J. Robot. Res. 35 : 186– 203 [Google Scholar]
• 138.  de Almeida Correia GH , van Arem B 2016 . Solving the user optimum privately owned automated vehicles assignment problem (UO-POAVAP): a model to explore the impacts of self-driving vehicles on urban mobility. Transp. Res. B 87 : 64– 88 [Google Scholar]
• 139.  Toth P , Vigo D 2014 . Vehicle Routing: Problems, Methods, and Applications Philadelphia: SIAM, 2nd ed.. [Google Scholar]
• 140.  Pillac V , Gendreau M , Guéret C , Medaglia AL 2013 . A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225 : 1– 11 [Google Scholar]
• 141.  Agatz NA , Erera AL , Savelsbergh MW , Wang X 2011 . Dynamic ride-sharing: a simulation study in metro Atlanta. Transp. Res. B 45 : 1450– 64 [Google Scholar]
• 142.  Santi P , Resta G , Szell M , Sobolevsky S , Strogatz SH , Ratti C 2014 . Quantifying the benefits of vehicle pooling with shareability networks. PNAS 111 : 13290– 94 [Google Scholar]
• 143.  Alonso-Mora J , Samaranayake S , Wallar A , Frazzoli E , Rus D 2017 . On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. PNAS 114 : 462– 67 [Google Scholar]
• 144.  Shaheen S , Christensen M 2014 . The true future of transportation has two big barriers to entry. CityLab Apr. 25. https://www.citylab.com/transportation/2014/04/true-future-transportation-has-two-big-barriers-entry/8933 [Google Scholar]
• 145.  NYC OpenData. 2016 . New York City yellow taxi trip data https://data.cityofnewyork.us/dataset/2016-Yellow-Taxi-Trip-Data/k67s-dv2t [Google Scholar]
• 146.  Ritzinger U , Puchinger J , Hartl RF 2016 . A survey on dynamic and stochastic vehicle routing problems. Int. J. Prod. Res. 54 : 215– 31 [Google Scholar]
• 147.  Alonso-Mora J , Wallar A , Rus D 2017 . Predictive routing for autonomous mobility-on-demand systems with ride-sharing. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 3583– 90 New York: IEEE [Google Scholar]
• 148.  Barnard M 2016 . Autonomous cars likely to increase congestion. CleanTechnica Jan. 17. http://cleantechnica.com/2016/01/17/autonomous-cars-likely-increase-congestion [Google Scholar]
• 149.  Zhang R , Rossi F , Pavone M 2017 . Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms. Auton. Robots. In press [Google Scholar]
• 150.  Levin MW 2017 . Congestion-aware system optimal route choice for shared autonomous vehicles. Transp. Res. C 82 : 229– 47 [Google Scholar]

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• Published: 06 May 2023

Exploration of issues, challenges and latest developments in autonomous cars

• B. Padmaja   ORCID: orcid.org/0000-0001-5327-9795 1 ,
• CH. V. K. N. S. N. Moorthy   ORCID: orcid.org/0000-0001-7209-8017 2 , 3 ,
• N. Venkateswarulu   ORCID: orcid.org/0000-0003-1782-579X 4 &
• Myneni Madhu Bala   ORCID: orcid.org/0000-0003-4734-5914 5  

Journal of Big Data volume  10 , Article number:  61 ( 2023 ) Cite this article

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Autonomous cars have achieved exceptional growth in the automotive industry in the last century in terms of reliability, safety and affordability. Due to significant advancements in computing, communication and other technologies, today we are in the era of autonomous cars. A number of prototype models of autonomous cars have been tested covering several miles of test drives. Many prominent car manufacturers have started investing huge resources in this technology to make it commercialize in the near future years. But to achieve this goal still there are a number of technical and non-technical challenges that exist in terms of real-time implementation, consumer satisfaction, security and privacy concerns, policies and regulations. In summary, this survey paper presents a comprehensive and up-to-date overview of the latest developments in the field of autonomous cars, including cutting-edge technologies, innovative applications, and testing. It addresses the key obstacles and challenges hindering the progress of autonomous car development, making it a valuable resource for anyone interested in understanding the current state of the art and future potential of autonomous cars.

Introduction

Autonomous cars are becoming more pragmatic from year to year as multi-national companies are racing ahead to produce intelligent vehicles. The projected value for autonomous vehicles in the global market is at $615 Billion by 2026. According to the U.S. Department of Transportation's National Highway Traffic Safety Administration (NHTSA) fully automated or autonomous vehicles are "those in which operation of the vehicle occurs without direct driver input to control the steering, acceleration, and braking and are designed so that the driver is not expected to constantly monitor the roadway while operating in self-driving mode.". The advancement and rise of autonomous cars are due to the significant research results obtained in the arenas of wireless and embedded systems, sensors, communication technologies, navigation, data acquisition, and analysis.

The initial thought of autonomous cars was initiated in the year 1920 with the "phantom autos" concept, which means a remote-control device, which was used to control the vehicle [ 1 ]. Later in the 1980s self-managed autonomous cars were developed. Further, NavLab of Carnegie Mellon University contributed majorly in this field by developing an Autonomous Land Vehicle (ALV) [ 2 ]. In a major breakthrough in 1987, the “Prometheus project” of Mercedes [ 3 ] gave the design of their first automated car with the capability of tracking lanes. At that time, it was not completely autonomous, but it had the ability to automatically switch lanes. In the twenty-first century, there is a huge demand for low-cost, high-performance autonomous cars. There is a fine line of difference between the two terminologies: the automated car and the autonomous car. The term automated car refers to a vehicle with little human intervention, whereas the term autonomous car refers to a vehicle without any human intervention. The autonomous car is a fully computer-controlled car which can instruct (guide) itself, make its own decisions, familiar with its surrounding without any human interference (intervention).

The concept of connected car technology is influenced by autonomous cars [ 4 ] as both technologies are related to each other. Layered architectures are being proposed to address challenges faced due to the internet response time and the compatibility of various components that are being used in connected car technology [ 5 ]. Autonomous vehicles need to be connected to each other to improve overall autonomy when driving on the road. For example, the connected car works on a vehicular ad hoc network (VANET) technology and a dedicated short-range communication (DSRC) standard protocol [ 6 ] using which communication between vehicles is possible when they are in range. The VANET [ 7 ] provides 2 types of applications; one regarding safety and the other one related to infotainments. In Autonomous cars, communication related security measures are stringent whereas in connected cars, security measures are moderately relaxed. In the latest developments regarding the connected cars and VANET technologies, many multinational technology companies such as Google along with car manufacturers such as Tesla and Audi are working together and we see a solid collaboration among the technology companies and vehicle manufacturers to facilitate the development and design of cars.

Similarly, Microsoft has begun a coalition with Volvo and Toyota for building autonomous cars. Also, companies like NVidia have shown their dedication to making autonomous cars by launching NVidia Drive PX2, a dynamic supercomputer GPU and a deep learning-based computing platform for autonomous cars [ 8 ]. A few more Asian companies such as TATA, KIA, and Hyundai are funding in design, development, and research regarding automated cars. Also, the European auto market (Mercedes and BMW) is in the race for autonomous cars, and their goal is the development of full-fledged commercial versions by 2020. Many companies such as Volkswagen and the French PSA group are focusing on developing autonomous cars and they have started test-drive since 2016. The PSA group brought together many car manufacturers together and drove their autonomous cars covering hundreds of kilometers from Paris to Amsterdam without any driver supervision in 2016 on a level-3 autonomy [ 9 ].

In the development of an autonomous system, there are several issues that must be addressed properly by the car manufacturing companies such as governments’ regulations, consumer satisfaction, cost, reliability, and safety. Further, an important role is played by federal regulations in achieving novel technologies, and autonomous cars are no exemption from that. The automatic transmission system plays a vital role in autonomous cars and these days as most automobiles use this technology due to its reduction in cost and improvement in quality and management of a fleet of electric vehicles [ 10 ]. In brief, this technology will consume some more time period till it is affordable, and reachable to customers in terms of cost and reliability. This paper highlights all the issues and challenges involved in the development of autonomous cars and conducts a survey on the latest autonomous car technologies that are trying to overcome these issues and challenges in an efficient way.

Related work

Till today, a number of works have been done to explore multiple issues of autonomous car system [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. But the majority of surveys focus only on certain aspects of the autonomous car and none of these surveys present a comprehensive (holistic) method towards autonomous car technology.

Campbell et al. [ 11 ] discussed the approaches to challenges faced in urban environments by autonomous vehicles. Okuda et al. [ 12 ] did a thorough survey on the usage of advanced driving assistance (ADAS) in autonomous cars and the trends in the technology. Fagnant et al. [ 13 ] helped in surveying the required policies to make a nation ready for autonomous vehicles. Moreover, Bagloee et al. [ 14 ] focused on a few challenges that such different policies provide to autonomous cars. Other surveys regarding its functionalities include planning and motion control [ 15 ], long-term maps’ constructions [ 18 ] and visual perception from both implementation and operators’ perspectives [ 20 , 21 ]. Furthermore, Abraham et al. [ 16 ] conducted a survey on consumer trust and also the preferences of the consumer on already available alternatives. Gupta et al. [ 22 ] aimed to understand the public's attitude towards autonomous vehicles by analyzing large amounts of data from Twitter without the need for manual labeling. The authors used advanced machine learning techniques to analyze the data and identify patterns in the public's perception of autonomous vehicles. The study found that the majority of tweets about autonomous vehicles were positive in nature, but there were also concerns about safety and privacy. Joy et al. [ 17 ] looked over security and privacy issues in autonomous cars. Madhav et al. [ 23 ] presented a study on how to improve human trust in autonomous vehicles through the use of Explainable Artificial Intelligence (XAI). The authors focused on bridging the gap between artificial decision-making and human trust by providing an explanation for the decisions taken by autonomous vehicles. Bairy et al. [ 24 ] presented a model for explaining the decision-making process of autonomous vehicles. The model is based on integrated formal methods, which are mathematical methods used to model and verify systems. The authors claim that providing explanations for the actions of autonomous vehicles is important for building trust in the technology of autonomous vehicles and for ensuring accountability in the event of an accident.

Parkinson et al. [ 19 ] have expansively analyzed cyber threats in autonomous cars and the challenges they pose on the future of connected vehicles. Mazri et al. [ 25 ] proposed a self-defense mechanism against security attacks for autonomous vehicles. The mechanism is designed to protect against various types of cyberattacks, including those targeting the communication systems and decision-making processes of the vehicles. The authors presented a detailed analysis of the potential vulnerabilities and risks of autonomous vehicles, and provide a comprehensive evaluation of the proposed mechanism using various simulation scenarios. Li et al. [ 26 ] investigated the preferences of drivers when it comes to performing secondary tasks and how the vehicle's level of autonomy affects the driver's task engagement. The study also explored the potential impact of secondary tasks on safety and comfort while driving. The findings of the study can be used to design interfaces and interactions that better support drivers’ needs and preferences in highly autonomous vehicles.

The use of blockchain technology for training autonomous cars has been proposed by G. M. Gandhi et al. [ 27 ]. In this process, all connected cars can share their experience with each other. Blockchain can also be used to maintain energy transactions at charging stations [ 28 ]. Many recent surveys on autonomous cars have majorly focused on a few topics of autonomous cars. Table 1 presents a summary of related works. Figure  1 depicts the prediction of Autonomous vehicle implementation by 2060.

Predicted Development of Autonomous Vehicles from 2010 to 2060. This figure illustrates the expected advancement of autonomous vehicle technology over the course of the next several decades. The x-axis represents the years, starting in 2010 and ending in 2060. The y-axis represents the level of autonomy, with 0% being no automation and 100% being fully autonomous. The line represents the projected trajectory of the technology, starting at 0% in 2010 and gradually increasing to 100% by 2060. The graph illustrates the rapid pace of technological advancement in the field of autonomous vehicles and the significant impact it is expected to have on transportation in the coming years

This ground-breaking survey delves deep into the latest developments in the exciting field of autonomous cars. From cutting-edge technologies and innovative applications to in-depth simulations, this paper provides a comprehensive overview of the current state of the art. Additionally, it addresses the key obstacles and challenges that are hindering the progress of autonomous car development, including both technical and non-technical issues.

Organization

Organized in a clear and logical manner, this survey begins by summarizing existing works in the field and outlining the different levels of autonomy according to the standards set by the Society of Automotive Engineers (SAE).

The heart of the paper is dedicated to exploring the underlying technology behind autonomous cars, including detailed descriptions of their design, components, and functionalities. The benefits of autonomous cars are also discussed, along with a survey of state-of-the-art research outcomes.

The survey goes on to explore the implementation and design challenges that must be overcome in order to bring autonomous cars to reality. Finally, the paper concludes with a look at the technology and challenges for autonomous car deployment, making it a must-read for anyone interested in this rapidly evolving field.

Contribution

This survey provides a thorough and up-to-date overview of the latest developments in the field of autonomous cars, including cutting-edge technologies, innovative applications, and in-depth simulations. Additionally, it addresses the key obstacles and challenges that are hindering the progress of autonomous car development, and provides an in-depth look at the technology, benefits, and challenges behind autonomous car deployment. It is a valuable resource for anyone interested in understanding the current state of the art and future potential of autonomous cars.

The autonomous car technology

The autonomous car has been in the spotlight during the last few decades and prototype models have been improved by various car manufacturers. But, the commercial actualization of autonomous cars is a major challenge. As a basic task, gearing of every autonomous car is performed with numerous actuators and sensors that produce vast amounts of data which must be handled and analyzed to take decisions on time. The amount of data that the car must handle depends on the levels of autonomy [ 38 ] as shown in Table 2 . The key point is the requirement of various sensors and actuators to function autonomously, so that the car should foresee, decide and maneuver cautiously according to some strategy. A number of fundamental characteristics of autonomous car design are outlined in this section. Figure  2 depicts a number of elevated functional components of a typical autonomous car.

This image displays the functional architecture of an autonomous car. The architecture is divided into several functional blocks, each of which is responsible for a specific set of tasks. The main functional blocks of the architecture are Data Acquisition, Data Processing and Actuation – The components that carry out the commands from the control system like acceleration, braking and steering

The functional architecture of an autonomous car is a layered structure which contains data acquisition, data processing and actuation. Data acquisition is performed by the hardware components, such as sensors, radars, cameras, LIDAR, and transceivers [ 40 ]. The collected data is processed by a central computer system, which is later implemented by the decision-support system (DSS). The DSS activates the autonomous car and the situational awareness is actualized through both short- and long-range imaging devices [ 41 ]. Figure  3 shows the system architecture of the autonomous car, where it shows the areas covered by different components in the car design. Different ranges of situational awareness vary from application to application and it is achieved through multiple components. For instance, prevention of front and rear bumper collisions is done through infrared devices, whereas short-range radars are used for object detection, lane-change cautioning, and traffic view. Equipping autonomous cars with a series of cameras for the surrounding views and LIDAR helps in collision avoidance and emergency brakes. Long-range radars help in cooperative cruise control and long-range traffic view construction. Altogether the aforementioned components are networked and work firmly with each other, as shown in Fig.  2 .

This figure displays the basic architecture of an autonomous car. The architecture consists of various components that work together to enable the car to drive itself. The main components of the architecture are Sensors, Perception and Localization, Planning and Control, Communication and Cloud

For movement of an autonomous car from one point A to B, the car performs a number of important steps in an iterative manner until it reaches its destination such as Perceive and aware about its surrounding environment, Path planning and navigation and Controlled movements on the road [ 42 ].

After perceiving its surrounding environment, it makes path planning along with its destination information and then starts navigating to reach the destination. A number of controlled movements are exercised for a smooth, safe ride on the road with the help of actuators and sensors [ 43 ]. Electronic Control Units (ECUs) control most of the components electronically. ECUs communicate with each other and with the decision support system through the controller area network (CAN) bus inside each car. During a drive, the autonomous car exhibits a number of manoeuvres which needs both software/hardware support, extensive coordination, and data sharing among different components of the car.

Autonomous car benefits

In spite of several complexities and difficulties, the autonomous car provides security, user-friendliness, comfort and value-added services to its customers.

Safety is one of the major concerns and is given top priority in the automotive domain. Every year many millions of people lost their lives in road accidents according to the National Highway Traffic Safety Administration (NHTSA). More than 90% of accidents [ 44 ] happen due to human errors and these errors are caused by various factors such as carelessness, aggressiveness, intoxication and distraction. So, it’s essential to have an alternative and safer mechanism like autonomous driving cars to eliminate human errors and save lives.

Daniel Zelle et al. [ 32 ] developed a security evaluation platform for autonomous driving (SPEAD) to enable researchers to develop, implement and evaluate new security solutions for autonomous vehicles. SPEAD allows us to model realistic autonomous vehicle architectures and test their security mechanisms. There is another aspect of safety that is how to protect the car from thieves. Due to high-end on-board sensors, recognition of the car owner is done by the car itself and it sends the owner an alert in case of any unwanted situation. Unlike ordinary cars, autonomous cars do not require keys, but they operate with finger prints, retina scan and voice recognition software. It is also equipped with finger-print enabled door-lock system to provide more security.

Increase in revenue and business opportunities

Mobility-as-a-Service (MaaS) benefits the customers by saving multiple resources including money, time, and space. Car sharing and car-pooling are two popular applications among customers today. With the advent of autonomous cars, these applications will become more effective by making use of car resources more efficiently. These applications create economic benefits, and also reduce air pollution produced by vehicles in urban areas. This also generates huge business opportunities for the customers. In future, autonomous cars will transform taxi and car rental business. There is no longer requirement of drivers in taxis and rental cars, therefore it will reduce the cost and increase the revenue of owners. Further, it will impact the software industry to develop many smart applications for cars. Hence, autonomous cars will help in increase in revenue by reducing maintenance and labour costs.

User-friendliness and convenience

User-friendliness and convenience are other additional benefits of autonomous cars. In some situations, physically disabled or intoxicated persons can’t drive a car manually. Similarly, for aged, young people without licence, autonomous cars will be a suitable mode of transportation. In such situations, the autonomous car provides a safe and economical way of transportation.

Improving traffic conditions

In autonomous cars, car sharing and car-pooling can be a major advantage, hence it will increment per-vehicle occupancy and decrement the number of vehicles on the road, thereby improving the traffic conditions. Inter-vehicle distance is strictly followed in autonomous cars to improve passenger safety; hence in turn it reduces road traffic. These cars follow traffic rules more precisely, thereby condensing the requirement for more traffic personnel on the road. Fuel efficiency is also improved by automatically choosing the best and shortest path from source to destination [ 45 ]. Hence there is also a decrease in air pollution.

Bo Liu et al. [ 29 ] proposed a redundancy concept for autonomous vehicle functions using microservice architecture in which vehicle-vehicle and cloud-vehicle communication ensure that important vehicle functions keep executing even in the case of a hardware failure. These cars also make use of a fuel-efficient mode through programming by eliminating unnecessary braking situations on the road. One such example is regenerative-braking [ 46 ] in electric autonomous vehicles which uses the kinetic energy of the car to convert it into electrical energy (fuel) until the car stops naturally. Qiao et al. [ 36 ] proposed a hybrid model for traffic assignment and control that combines the strengths of both traditional traffic assignment models and control methods for autonomous vehicles. The model uses a combination of traffic simulation, machine learning, and optimization techniques to assign traffic and control the movement of autonomous vehicles in a given road network. The authors evaluated the performance of the proposed model using real-world traffic data and show that it can improve the overall efficiency and safety of traffic flow in comparison to traditional methods.

Autonomous parking

In metropolitan cities, there are a number of issues related to vehicle parking such as getting parking space in peak hours, increased population and maintaining inter-vehicle distance in parking slots. The advantage of autonomous cars is they can park themselves even in a narrow available parking slot, which is very difficult for a driver to park a car manually. Marvy Badr Monir Mansour et al. [ 47 ] implemented a project to demonstrate autonomous parallel car parking that can be used efficiently in metropolitan cities. Baramee Thunyapoo et al. [ 48 ] proposed a simulation framework for autonomous-car parking for moderate complexity scenarios. Shiva Raj Pokhrel et al. [ 49 ] developed (and evaluated) an experience-driven, secure and privacy-aware framework of parking reservations for automated cars. Since autonomous-cars can communicate with each other, they can reduce traffic congestion by coordinating their movement on the road.

Consumer-centric experience

In autonomous cars passengers and drivers can sit, relax and enjoy the ride. They can also simultaneously do their personal work or utilize the car’s entertainment system [ 50 ]. When the autonomous car is paired with a smart phone then a passenger can command the car to perform some important tasks automatically like picking up children from school, picking up or dropping someone at the airport. These kinds of sophisticated features called “ Summon ” of autonomous cars are introduced by Tesla Company in its high-end models [ 51 ]. Using the “ Summon” application, the car owner can instruct the car to go to the designated parking place even to the basement of the building.

Further research is going on to understand various driving patterns by analysing the behaviour of the drivers by considering drivers’ age, gender, driving experience, personality, emotion, and history of accidents [ 52 ]. A number of features of autonomous cars can be customized based on the human behaviour. For example, overtaking and speed while driving depends on gender, age, emotion etc. Young people drive faster than elders, whereas female drivers drive more cautiously. Similarly, people with families and kids drive more carefully than others. So, during autonomous car customization, one should take these factors into consideration. In future, autonomous cars will lead to attract the developers to build a number of customer-driven applications, where customers can personalize their travel experience according to their choice of speed, in-car entertainment, and level of risk Travelling in autonomous vehicles also increases productivity as the user can focus on work instead of driving the vehicle during transit.

Technology behind autonomous cars

In this section, various technological researches conducted so far in the area of autonomous cars are presented. This section also covers the software components and algorithms used in this technology. Broggi et al. [ 53 ] discussed regarding a number of tests performed on driverless cars in different scenarios such as roads with less to heavy traffic during 1990–2013. The outcomes witnessed changes in the behaviours of the drivers in different environments such as traffic lights, freeway junctions, pedestrian crossing. This car test was named PROUD (Public Road Urban Driverless) and it was one of the biggest achievements in autonomous car technology. PROUD resulted in a few important observations such as precise route maps, efficient learning and perception mechanism. Jo et al. [ 54 , 55 ] concentrated on the generalization of the autonomous car development procedure independent of any specific environment. FlexRay was utilized as a communication protocol and software platform to increase the system performance. Traditionally CAN (Controller Area Network) technology is used to communicate among different ECUs (Electronic Control Unit) in normal cars. But this technology was not suitable for autonomous cars due to its low speed and vulnerability to different attacks [ 56 , 57 ]. FlexRay is a faster and more efficient technology, but it’s expensive. Some technologies used in fully automated cars are Lane Keep Assist (LKA), Park Assist (PA), Automatic Emergency Braking (AEB), Driver Monitoring (DM), Traffic Jam Assist (TJA), Dead Reckoning (DR) [ 38 ]. Most of these rely on sensor data and processing of this sensor data using machine learning/deep learning algorithms.

Computer vision

Autonomous cars require two essential and critical features such as object detection and computer vision. Using object detection techniques, autonomous cars should see the road and detect the object in the road. These two important features along with other modules help to drive safely on the road by avoiding unwanted situations. Janai et al. [ 58 ] conducted an accurate survey on computer vision algorithms such as perception, object detection and tracking, and motion planning used in autonomous cars. But still there are a number of unpredictable scenarios creating challenges for autonomous cars. Consequently, the success of computer vision algorithms will presumably take longer time. In addition to these algorithms, the decision support system has an important role in analysing the behaviour, learning mechanism of drivers. Also, AI has an important part in the prediction and perception aspects of autonomous cars. Shi et al. [ 20 ] studied on computer vision algorithms used for lane detection, object detection and surface detection experimented with GPU (Graphics Processing Unit), FPGA (Field-programmable Gate Array) and ASIC (Application-specific Integrated Circuit) Among these, the performance of ASICs is better than GPUs and FPGAs.

Computer vision is a boundless field of study, where autonomous cars use only object detection and motion estimation algorithms. These cars detect both static and dynamic objects and accurate object detection is always a challenging task due to multiple factors like shadows, identical objects, background lighting, and the size of the object. The detection algorithms accept these multiple factors into consideration and perform object detection with different sensors. Recent Autonomous cars use sensor fusion mechanisms to combine different types of sensors to detect day/night time, living/non-living organisms [ 59 , 60 ]. Sensor-fusion based object detection techniques have more accuracy than traditional object detection methods. Chen et al. [ 61 ] proposed a CNN (Convolutional Neural Network) deep learning approach to detect 3D objects with a single camera, which takes generated date in LIDAR and images as input and predicts 3-D representation of that data. This method first detects objects using dissimilar features and then improves it for the identification of true objects. Then with the help of sensors data, it categorizes the objects into different types. This process of categorization is termed as pixel level semantic segmentation [ 62 ]. To support semantic segmentation, shallow and DL approaches are used for classification and prediction [ 62 , 63 , 64 ]. The approaches used in autonomous cars for object detection, semantic segmentation, and classification give improved accuracy, but have some drawbacks such as algorithm complexity, computational overhead, latency and complex design features. Therefore, compared to the shallow learning approach, DL (deep learning) approaches (such as auto-encoders and CNN) are preferred for object detection and classification due to its automatic feature selection process [ 65 , 66 ]. DL-based approaches are also used in the construction of 3D images from the 2D image which are used for motion planning and actuation process in autonomous cars [ 67 , 68 ].

Deep and machine learning approaches in autonomous cars

AI, ML and DL techniques are essential for autonomous cars as the object behaviour and surrounding environment are unpredictable. ML and DL techniques are used by most computer vision mechanisms. A deep Neural Network (DNN) approach learns features automatically from large data sets. Perception is an important aspect of autonomous cars and analysis of huge data collected from sensors for decision making is done by deep learning techniques. Tian et al. [ 69 ] introduced a DNN model called “ DeepTest ” to assess the behaviour of autonomous cars and found erroneous behaviour multiple times during testing when the car comes under erratic traffic and environmental conditions. These test results were not satisfactory for the current challenges and increased the need for more meticulous measures for the accurate functioning of autonomous cars. Chen et al. [ 70 ] used a novel mechanism to learn automatically the features from an image to calculate affordance in autonomous cars. Chen computed affordance for each driving action, as a substitute for individual driving scenes, it depends on multiple factors like static and dynamic objects on the road, pedestrian and vegetation Duanfeng Chu et al. [ 33 ] proposed a combined trajectory planning and tracking algorithm for autonomous vehicle control. Muhammad Mobaidul Islam et al. [ 71 ] proposed an efficient training strategy for pedestrian detection by occlusion handling by classifying body parts. Mohammed Ikhlayel et al. [ 72 ] made a car prototype for traffic sign detection using convolutional neural network (CNN).

There are two types of perceptions used in autonomous cars namely mediated perceptron and behaviour reflex mode. In mediated perceptron, current surrounding is not known and in behaviour reflex mode, deep neural networks are used to train the system based on human behaviour [ 70 , 73 ]. Furthermore, Chen et al. also proposed another direct perception technique which is based on CNN. This system systematically learns mapping from a captured image to various features related to driving actions. TORCS, an open-source car racing simulator was used to test the car. Laddha et al. [ 74 ] proposed an algorithm to identify features from road images required for autonomous driving. In this algorithm an automated labelled training dataset was taken to make the process more scalable. It takes multiple sensor inputs which are mounted on vehicles including localization and camera sensors. This algorithm effectively utilized CNN to detect obstacles along the way with a moderately good accuracy. Dairi et al. [ 74 ] introduced another DL technique to identify obstacles on the road based on using a hybrid deep autoencoder and stereovision. More than 98% of accuracy was shown on different datasets by this method and it outperformed the DBN (Deep Belief Network) and SDA (Stacked Auto-encoders). Tam et al. [ 35 ] proposed a probability-based artificial potential field method for autonomous vehicles to avoid uncertain obstacles. The authors argued that this method is more effective in avoiding uncertain obstacles than traditional methods, and that it can improve the safety of autonomous vehicles.

Qizwini et al. [ 73 ] made a system and trained with 5 affordance parameters and tested by simulating with some realistic assumptions. XU et al. [ 75 ] used a Long Short-Term Memory Fully Convolutional Network (LSTM-FCN) by training the multi-modal driving-behaviours to predict future ego-motion problem in autonomous driving. The LSTM-FCN model was trained by a large video dataset of vehicular actions by the authors [ 76 ]. The author addressed the problems of traditional end-to-end learning by using a very large crowd-sourced dataset and the learning results were promising. Similar to this model, a number of popular DL architectures such as VGG-16, Google LeNet, AlexNet and ResNet are used accurately in semantic segmentation and scene understanding in autonomous cars [ 77 ]. Among these models, AlexNet achieved an accuracy of 84.6% and VGG achieved an accuracy of 92.7%, Google LeNet 93.3% and ResNet with 96.4% respectively. So, DL architectures played important roles in the multiple aspects of autonomous cars.

Sensors, communications, and control

Computing unit implement all the logic and it is the heart of an autonomous car. Sensors and actuators are highly needed in autonomous car systems. Basically, both known and unknown situations and environments are dealt with by autonomous cars and it needs ML, DL, AI techniques. These ML/DL techniques are data-intensive and these data are collected using various sensors placed within the car. Therefore, it follows a series of procedures such as data acquisition, data processing, communication and controlling among different modules inside the car and its surroundings. In addition to that it has to take autonomous decisions based on the circumstances and this feature requires a lot of interaction data with neighbours, infrastructure and the Internet. Communication among different modules and the environment is a vital function in autonomous cars and this data helps in sensor data analytics.

Autonomous cars deal with a huge number of sensors which generates a huge volume of data and these data are processed to get maximum utilization of it. Sensor Fusion is one of the commonly used techniques to gather data from multiple sensors intelligently to assist in the decision support systems. A number of algorithms have been proposed to deal with heterogeneous data used in autonomous cars. Oliveria et al. [ 78 ] proposed a more accurate technique to visualize a scene from 3D data gathered from sensors by using large scale polygonal primitives. Also, a visual scene may change constantly when obstacles come on the road, so there is a need for a stable mechanism to deal with unanticipated environments. So, the reconstructed scene should be calibrated continuously with the current scene to increase the efficiency as always, the new data from sensors is processed. Polygonal primitives-based scene reconstruction algorithms are incremental in nature and detection of polygon and reconstruction of new scene is performed using old data. Therefore, time required for detecting these polygons increases with an increase in the number of polygons, hence there is an increase in efficiency.

Identifying the road is one of the important aspects in autonomous cars and it is done using different sensors, camera and LIDAR (Fig. 4 ). Xiao et al. [ 79 ] applied a Hybrid Conditional Random Field (HCRF) to overcome the disadvantages of LIDAR and camera sensors. The proposed technique used a multi-modal approach and applied a binary labelling mechanism for separating road and background. This approach showed a maximum degree of accuracy then the existing point-wise CRF. Jo et al. [ 54 , 55 ] used a number of existing functionalities along with sensor fusion algorithms that deal with sensors used inside the car, communication systems and also stressed upon communication of autonomous cars with the outside world by implementing different algorithms and schemes to establish communication with other entities. There exists efficient coupling between connected vehicles and autonomous cars as they are orthogonal to each other and reinforce towards making of intelligent transportation system. Vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies are popularly used to exchange their mobility data in platooning and cooperative traffic information system applications. Hobert et al. [ 80 ] worked on applications of cooperative autonomous cars such as convoy management, intersection management, low delay, cooperative sensing and enhanced reliability. It also helps in sharing the information about surrounding environment to operate more efficiently, timely and safely.

Source – Velodyne. This image shows a 3D map of the surroundings of an autonomous car, constructed using lidar sensor data. The map illustrates the precision and accuracy of the lidar sensor in capturing the geometry of the environment, including buildings, roads, and other obstacles. The use of lidar sensors in autonomous cars is crucial for creating a detailed and accurate map of the car's surroundings, which is then used for navigation and localization

Peng et al. [ 81 ] scrutinized the presentation of IEEE 802.11p protocol and considered inter-platoon communication for different platoons in terms of network performance, delay, packet loss and other parameters. Platoon management is an important task in platoon communication, but it is LTE-driven to reduce the communication delay in autonomous cars. In recent years, transmitters and receivers are fixed in the front and back lights of the cars which uses visible light communication (VLC) technology, but it is in its initial stage and structured channel modelling is mandatory to meet the requirements of autonomous cars. VLC technology is also affected by the massive amount of data and network bandwidth. So numerous researchers are working on finding new techniques to reach out the demands of bandwidth requirement. Chang et al. [ 82 ] experimented on cooperative motion planning to communicate with pedestrians called “Eyes” on car, which means how the autonomous car makes an eye contact with the walker and evaluate the intention of the walker while crossing the road. The author tested with the real-world users and obtained the results of 86.6% in deciding intention of the users while crossing the road. It is observed that the real users made faster decision around 0.287 seconds then no eyes on the car.

Sensors used in an autonomous- car depends on its autonomy level as per the standards of SAE (table –2). There are a total of 6 levels of autonomy ranging from 0-5. Level 0 (Driver only) has no automation whereas level 5 refers to full autonomy in which the car performs all the Dynamic Driving Tasks (DDT) and also achieves the minimum risk condition (DDT fallback) [ 38 ]. A wide range of sensors are used until level 3 of autonomy, from level 4 sensors remain the same but the algorithms and processing of sensor fusion data achieve more autonomy. The U.S. Department of Transportation's National Highway Traffic Safety Administration (NHTSA) has adopted the standards set by the Society of Automotive Engineers (SAE) in the September of 2016.

Autonomous vehicle control (AVC) module is an important feature in autonomous cars that controls the behaviour and environments in various situations by controlling the hardware level component. AVC module is responsible for trajectory during motion by handling both predicted and unforeseen circumstances in autonomous cars. This module is also used in steering wheels to calculate the steering angle for the next action based on the control algorithm. This module is also responsible for speed control, distance travelled and emergency brakes. Apart from this module, the predictive control module is also used in autonomous cars to optimize the predicted motion for inter-vehicular communications. Immense amount of real-time data is required to communicate with neighbours and surroundings to stabilize the operations of autonomous cars. Autonomous cars need a powerful control mechanism to avoid transmission delays and communication errors that arise due to wireless communication. Zeng et al. [ 83 ] suggested an integrated control mechanism, where the communication delays were analysed to find the stability in autonomous platoons. The authors suggested to use the adaptive control mechanism to improve the reliability of the communication mechanism. The traditional control system is quite different then the control mechanism used in autonomous cars. The central control mechanism of autonomous cars is context oriented and uses adaptive control system to act immediately as the information of the context is highly required. Liu et al. [ 84 ] suggested a joint control communication mechanism to understand different road situations in autonomous cars.

Autonomous decision making

Autonomous decision making is a complex task in autonomous cars and certain cars are known as “ego-vehicles” which focus only on their local surroundings, current speed, direction and destination. But the communication mechanism must be strong enough in autonomous cars, so the concepts of “ego-vehicles” are fading away. So, there is a need of different prediction algorithms which gives predictions of high probability. And the final decision is a combined decision by taking the inputs from sensory data and other modules. But there is a number of other challenges related to environment, noise and limitations in the sensor data, mysterious state of neighbours. Therefore, autonomous cars should have accurate information about the neighbours in order to make predictions with high probability. But sometimes the neighbour information is neither available in public nor shared and this restriction causes serious problems for doing predictions and taking decisions accurately. Mockery of human behaviour in autonomous cars is very difficult, therefore the decision-making process is challenging. Numerous factors affect the decision-making process in autonomous cars such as behaviour of the car, prediction and perception, information about the neighbours, data processing done by sensors, calibration of equipment’s A number of present decision-making mechanisms are based on machine/deep learning, AI (Artificial Intelligence), Markov decision process. Hubmann et al. [ 85 ] highlighted the decision-making process for uncertain prediction of the surrounding vehicles from the sensor data. Many such AI based decision making approaches have been discussed by Claussmann et al. [ 86 ]. Several external factors impact the decision-making process in autonomous cars and these issues are addressed in a comprehensive way.

Issues such as ego-motion and inter-vehicle communication play a key role in crowd sensing and perception of neighbour’s behaviour. For commercializing autonomous cars in future, a major role will be played by connected car technology and it is seen today in many high-end cars [ 87 ]. The cloud infrastructure connected to the autonomous-car can also provide more computational resources for the car to execute its software processing functions, ensuring the car functions even in case of a hardware failure. Cloud infrastructure can also provide many new applications including entertainment [ 88 ]. But few issues arise while using the cloud services such as communication delay between cloud infrastructure and autonomous car. Any kind of delay can’t be tolerated by the decision-making module of autonomous cars due to cloud infrastructure, hence mostly decision-making is done locally in real-time environments. Similarly, fog computing techniques are also implemented which gives low delays in providing real-time services [ 89 ].

Testing of autonomous cars in real-time

In today’s scenario, numerous tests of autonomous cars have been carried out to evaluate its performance. Campbell et al. [ 11 ] took part in the DARPA grand challenge (DGC) and conducted 3 rounds of test for autonomous cars and documented the results from those tests. Campbell tried to incorporate the autonomous car mechanism in a regular car and tested it without any human intervention. In DGC challenge a route network definition file (RNDF) was given for self-driving using a road map to follow [ 90 ]. This challenge helped Campbell to understand the problems and issues that need to be addressed and how to commercialize the vehicle. Endsley et al. [ 91 ] considered Tesla autonomous car Model S for a period of 6 months and analysed different issues related to situation awareness, response to unanticipated circumstances on the road He also studied a number of other parameters related to consumer behaviour, trust, complexity, interface design and feedback from the customers. Broggi et al. [ 92 ] invented a BRAiVE (Brain Drive) system to conduct tests on autonomous car at Intelligent Systems and Artificial Vision Lab. Broggi’s test accumulated a huge amount of test data of real-time driving that is now used for upgrading the existing autonomous cars. Further Broggi tested autonomous cars on streets and he named his project as PROUD. Jo et al. [ 54 , 55 ] designed an autonomous car and participated in South Korea in 2012. Jo developed the architecture of the car and experimented in various environments.

AUTOSTAR [ 93 ] is one of the open standard architectures popularly used in many autonomous cars, but it has its limitations due to high cost and high complexity to implement. Later AUTOSTAR-lite came into market as a replacement to AUTOSTAR. Jo tried a distributed architecture instead of a centralized architecture to reduce the complexity and to group the functional components. This architecture also increased the efficiency and performance through parallel computations. A number of automotive industries along with academia experimented and “ Drive Me ” is one of the special projects done by Volvo in autonomous cars. This project in Sweden collected information from 100 consumers about their daily routines, driving behaviour, their preferences and other important aspects of driving. This consumer data helped researchers to improve the commercial autonomous cars. Blockchain technology can also be used for shared training of autonomous-cars where cars can share their experience over a blockchain network [ 27 ]. Blockchain technology can also be used to charge electric vehicles with no human intervention as shown in [ 28 ] with the help of a public ledger recording each transaction.

Design and implementation issues

A number of factors such as safety, robustness, hardware / software designs, customer behaviour will decide the future of autonomous cars. And also, these cars need to be designed with utmost precision, safety and reliability features [ 94 ]. The major components of autonomous cars are LIDAR, sensors, radar, positioning systems and various optimized software’s. A number of issues have direct impact on the autonomous car industry such as cost, maps, software complexity and simulation. First, the software/hardware cost is a major barrier in manufacturing autonomous cars. LIDAR is one of the expensive products in the car. Second, the maps used in autonomous cars contain a number of road details and it differs from the normal maps generated by the GPS systems. Memory requirements and processing power are enormous to store all the road details. The log files generated from these cars also requires memory to store and it contains detailed information about localization and mapping. Third, the software program of the car decides the various operations of the car such as move, stop, and lane change and overtake. So, a highly accurate and reliable software program is required which takes inputs from different sensors [ 95 ]. The huge data acquired from the sensors, environment and its surroundings create a real challenge for the autonomous car [ 96 ]. The algorithms processing this huge amount of sensor data should be efficient as well. An example stated in [ 38 ] is the use of stereo-camera over normal camera. A stereo-camera can take 3D images that map the environment accurately. Even though the data generated by stereo-camera is huge, it takes less time to process this data then conventional image processing.

Autonomous cars still need to be tested in adverse conditions such as mist, rainstorms, night-time and densely populated cities. The software used in autonomous cars also records the driving patterns and behaviours such as obstacle management, pedestrians crossing, and overtaking. Simulation technology is becoming the self-driving technology for autonomous car designers and Google is one among the top leading company in the market along with hardware giants like Nvidia announcing the launch of support hardware for autonomous cars [ 8 ]. To check the software reliability and safety, large-scale simulation is necessary. After simulation it is tested on real hardware with all built-in functionalities. Simulation tools developed specifically for the requirement of autonomous vehicles are utilized to simulate diverse aspects such as path planning and testing, mobility dynamics, fuel economy in urban scenarios [ 97 ]. Sajjad et al. [ 49 ] proposed an efficient and scalable simulation model for autonomous vehicles with economical hardware. A reduced reality gap for testing autonomous vehicles has been proposed by Patel et al. [ 98 ]. The simulation tool designed by Buzdugan et al. [ 34 ] provides a realistic environment for students to learn about the behaviour of autonomous vehicles and how they interact with their surroundings. The authors also evaluated the effectiveness of the tool in teaching the behaviour of autonomous vehicles. The study can help in the development of new methods of teaching autonomous vehicles and to improve the understanding of the behaviour of autonomous vehicles. When such simulation and testing models are combined, the industry can use various models for training autonomous cars both efficiently accurately.

Challenges for autonomous car deployment

A number of challenges still exist and it must be resolved by various stakeholders, manufacturers, developers, academicians, policy makers and designers [ 99 ]. These challenges can be categorized into technical, non-technical, social and policy related. A number of technical challenges arise during car deployment such as validation and testing, hardware / software resources, quality, safety, privacy and security. Validation and testing are time consuming process and it varies from model to model and it also depends on the degree of sophistication. Different of testing techniques are used such as simple bug fixing to quality testing. A number of safety and mission-critical testing are performed to fine tune the performance of autonomous cars and to check whether the rigorous necessities are met or not. Koopman et al. conducted widespread overview of the validation and testing trials for autonomous cars when deployed at scale [ 100 ]. The author applied ISO 26262 development V process which maps each design of autonomous vehicles to an appropriate testing method while focusing more on specific advanced challenges and divided the work into 3 phases namely phased deployment, monitor architecture, and fault inoculation. This testing framework is popularly used in in automotive industry for validation and testing and it checks the specific requirements of vehicles minutely and test all expected functionalities. Autonomous cars have specific set of complex requirements, and it is different from traditional validation and testing process. The author also discussed the challenges in the testing of autonomous vehicles as vehicle level testing is not thorough enough to ensure systems with ultimate dependencies which, in this case, are the various technologies employed in autonomous cars discussed in the prior sections. Younang et al. [ 37 ] examined how the concerns of users and government policies regarding autonomous vehicles compare and contrast. They analysed data from surveys and government policy documents to understand user's concerns and government policies on autonomous vehicles (Fig. 5 ).

His image is a flowchart that classifies the challenges faced in the development of autonomous cars into four categories: Technical, Non-technical, Social, and Policy. The chart illustrates the complex nature of autonomous car development and the various factors that must be considered. The Technical challenges include issues related to sensor technology, machine learning, and artificial intelligence. This flowchart is a useful tool to understand and address the different challenges that autonomous car development faces, and it helps to identify the areas that need further research and development

Safety and reliability are the second core aspects to be addressed in autonomous cars. The degree of reliability can be identified to some extent by statistical analysis by using a huge database of distance travelled by the car. Safety is one of the major concerns for autonomous cars, so a number of new methods are required for measuring the reliability parameter. Similarly, legislation is another prime challenge in autonomous cars. Safety is an important and interdisciplinary issue, so a lot amount time and money are spent on safety measures of the car [ 101 ].

Software quality is third aspect in autonomous cars as the car is run by complex and sophisticated software. Validation and testing of these software’s play a major role due to mission-critical and complex software system used by these cars. These software’s responds to a number of unanticipated situations and in all situations, it must be fail-safe.

Computational resource requirement is the fourth aspect in autonomous cars due to a number of high-resolution cameras used for monitoring various operations of the car. Apart from that a number of sensors are used for object recognition such as LIDAR. So autonomous cars require GPU processors with FPGA (field-programmable gate array) and SoC (system on a chip) to meet the functional and operational requirements of the car. Optionally these can be connected to a cloud infrastructure which provides the necessary computational power as a service. But in case of a connection timeout, it can cause problems. A number of sophisticated algorithms are used for data processing and provide an efficient and reliable system.

Security requirement is the fifth and one of the important aspects in autonomous cars. The data shared between various components and among other vehicles and infrastructure must be kept safe and inaccessible to unauthorized people. All type of communication information must be safe from hackers, so a number of cryptographic techniques are used to provide internal security and privacy.

Automotive industry is fast changing and a number of auto maker’s companies are now making autonomous cars. A number of new business opportunities are there for car makers along with few challenges such as safety for car and passengers. In this paper, we discussed the current solutions regarding design, operation issues, and forthcoming challenges. A number of major benefits and technical challenges are thoroughly discussed. A number of research areas to be focussed for autonomous cars have been outlined such as computer vision using deep learning, decision-making using machine learning, navigation, planning, control, perception, blockchain technology, cloud infrastructure integration, A number of real-life tests conducted so far are also outlined on autonomous cars. This work has focussed on the recent technologies behind autonomous cars like AI decision-making-based path planning, layered architectures, cyber threat protection mechanism, use of blockchain for transaction management and shared training of autonomous vehicles and also provides a valuable reference to the field of autonomous cars. This paper focussed on a number of technical and non-technical issues that exist in the design and implementation, including the analysis of the consumer and driver preferences, of autonomous cars. A number of issues were reviewed regarding object tracking and detection, data acquisition from sensors, safety and reliability, decision support, security, simulation models and privacy. Even though notable results are achieved in the research and development of autonomous cars and now that we have entered the commercialization phase, but still this field still has several areas of research like user experience and precautions and hardware for efficient decision making on the vehicle. The future of autonomous cars is promising, but further research and development is needed to overcome the remaining obstacles like autonomous vehicle testing for component compatibility, accurate simulations, large scale training and to fully realize their potential. The paper concludes with a call for further research in the field of autonomous cars to address the remaining technical and non-technical issues and to maximize the potential and adoption of autonomous cars in the future.

Availability of data and materials

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Lafrance A. Our grandmother’s driverless car. https://doi.org/https://www.theatlantic.com/technology/archive/2016/06/beep-beep/489029/ ; 2016.

Kanade T, Thorpe C, Whittaker W. Autonomous land vehicle project at cmu. In: Proceedings of the 1986 ACM Fourteenth Annual Conference on Computer Science, CSC ’86, (New York, NY, USA), pp. 71–80, ACM, 1986.

Schmidhuber J. Robot car history. https://doi.org/http://people.idsia.ch/~juergen/robotcars.html .

Guerrero-ibanez JA, Zeadally S, Contreras-Castillo J. Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wirel Commun. 2015;22:122–8.

Article   Google Scholar  

Contreras-Castillo J, Zeadally S, IbÃaÃsez JAG. A seven layered model architecture for internet of vehicles. J Inf Telecommun. 2017;1(1):4–22.

Kenney JB. Dedicated short-range communications (dsrc) standards in the United States. Proc IEEE. 2011;99:1162–82.

Zeadally S, Hunt R, Chen Y-S, Irwin A, Hassan A. Vehicular ad-hoc networks (vanets): status, results, and challenges. Telecommun Syst. 2012;50:217–41.

Lopez N. Nvidia announces a ’supercomputer’ gpu and deep learning platform for self-driving cars.” https://doi.org/https://thenextweb.com/author/napierlopez/#.tnw_G6F0jhzi , 2016.

P. Group, “Two psa group autonomous cars drive from Paris to Amsterdam in "eyes off" mode.” http://www.businesswire.com/news/home/20160414006039/en/PSA-Group-Autonomous-Cars-Drive-Paris-Amsterdam , 2016.

Mehar S, Zeadally S, Ralmy G, Senouci SM. Sustainable transportation management system for a fleet of electric vehicles. IEEE Trans Intell Transport Syst. 2015;16:1401–14.

Campbell M, Egerstedt M, How JP, Murray RM. Autonomous driving in urban environments: approaches, lessons and challenges. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 2010;368(1928):4649–72.

Google Scholar  

Okuda R, Kajiwara Y, Terashima K. A survey of technical trend of adas and autonomous driving. In: Proceedings of Technical Program - 2014 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA), pp. 1–4, 2014.

Fagnant DJ, Kockelman K. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice. 2015;77:167–81.

Bagloee SA, Tavana M, Asadi M, Oliver T. Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. Journal of Modern Transportation. 2016;24:284–303.

Paden B, Änãap M, Yong SZ, Yershov D, Frazzoli E. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans Intell Vehicles. 2016;1:33–55.

Abraham H, Lee C, Brady S, Fitzgerald C, Mehler B, Reimer B, Coughlin JF. White paper: Autonomous vehicles, trust, and driving alternatives: A survey of consumer preferences. Tech. Rep. 2016–6, MIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA; 2016.

Joy J, Gerla M. Internet of vehicles and autonomous connected car - privacy and security issues. In: 2017 26th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9, 2017.

Bresson G, Alsayed Z, Yu L, Glaser S. Simultaneous localization and mapping: A survey of current trends in autonomous driving. IEEE Transactions on Intelligent Vehicles. 2017;2:194–220.

Parkinson S, Ward P, Wilson K, Miller J. Cyber threats facing autonomous and connected vehicles: Future challenges. IEEE Trans Intell Transport Syst. 2017;99:1–18.

Shi W, Alawieh MB, Li X, Yu H. Algorithm and hardware implementation for visual perception system in autonomous vehicle: A survey. Integr VLSI J. 2017;59:148–56.

Hulse LM, Xie H, Galea ER. Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age. Saf Sci. 2018;102:1–13.

Gupta AS, Sharma S. Analysis of Public Perception of Autonomous Vehicles Based on Unlabelled Data from Twitter. In: Tuba M, Akashe S, Joshi A, editors. ICT Infrastructure and Computing Lecture Notes in Networks and Systems, vol. 520. Singapore: Springer; 2023.

Madhav, A.V.S., Tyagi, A.K. (2023). Explainable Artificial Intelligence (XAI): Connecting Artificial Decision-Making and Human Trust in Autonomous Vehicles. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P., Ganzha, M. (eds). Proceedings of Third International Conference on Computing, Communications, and Cyber-Security Lecture Notes in Networks and Systems. Springer, Singapore.

Bairy A. (2022). Modeling Explanations in Autonomous Vehicles. In: ter Beek, M.H., Monahan, R. (eds) Integrated Formal Methods. IFM 2022. Lecture Notes in Computer Science, vol 13274. Springer, Cham. https://doi.org/10.1007/978-3-031-07727-2_20

Mazri T, Tibari S. The Proposed Self-defense Mechanism Against Security Attacks for Autonomous Vehicles. In: Ben Ahmed M, Boudhir AA, Karaș İR, Jain V, Mellouli S, editors. Innovations in Smart Cities Applications Volume 5 SCA 2021 Lecture Notes in Networks and Systems. Cham: Springer; 2022.

Li Q, Wang Z, Wang W, Yuan Q. Understanding Driver Preferences for Secondary Tasks in Highly Autonomous Vehicles. In: Long S, Dhillon BS, editors. Man-Machine-Environment System Engineering. MMESE 2022. Lecture Notes in Electrical Engineering, vol. 941. Singapore: Springer; 2023.

Gandhi GM, Salvi. Artificial Intelligence Integrated Blockchain For Training Autonomous Cars. In: 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 2019, pp. 157–161, https://doi.org/10.1109/ICONSTEM.2019.8918795 .

Aguilar Cisneros JR, Fernández-y-Fernández CA, Juárez Vázquez J. Blockchain Software System Proposal Applied to Electric Self-driving Cars Charging Stations: A TSP Academic Project. In: 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT), 2020, pp. 174–179, https://doi.org/10.1109/CONISOFT50191.2020.00033 .

Liu B, Betancourt VP, Zhu Y, Becker J. Towards an On-Demand Redundancy Concept for Autonomous Vehicle Functions using Microservice Architecture. IEEE International Symposium on Systems Engineering (ISSE). 2020;2020:1–5. https://doi.org/10.1109/ISSE49799.2020.9272016 .

Qiu H, Ayara A, Glimm B. A Knowledge Architecture Layer for Map Data in Autonomous Vehicles. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020, pp. 1–6, https://doi.org/10.1109/ITSC45102.2020.9294712 .

Coicheci S, Filip I. Self-driving vehicles: current status of development and technical challenges to overcome. In: 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2020, pp. 000255–000260, https://doi.org/10.1109/SACI49304.2020.9118809 .

Zelle D, Rieke R, Plappert C, Krauß C, Levshun D, Chechulin A. SEPAD – Security Evaluation Platform for Autonomous Driving. In: 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2020, pp. 413–420, https://doi.org/10.1109/PDP50117.2020.00070 .

Li H, Wu C, Chu D, Lu L, Cheng K. Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles. IEEE Access. 2021;9:9453–63. https://doi.org/10.1109/ACCESS.2021.3050005 .

Buzdugan ID, Roșu IA, Antonya C. Development of a Simulator Tool for Teaching the Autonomous Vehicles Behavior. In: Auer ME, El-Seoud SA, Karam OH (eds) Artificial Intelligence and Online Engineering. REV 2022. Lecture Notes in Networks and Systems. Springer, Cham. 2013.

Tam PM, Anh HPH. A Probability-Based Artificial Potential Field for Autonomous Vehicles in Avoiding Uncertain Obstacles. In: Huang YP, Wang WJ, Quoc HA, Le HG, Quach HN, editors. Computational Intelligence Methods for Green Technology and Sustainable Development GTSD 2022 Lecture Notes in Networks and Systems. Cham: Springer; 2023.

Qiao J, de Jonge D, Zhang D, Sierra C, Simoff S. A Hybrid Model of Traffic Assignment and Control for Autonomous Vehicles. In: Aydoğan R, Criado N, Lang J, Sanchez-Anguix V, Serramia M, editors. PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science. Cham: Springer; 2023.

Wakam Younang VC, Yang J, Jacuinde LG, Sen A. A Comparative Analysis of User’s Concerns and Government Policies on Autonomous Vehicles. In: Tekinerdogan B, Wang Y, Zhang LJ, editors. Internet of Things –ICIOT 2022 Lecture Notes in Computer Science. Cham: Springer; 2023.

Zanchin BC, Adamshuk R, Santos MM, Collazos KS. On the instrumentation and classification of autonomous cars. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 2631–2636, https://doi.org/10.1109/SMC.2017.8123022 .

Preliminary statement of policy concerning automated vehicles", NHTSA. https://doi.org/https://www.transportation.gov/briefing-room/us-department-transportation-releases-policy-automated-vehicle-development .

Domínguez R, Onieva E, Alonso J, Villagra J, González C. LIDAR based perception solution for autonomous vehicles. In: Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on, 2011, p. 790–795.

Hasch, E.Topak, R. Schnabel, T. Zwick, R.Weigel, and C.Waldschmidt, “Millimeter-wave technology for automotive radar sensors in the 77 GHz frequency band,” IEEE Transactions on Microwave Theory and Techniques, vol. 60, no. 3, pp. 845–860, 2012

Fu M, Song W, Yi Y, Wang M. Path planning and decision making for autonomous vehicle in urban environment. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 686–692, 2015.

Lee M-H, Chen Y-J, Li THS. Sensor fusion design for navigation and control of an autonomous vehicle. In: Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, 2011, p. 2209–2214.

“Critical reasons for crashes investigated in the national motor vehicle crash causation survey.” https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115 , 2015. [Online].

Anderson MJ, Nidhi K, Karlyn DS, Sorensen P, Samaras C, Oluwatola OA. Autonomous vehicle technology: A guide for policymakers. In: RAND Corporation; 2016.

Heydari S, Fajri P, Sabzehgar R, Asrari A. Optimal Blending of Regenerative and Friction Braking at Low Speeds for Maximizing Energy Extraction in Electric Vehicles. IEEE Energy Conversion Congress and Exposition (ECCE). 2019;2019:6815–9. https://doi.org/10.1109/ECCE.2019.8913117 .

Mansour MBM, Said A, Ahmed NE, Sallam S. Autonomous Parallel Car Parking. In: 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 2020, pp. 392-397.

Pokhrel SR, Qu Y, Nepal S, Singh S. Privacy-Aware Autonomous Valet Parking: Towards Experience Driven Approach. IEEE Trans Intell Transp Syst. 2021;22(8):5352–63. https://doi.org/10.1109/TITS.2020.3006337 .

Sajjad M, et al. An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware. IEEE Trans Intell Transp Syst. 2021;22(3):1718–32. https://doi.org/10.1109/TITS.2020.2980855 .

Kim HS, Yoon HS, Kim MJ, Ji YG. Deriving future user experiences in autonomous vehicle. In: Adjunct Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI ’15, (New York, NY, USA), pp. 112–117, ACM, 2015.

Pierce D. Tesla summon hints at how the world of self-driving cars will work. https://doi.org/https://www.wired.com/2016/01/tesla-summon/ , 2016.

Li L, Liu Y, Wang J, Deng W, Oh H. Human dynamics based driver model for autonomous car. IET Intel Transport Syst. 2016;10(8):545–54.

Broggi A, Cerri P, Debattisti S, Laghi MC, Medici P, Molinari D, Panciroli M, Prioletti A. Proud: Public road urban driverless-car test. IEEE Trans Intell Transp Syst. 2015;16:3508–19.

Jo K, Kim J, Kim D, Jang C, Sunwoo M. Development of autonomous car (part i): Distributed system architecture and development process. IEEE Trans Industr Electron. 2014;61:7131–40.

Jo K, Kim J, Kim D, Jang C, Sunwoo M. Development of autonomous car (part ii): A case study on the implementation of an autonomous driving system based on distributed architecture. IEEE Trans Industr Electron. 2015;62:5119–32.

Woo S, Jo HJ, Lee DH. A practical wireless attack on the connected car and security protocol for in-vehicle can. IEEE Trans Intell Transp Syst. 2015;16:993–1006.

Woo S, Jo HJ, Kim IS, Lee DH. A practical security architecture for in-vehicle can-fd. IEEE Trans Intell Transp Syst. 2016;17:2248–61.

Janai J, Güney F, Behl A, Geiger A. Computer vision for autonomous vehicles: Problems, datasets and state-of-the-art. CoRR, vol. abs/1704.05519, 2017.

Chen X, Kundu K, Zhu Y, Ma H, Fidler S, Urtasun R. 3d object proposals using stereo imagery for accurate object class detection. CoRR, vol. abs/1608.07711, 2016.

Gonzalez A, Vãazquez D, Lãspez AM, Amores J. On-board object detection: Multicue, multimodal, and multiview random forest of local experts. IEEE Trans Cybern. 2017;47:3980–90.

Chen X, Ma H, Wan J, Li B, Xia T. Multi-view 3d object detection network for autonomous driving. CoRR, abs/1611.07759, 2016.

Baek J, Kim J, Kim E. Fast and efficient pedestrian detection via the cascade implementation of an additive kernel support vector machine. IEEE Trans Intell Transp Syst. 2017;18:902–16.

Bilal M. Algorithmic optimisation of histogram intersection kernel support vector machine-based pedestrian detection using low complexity features. IET Comput Vision. 2017;11(5):350–7.

Hattori H, Boddeti VN, Kitani K, Kanade T. Learning scene-specific pedestrian detectors without real data. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3819–3827, 2015.

Sermanet P, Kavukcuoglu K, Chintala S, Lecun Y. Pedestrian detection with unsupervised multi-stage feature learning. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626–3633, 2013.

Xu D, Ouyang W, Ricci E, Wang X, Sebe N. Learning cross-modal deep representations for robust pedestrian detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4236–4244, 2017.

Luo W, Schwing AG, Urtasun R. Efficient deep learning for stereo matching. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5695–5703, 2016.

Mayer N, Ilg E, HÃdusser P, Fischer P, Cremers D, Dosovitskiy A, Brox T. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4040–4048, 2016.

Tian Y, Pei K, Jana S, Ray B. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. CoRR, vol. abs/1708.08559, 2017.

Chen C, Seff A, Kornhauser A, Xiao J. Deepdriving: Learning affordance for direct perception in autonomous driving. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2722–2730, 2015.

Islam MM, Newaz R, Gokaraju B, Karimoddini A. Pedestrian Detection for Autonomous Cars: Occlusion Handling by Classifying Body Parts. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, pp. 1433–1438, https://doi.org/10.1109/SMC42975.2020.9282839 .

Ikhlayel M, Iswara AJ, Kurniawan A, Zaini A, Yuniarno EM. Traffic Sign Detection for Navigation of Autonomous Car Prototype using Convolutional Neural Network. In: 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), 2020, pp. 205–210, https://doi.org/10.1109/CENIM51130.2020.9297973 .

Al-Qizwini M, Barjasteh N, Al-Qassab H, Radha H. Deep learning algorithm for autonomous driving using googlenet. in 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 89–96, 2017.

Laddha A, Kocamaz MK, Navarro-Serment LE, Hebert M. Map-supervised Road detection. In: 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 118–123, 2016.

Xu H, Gao Y, Yu F, Darrell T. End-to-end learning of driving models from large-scale video datasets. CoRR, vol. abs/1612.01079, 2016.

Daftry S, Bagnell JA, Hebert M. Learning transferable policies for monocular reactive (mav) control. CoRR, vol. abs/1608.00627, 2016.

Milioto, Andres & Behley, Jens & Mccool, Chris & Stachniss, Cyrill. (2020). LiDAR Panoptic Segmentation for Autonomous Driving. https://doi.org/10.1109/IROS45743.2020.9340837 .

Oliveira M, Santos V, Sappa AD, Dias P, Moreira AP. Incremental scenario representations for autonomous driving using geometric polygonal primitives. Robot Auton Syst. 2016;83:312–25.

Xiao L, Wang R, Dai B, Fang Y, Liu D, Wu T. Hybrid conditional random field based camera-lidar fusion for road detection. Inf Sci. 2018;432:543–58.

Article   MathSciNet   Google Scholar  

Hobert L, Festag A, Llatser I, Altomare L, Visintainer F, Kovacs A. Enhancements of v2x communication in support of cooperative autonomous driving. IEEE Commun Mag. 2015;53:64–70.

Peng H, Li D, Abboud K, Zhou H, Zhao H, Zhuang W, Shen X. Performance analysis of ieee 802.11p dcf for multiplatooning communications with autonomous vehicles. IEEE Trans Veh Technol. 2017;66:2485–98.

Chang C-M, Toda K, Sakamoto D, Igarashi T. Eyes on a car: An interface design for communication between an autonomous car and a pedestrian. In: Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI ’17, (New York, NY, USA), pp. 65–73, ACM, 2017.

Zeng T, Semiari O, Saad W, Bennis M. Joint communication and control for wireless autonomous vehicular platoon systems. CoRR, vol. abs/1804.05290, 2018.

Liu J, Zhang S, Sun W, Shi Y. In-vehicle network attacks and countermeasures: Challenges and future directions. IEEE Network. 2017;31(5):50–8.

Hubmann C, Becker M, Althoff D, Lenz D, Stiller C. Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles. In: 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1671–1678, 2017.

Claussmann L, Revilloud M, Glaser S, Gruyer D. A study on al-based approaches for high-level decision making in highway autonomous driving. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3671–3676, 2017.

Ilas C. Electronic sensing technologies for autonomous ground vehicles: A review. In: Advanced Topics in Electrical Engineering (ATEE), 2013 8th International Symposium on, 2013, p. 1–6.

Hussain R, Rezaeifar Z, Oh H. A paradigm shift from vehicular ad hoc networks to vanet-based clouds. Wireless Pers Commun. 2015;83(2):1131–58.

Aazam M, Zeadally S, Harras K. Fog computing A ¸S architecture, evaluation, and future research directions. IEEE Communications Magaz (in press), 2018.

Knight W. The Future of self-driving cars. MIT Technology Review, Massachusetts Institute of Technology, 2013.

Endsley MR. Autonomous driving systems: A preliminary naturalistic study of the tesla models. Journal of Cognitive Engineering and Decision Making. 2017;11(3):225–38.

Broggi A, Buzzoni M, Debattisti S, Grisleri P, Laghi MC, Medici P, Versari P. Extensive tests of autonomous driving technologies. IEEE Trans Intell Transp Syst. 2013;14:1403–15.

Bunzel S. Autosar – the standardized software architecture. Informatik-Spektrum. 2011;34:79–83.

Chakraborty S, Laware H, Castanon D, Zekavat SR. High precision localization for autonomous vehicles via multiple sensors, data fusion and novel wireless technologies. In: Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual, 2016, p. 1–9.

Li Q, Chen L, Li M, Shaw S-L, Nuchter A. A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios”. IEEE Trans Vehic Technol. 2014;63(2):540–55.

Google, “Waymo: On the road.” https://waymo.com/ontheroad/ , 2017.

Figueiredo MC, Rossetti RJF, Braga RAM, Reis LP. An approach to simulate autonomous vehicles in urban traffic scenarios. In: 2009 12th International IEEE Conference on Intelligent Transportation Systems, pp. 1–6, 2009.

Kaival Kamleshkumar P. A Simulation Environment with Reduced Reality Gap for Testing Autonomous Vehicles. 2020. Electronic Theses and Dissertations. 8305.

The Road Ahead: The Emerging Policy Debates for IT in Vehicles", Information Technology & Innovation Foundation, https://doi.org/http://www2.itif.org/2013-road-ahead.pdf .

Koopman P, Wagner M. Challenges in autonomous vehicle testing and validation. SAE Int. J. Trans. Safety, vol. 4, pp. 15–24, 2016.

de Lemos R, et al., Software Engineering for Self-Adaptive Systems: A Second Research Roadmap, pp. 1–32. Berlin: Springer, 2013.

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Acknowledgements

We would like to acknowledge Prof. G Chandrasekhar, Prof. E Krishna Rao Patro and Mr. Habeeb for their assistance in editing and proofreading the paper.

This research received no specific grant from any funding agency. There is no funding information available for this research work.

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Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India

Department of Mechanical Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India

CH. V. K. N. S. N. Moorthy

Engineering and Architecture Faculty, Nisantasi University, Istanbul, Turkey

Department of Computer Science and Engineering, G. Narayanamma Institute of Technology & Science (Autonomous), Hyderabad, Telangana, India

N. Venkateswarulu

VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India

Myneni Madhu Bala

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Contributions

BP has made substantial contributions towards literature survey and compiling the contents of this paper. CHVKNSNM has performed the analysis and manuscript preparation. NV has given significant contributions in writing and arranging the contents in proper order. MMB contributed to data collection and analysis on tools and techniques. All authors read and approved the final manuscript.

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Dr. B Padmaja is currently working as an Associate Professor and Head, CSE (Artificial Intelligence and Machine Learning), Institute of Aeronautical Engineering, Hyderabad, Telangana, India. She has received her B.Tech from North Eastern Regional Institute of Science and Technology (NERIST), Arunachal Pradesh, India in May 2001. She completed her M.Tech from School of IT, JNTUH, and Hyderabad, India in 2010. She was awarded the Ph.D. degree in Computer Science and Engineering in 2021 by JNTUH, Hyderabad. She has vast teaching and research experience of 20 years. She has published more than 20 research papers in various International journals and presented more than 15 papers in various International conferences. She is also a reviewer for 08 International journals. Her current areas of research interest include Machine Learning, Deep Learning, Computer Vision, and Social Network Analysis. She is a life member of ISTE, CSI, IAENG and CSTA.

Prof. Dr. CH V K N S N Moorthy is working as Director R&D, Vasavi College of Engineering, Hyderabad, Telangana, India. He is a multidisciplinary and cross domain researcher having experience in the fields of Computer Science and Mechanical Engineering. He received Master of Technology both in the fields of Computer Science Engineering and Heat Power Refrigeration & Air Conditioning. He received Doctoral degree for research in the field of Thermo-Nano Fluid Heat Transfer from GITAM University, Vishakhapatnam and pursuing his Doctoral degree in the field of Machine Learning too. He has nearly two decades of teaching and research experience with a total research grant of 424.46 K USD from Department of Science and Technology, Ministry of Science and Technology, Government of India for various projects under cross domain research, more than 40 research publications, International Research Collaborations, Awards and Patents to his credit. He is a Chartered Engineer and Fellow Member of Institution of Engineers, India (IEI), a Life Member of Indian Society for Technical Education (ISTE), Member of American Society of Mechanical Engineers (ASME) and Institute of Electrical and Electronics Engineers (IEEE). His thrust areas of research include Cognitive Science, Data Analytics and Data Science, Machine Learning, Artificial Intelligence, Thermo-Nano fluid Heat Transfer, Nanotechnology, Carbon Nano Tubes, Computational Fluid Dynamics.

Mr. N. Venkateswarulu is working as Assistant Professor, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology & Science, Shaikpet, Hyderabad, Telangana, India. He is a research scholar currently persuing Ph.D in KL University, Andhra Pradesh, India. He has received his master’s from Sree Vidyanikethan Engineering College, Tirupathi, Andhra Pradesh, India. He has received his bachelor’s degree from Sreenivasa Institute of Technology and Management Studies, Chittoor, Andra Pradesh, India in June 2003. He has total thirteen years of teaching experience. He has six research publications in international journals out of which one got indexed in SCOPUS and one got published in SCI journal JSIR in February 2023. His thrust areas of research include Computer Algorithms, Data Mining, Data Science, Machine Learning, and Artificial intelligence.

Dr Madhu Bala Myneni is working as a professor and Head of computer science and engineering at Institute of Aeronautical Engineering, Hyderabad. She received her Ph.D in Computer Science and Engineering from JNTUH. She has Twenty-one years of academic and research experience. Her research interests are Data Science frameworks, Image Mining, Text mining, Machine learning, Artificial Intelligence, Deep Learning and Data Analytics. She has published 57 articles in reputed Journals indexed in SCOPUS, SCI etc. She has published 2 patents. She is the Principal Investigator of a DST-funded sustainable smart city development project. And has received various grants from AICTE for organizing Short Term Training Programs; Infrastructure Development; and Faculty Development Programs. And selected a part of AICTE national mission programs such as Student Learning Outcomes Assessment (SLA); Technical Book Writing (TBW). She is a reviewer for Elsevier, Springer, and more indexed journals. She acted as session chair, organizing member, and advisory member for various International Conferences. She delivered various invited talks on Data Modelling, Data Science, and Analytics. She is a Life member of professional bodies like CSI and ISTE, Sr. Member for IEEE, WIE & International association IAENG, ICST, and SDIWC.

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Padmaja, B., Moorthy, C.V.K.N.S.N., Venkateswarulu, N. et al. Exploration of issues, challenges and latest developments in autonomous cars. J Big Data 10 , 61 (2023). https://doi.org/10.1186/s40537-023-00701-y

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Received : 13 January 2022

Accepted : 17 February 2023

Published : 06 May 2023

DOI : https://doi.org/10.1186/s40537-023-00701-y

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