Engineering · Literature

Research papers on Self-driving car safety

Recent and highly-cited academic work on self-driving car safety, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>

   Ekim Yurtsever, Jacob Lambert, Alexander Carballo, et al. · 2020 · IEEE Access · 1,742 citations

   Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a re

2. A survey of deep learning techniques for autonomous driving

   Sorin Grigorescu, Bogdan Trăsnea, Tiberiu Cocias, et al. · 2019 · Journal of Field Robotics · 1,738 citations

   Abstract The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each

3. Planning and Decision-Making for Autonomous Vehicles

   Wilko Schwarting, Javier Alonso–Mora, Daniela Rus · 2018 · Annual Review of Control Robotics and Autonomous Systems · 940 citations

   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, su

4. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning

   Todd Litman · 2015 · Transportation Research Board 94th Annual MeetingTransportation Research Board · 865 citations

   This paper explores the impacts that autonomous (also called self-driving, driverless or robotic) vehicles are likely to have on travel demands and transportation planning. It discusses autonomous vehicle benefits and costs, predicts their likely development and implementation based on experience with previous vehicle technologies, and explores how they will affect planning decisions such as optimal road, parking and public transit supply. The analysis indicates that some benefits, such as independent mobility for affluent non-drivers, may begin in the 2020s or 2030s, but most impacts, including reduced traffic and parking congestion (and therefore road and parking facility supply requiremen

5. Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

   De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al. · 2021 · Sensors · 820 citations

   With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implem

6. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

   Christos Katrakazas, Mohammed Quddus, Wen‐Hua Chen, et al. · 2015 · Transportation Research Part C Emerging Technologies · 787 citations

   Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have be

7. Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age

   Lynn Hulse, Hui Xie, Edwin R. Galea · 2017 · Safety Science · 689 citations

8. Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems

   On-Road Automated Driving (ORAD) Committee · 2014 · 684 citations

   &lt;div class="section abstract"&gt; &lt;div class="htmlview paragraph"&gt;This Information Report provides a taxonomy for motor vehicle automation ranging in level from no automation to &lt;i&gt;full automation&lt;/i&gt;. However, it provides detailed definitions only for the highest three levels of automation provided in the taxonomy (namely, &lt;i&gt;conditional&lt;/i&gt;, &lt;i&gt;high&lt;/i&gt; and &lt;i&gt;full automation&lt;/i&gt;) in the context of &lt;i&gt;motor vehicles&lt;/i&gt; (hereafter also referred to as “&lt;i&gt;vehicle&lt;/i&gt;” or “&lt;i&gt;vehicles&lt;/i&gt;”) and their operation on public roadways. These latter levels of advanced automation refer to cases in which the

9. Autonomous Vehicle Technology: A Guide for Policymakers

   James Anderson, Nidhi Kalra, Karlyn Stanley, et al. · 2016 · RAND Corporation eBooks · 647 citations

   Self-driving vehicles offer the promise of significant benefits to society, but raise several policy challenges, including the need to update insurance liability regulations and privacy concerns such as who will control the data generated by this technology.

10. Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems

    Shinpei Kato, Shota Tokunaga, Yuya Maruyama, et al. · 2018 · 629 citations

    This paper presents Autoware on Board, a new profile of Autoware, especially designed to enable autonomous vehicles with embedded systems. Autoware is a popular open-source software project that provides a complete set of self-driving modules, including localization, detection, prediction, planning, and control. We customize and extend the software stack of Autoware to accommodate embedded computing capabilities. In particular, we use DRIVE PX2 as a reference computing platform, which is manufactured by NVIDIA Corporation for development of autonomous vehicles, and evaluate the performance of Autoware on ARM-based embedded processing cores and Tegra-based embedded graphics processing units (

11. Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

    Long Chen, Yuchen Li, Chao Huang, et al. · 2022 · IEEE Transactions on Intelligent Vehicles · 491 citations

    Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical su

12. Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

    Jamil Fayyad, Mohammad A. Jaradat, Dominique Gruyer, et al. · 2020 · Sensors · 478 citations

    Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for

13. Can You Trust Autonomous Vehicles : Contactless Attacks against Sensors of Self-driving Vehicle

    Chen Yan · 2016 · 357 citations

14. Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review

    Zaid Tahir, R. Alexander · 2020 · 2020 IEEE International Conference On Artificial Intelligence Testing (AITest) · 45 citations

    Self-driving Autonomous Vehicles (SAVs) are gaining more interest each passing day by the industry as well as the general public. Tech and automobile companies are investing huge amounts of capital in research and development of SAVs to make sure they have a head start in the SAV market in the future. One of the major hurdles in the way of SAVs making it to the public roads is the lack of confidence of public in the safety aspect of SAVs. In order to assure safety and provide confidence to the public in the safety of SAVs, researchers around the world have used coverage-based testing for Verification and Validation (V&V) and safety assurance of SAVs. The objective of this paper is to investi

15. Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

    Hao Zhou, Jorge A. Laval, Anye Zhou, et al. · 2019 · Transportation Research Record · 44 citations

    Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV indust

16. Advancements and challenges in achieving fully autonomous self-driving vehicles

    V. Satya, Rahul Kosuru, Ashwin Kavasseri Venkitaraman · 2023 · World Journal of Advanced Research and Reviews · 37 citations

    This article presents a review and analysis of the prospects of achieving full-length autonomous driving, a concept that has long been a dream of humans. Although the automotive industry has made significant progress in many areas, creating fully automated vehicles (level 5) has remained a challenge. This paper examines some companies that are already racing to achieve this feat, such as Tesla, Google's Waymo, and Uber, and the challenges they face, such as ensuring safety and reliability while also dealing with complex and expensive technology. The article highlights the issues that must be addressed when discussing fully automated vehicles, such as legal and regulatory frameworks, public a

17. Steering towards safe self-driving laboratories

    Shi Xuan Leong, Caleb E Griesbach, Rui Zhang, et al. · 2025 · Nature Reviews Chemistry · 37 citations

18. Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

    Chengqun Qiu, Hao Tang, Yucheng Yang, et al. · 2024 · Scientific Reports · 35 citations

    The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning c

19. How to Guarantee Driving Safety for Autonomous Vehicles in a Real-World Environment: A Perspective on Self-Evolution Mechanisms

    Shuo Yang, Yanjun Huang, Li Li, et al. · 2024 · IEEE Intelligent Transportation Systems Magazine · 24 citations

    A succession of accidents shows that production vehicles with autonomous driving systems do not work safely in real-world environments, especially when facing unseen scenarios. Therefore, how to ensure that autonomous systems drive more safely becomes a challenge. Thanks to the self-learning ability of human beings, human drivers can gradually learn how to drive from a driving test with typical and finite scenarios to the real world with infinite ones. Analogically, it is believed that accidents can be largely reduced once the designed autonomous vehicles are endowed with a self-learning ability to adapt to the unseen and then to infinite scenarios in the real world. Accordingly, this work p

20. Road traffic safety assessment in self-driving vehicles based on time-to-collision with motion orientation.

    F. M. Ortiz, Matteo Sammarco, Marcin Detyniecki, et al. · 2023 · Accident; analysis and prevention · 19 citations

    Traffic conflict analysis based on Surrogate Safety Measures (SSMs) helps to estimate the risk level of an ego-vehicle interacting with other road users. Nonetheless, risk assessment for autonomous vehicles (AVs) is still incipient, given that most of the AVs are currently prototypes and current SSMs do not directly apply to autonomous driving styles. Therefore, to assess and quantify the potential risk arising from AV interactions with other road users, this study introduces the TTCmo (Time-to-Collision with motion orientation), a metric that considers the yaw angle of conflicting objects. In fact, the yaw angle represents the orientation of the other road users and objects detected by the

21. Real-time combined safety-mobility assessment using self-driving vehicles collected data.

    Ahmed Kamel, Tarek Sayed, M. Kamel · 2024 · Accident; analysis and prevention · 16 citations

    The study presents a real-time safety and mobility assessment approach using data generated by autonomous vehicles (AVs). The proposed safety assessment method uses Bayesian hierarchical spatial random parameter extreme value model (BHSRP), which can handle the limited availability and uneven distribution of conflict data and accounts for unobserved spatial heterogeneity. The approach estimates two real-time safety metrics: the risk of crash (RC) and return level (RL), using Time-To-Collision (TTC) as conflict indicator. Additionally, a Risk Exposure (RE) index was developed to reflect the risk of an individual vehicle to travel through a corridor. In parallel, the mobility of corridor were

22. Autonomous Lateral Maneuvers for Self-Driving Vehicles in Complex Traffic Environment

    Zhaolun Li, Jingjing Jiang, Wen‐Hua Chen, et al. · 2023 · IEEE Transactions on Intelligent Vehicles · 15 citations

    Autonomous driving functions have gained great interests from both academia and industry over the years. This paper proposes a Model Predictive Control based method to generate a safe and feasible trajectory for the ego vehicle to perform various lateral maneuvers and to produce optimized control inputs to guide the ego vehicle through a mixed traffic environment with both human drivers and autonomous vehicle. A novel reference speed generation function is proposed to automatically adjust the position of the ego vehicle before the initiation of any lateral maneuvers. After a proper gap is selected, the lateral maneuver initiation function with an add-on threshold function is introduced to en

23. From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles

    Giandomenico Caruso, Mohammad Kia Yousefi, Lorenzo Mussone · 2022 · Vehicles · 9 citations

    The driving behaviour of Connected and Automated Vehicles (CAVs) may influence the final acceptance of this technology. Developing a driving style suitable for most people implies the evaluation of alternatives that must be validated. Intelligent Virtual Drivers (IVDs), whose behaviour is controlled by a program, can test different driving styles along a specific route. However, multiple combinations of IVD settings may lead to similar outcomes due to their high variability. The paper proposes a method to identify the IVD settings that can be used as a reference for a given route. The method is based on the cluster analysis of vehicular data produced by a group of IVDs with different setting

24. Robust Multi‐Agent Reinforcement Learning Against Adversarial Attacks for Cooperative Self‐Driving Vehicles

    Chuyao Wang, Ziwei Wang, Nabil Aouf · 2025 · IET Radar, Sonar &amp; Navigation · 7 citations

    Multi‐agent deep reinforcement learning (MARL) for self‐driving vehicles aims to address the complex challenge of coordinating multiple autonomous agents in shared road environments. MARL creates a more stable system and improves vehicle performance in typical traffic scenarios compared to single‐agent DRL systems. However, despite its sophisticated cooperative training, MARL remains vulnerable to unforeseen adversarial attacks. Perturbed observation states can lead one or more vehicles to make critical errors in decision‐making, triggering chain reactions that often result in severe collisions and accidents. To ensure the safety and reliability of multi‐agent autonomous driving systems, thi