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Research papers on Self-driving car safety

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  1. A Survey of Autonomous Driving: Common Practices and Emerging Technologies

    Ekim Yurtsever, Jacob Lambert, Alexander Carballo, et al. · 2020 · IEEE Access · 1,801 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

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  2. A survey of deep learning techniques for autonomous driving

    Sorin Grigorescu, Bogdan Trăsnea, Tiberiu Cocias, et al. · 2019 · Journal of Field Robotics · 1,790 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

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  3. Planning and Decision-Making for Autonomous Vehicles

    Wilko Schwarting, Javier Alonso–Mora, Daniela Rus · 2018 · Annual Review of Control Robotics and Autonomous Systems · 969 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

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  4. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning

    Todd Litman · 2015 · Transportation Research Board 94th Annual MeetingTransportation Research Board · 864 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

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

    De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al. · 2021 · Sensors · 859 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

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  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 · 804 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 b

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

    On-Road Automated Driving (ORAD) Committee · 2014 · 686 citations

    This Information Report provides a taxonomy for motor vehicle automation ranging in level from no automation to full automation. However, it provides detailed definitions only for the highest three levels of automation provided in the taxonomy (namely, conditional, high and full automation) in the context of motor vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on public roadways. These latter levels of advanced automation refer to cases in which the dynamic driving task is performed entirely by an automated driving system during a given driving mode or trip. Popular, media, and legislative references to “autonomous” or “self-driving” vehicles encompass s

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  9. Autonomous Vehicle Technology: A Guide for Policymakers

    James Anderson, Nidhi Kalra, Karlyn Stanley, et al. · 2016 · RAND Corporation eBooks · 654 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.

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

    Shinpei Kato, Shota Tokunaga, Yuya Maruyama, et al. · 2018 · 649 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 (

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  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 · 509 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

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

    Jamil Fayyad, Mohammad A. Jaradat, Dominique Gruyer, et al. · 2020 · Sensors · 494 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

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  13. 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

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  14. A self driving license: Ensuring autonomous vehicles deliver on the promise of safer roads

    Christopher Bradley · 2020 · MIT Science Policy Review · 3 citations

    Upon maturation, autonomous vehicles (AVs) have the potential to provide significant benefit to society. A breadth of partially autonomous systems are already commercially available, and vehicles with advanced capabilities are tested and deployed on public roads. Although the advancement of AV technology is highly anticipated, the future of the industry currently rests on uncertain ground with respect to regulatory oversight. The current industry standards and legal regulations which apply to AVs are only equipped to fully ensure that simple autonomous capabilities are safe. As vehicles become more autonomous, and as driving decisions are shifted from human to computer, a regulatory paradigm

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  15. Safety-Aware Adversarial Inverse Reinforcement Learning for Highway Autonomous Driving

    Fangjian Li, John Wagner, Yue Wang · 2021 · Journal of Autonomous Vehicles and Systems · 2 citations

    Abstract Inverse reinforcement learning (IRL) has been successfully applied in many robotics and autonomous driving studies without the need for hand-tuning a reward function. However, it suffers from safety issues. Compared to the reinforcement learning algorithms, IRL is even more vulnerable to unsafe situations as it can only infer the importance of safety based on expert demonstrations. In this paper, we propose a safety-aware adversarial inverse reinforcement learning (S-AIRL) algorithm. First, the control barrier function is used to guide the training of a safety critic, which leverages the knowledge of system dynamics in the sampling process without training an addition

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  17. Investigation of Driving Safety on Desert Highways Under Crosswind Direction Disturbances

    Zheguang Zhang, Songli Chen, Wei Zhang · 2025 · Vehicles · 2 citations

    Desert highways, with open terrain and minimal wind barriers, expose high-speed vehicles to significant stability risks from combined crosswinds and sand accumulation. This study uses numerical simulation to assess the effects of varying wind direction angles and sand thicknesses on vehicle stability across different models. Five dynamic indicators—lateral displacement, yaw angle, aerodynamic sideslip angle, lateral acceleration, and roll angle—are analyzed. The results show that a 120° wind angle causes the most pronounced parameter changes, while stability is lowest at 150°, where critical thresholds are reached within 0.75 s and danger thresholds by 2.25 s. Rapid wind speed variations fur

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  18. The Lexicon of Self-Driving Vehicles and the Fuliginous Obscurity of ‘Autonomous’ Vehicles

    James Marson, Katy Ferris · 2023 · Statute Law Review · 1 citations

    Abstract Self-driving cars, also referred to as connected and autonomous vehicles, are not only in vogue among technology and car enthusiasts (among others) but they have been broadly considered to form a new and disruptive means of transport. The benefits of self-driving cars are replete with stories of inclusivity, safety, environmental benefits, and social connectivity. However, the reality of the words ‘self-driving’ and ‘autonomous’ in the designation of this form of transport are not only inadequately defined, they appear to be actively misleading individuals as to the capabilities of the vehicle and the responsibility that they as driver or person behind the wheel have

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  19. Edge Computing for Autonomous Vehicles Improving Real-Time Data Analysis and Decision-Making for Enhanced Safety and Efficiency in Self-Driving Cars

    Gayatri M. Bhandari · 2025 · Journal of Information Systems Engineering and Management · 1 citations

    Edge computing is a key part of making autonomous cars (AVs) better because it lets people analyze and make decisions about data in real time at the network's edge, which improves safety and efficiency. This theoretical looks at how edge computing advances can be utilized in AV frameworks to unravel critical issues and progress execution. Numerous sensors and frameworks offer assistance self-driving cars get it and get around in their environment. These screens deliver a colossal sum of information that has to be taken care of rapidly so that choices can be made on time. Idleness issues happen with conventional cloud-based strategies since information exchange delays happen between the car a

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  21. AUTOMATED AND AUTONOMOUS VEHICLES - SAFETY, APPROVAL, SOCIAL BENEFITS AND FEARS OF INTRODUCING AUTOMATIC DRIVING SYSTEMS

    Artur Gołowicz, Sławomir Cholewiński · 2021 · Transport Samochodowy

    The paper discusses the details of the work of automation systems for motor vehicles and their methods of testing. The requirements of the new UN Regulation No. 157 were presented as a tool for conducting the type-approval of automated vehicles in the field of the Automated Lane Keeping System (ALKS). The most important requirements and methods of testing ALKS systems for the vehicle type-approval are described. Evaluation of the advantages and disadvantages as well as the effects and social concerns of the implementation of such systems in relation to the road safety, has been carried out.

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