相关论文: A Note on Tesla's Revised Safety Report Crash Rate…

We present an overview of recently developed data-driven tools for safety analysis of autonomous vehicles and advanced driver assist systems. The core algorithms combine model-based, hybrid system reachability analysis with sensitivity…

Nowadays, systematic security risk analysis plays a vital role in the automotive domain. The demand for advanced driver assistance systems and connectivity of vehicles to the internet makes cyber-security a crucial requirement for vehicle…

Proprietary Autonomous Driving Systems are typically evaluated through disengagements, unplanned manual interventions to alter vehicle behavior, as annually reported by the California Department of Motor Vehicles. However, the real-world…

Advanced driver assistance systems (ADAS) are often used in the automotive industry to highlight innovative improvements in vehicle safety. However, today it is unclear whether certain automation (e.g., adaptive cruise control, lane…

Introduction: This paper reviewed current driving automation (DA) and baseline human-driven crash databases and evaluated their comparability. Method: Five sources of DA crash data and three sources of human-driven crash data were reviewed…

Risk is traditionally described as the expected likelihood of an undesirable outcome, such as collisions for autonomous vehicles. Accurately predicting risk or potentially risky situations is critical for the safe operation of autonomous…

Reducing traffic accidents is an important public safety challenge. However, the majority of studies on traffic accident analysis and prediction have used small-scale datasets with limited coverage, which limits their impact and…

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are expected to improve comfort, productivity and, most importantly, safety for all road users. To ensure that the systems are safe, rules and regulations…

Walter Huang, a 38-year-old Apple Inc. engineer, died on March 23, 2018, after his Tesla Model X crashed into a highway barrier in Mountain View, California. Tesla immediately disavowed responsibility for the accident. "The fundamental…

Validating Autonomous Vehicles (AVs) requires exposure to rare, safety-critical scenarios, infrequent in routine driving data. Existing benchmarks address this by generating synthetic conflicts or mapping accident descriptions to abstract…

Reinforcement Learning (RL) is a promising approach for achieving autonomous driving due to robust decision-making capabilities. RL learns a driving policy through trial and error in traffic scenarios, guided by a reward function that…

In this paper, we focus on the performance analysis of a semi-persistent scheduling scheme for vehicular safety communications, motivated by the Mode 4 medium access control protocol in 3GPP Release 14 for Cellular-V2X. An analytical model…

Most novice drivers are teenagers since many individuals begin their driving journey during adolescence. Novice driver crashes remain a leading cause of death among adolescents, underscoring the necessity for effective education and…

Crash data of autonomous vehicles (AV) or vehicles equipped with advanced driver assistance systems (ADAS) are the key information to understand the crash nature and to enhance the automation systems. However, most of the existing crash…

Increasing communication and self-driving capabilities for road vehicles lead to threats imposed by attackers. Especially attacks leading to safety violations have to be identified to address them by appropriate measures. The impact of an…

Road safety is impacted by a range of factors that can be categorized into human, vehicle, and roadway/environmental elements. This research explores the connection between pavement performance and road safety, particularly in relation to…

Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with…

This paper proposes an extensive overview of safety applications and approaches as it relates to automated driving from the prospectives of sensor configurations, vehicle dynamics modelling, tyre modeling, and estimation approaches. First,…

Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods…

Human drivers naturally balance the risks of different concerns while driving, including traffic rule violations, minor accidents, and fatalities. However, achieving the same behavior in autonomous driving systems remains an open problem.…