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Papers on “autonomous vehicles self-driving safety”

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1. 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 cites

   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

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

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

   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

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

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

   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

4. Understanding “driving readiness”: exploiting self-driving functions of autonomous vehicles to increase assembly performance

   Tom Kathmann, Daniel Reh, Julia C. Arlinghaus · 2022 · Procedia CIRP · 2 cites

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

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

   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

6. What Safety are We Entitled to Expect of Self-driving Vehicles?

   Taivo Liivak · 2019 · Juridica International · 2 cites

   A manufacturer of self-driving vehicles could face claims involving assertions of the product’s defectiveness. Under the Product Liability Directive, a product is deemed defective when it does not provide the safety that a person is entitled to expect. Efforts to ascertain the possibility of defectiveness connected with a self-driving vehicle could necessitate evaluating the design of the vehicle, matters of human–machine interaction, and the role of the human in the relevant incident of damage. This article lays groundwork by considering the capabilities of self-driving vehicles, the role and expectations of human beings, and legislation aimed at ensuring safety and preventing damage. This

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

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

   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

8. 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 cite

   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