Quantifying risk factor influences in autonomous vehicle collisions: a Bayesian network probabilistic analysis

OBJECTIVES: The frequent occurrence of Autonomous Vehicle (AV) collisions significantly impacts development and user trust. These collisions arise from a complex interaction of factors, but their interdependencies are not fully understood. This study analyzed 776 publicly available AV -related collision reports from the California Department of Motor Vehicles, identifying key factors and their complex interactions. The identified risk factors are divided into three categories: vehicle basic information, collision details, and road and environmental characteristics. METHODS: The statistical analysis method of Chi-square test was used to evaluate the significance of single factors. Bayesian network analysis further exploratorily constructs causal chains and examines the impact of each factor on collision severity. RESULTS: Six variables, including vehicle mode, brand, collision type, pre-collision motion, weekday and roadway type, can independently affect the collision severity. Bayesian network exploration found that brand affects vehicle mode, vehicle mode affects pre-collision motion, and pre-collision motion affects collision type. Side swipe collisions, rear-end collisions, road sections, stationary or slow-moving conditions are the most likely to cause property damage. Casualties are most likely to occur in incidents involving broadside collisions and highways. Additionally, intersections are high-risk collision locations. The autonomous driving mode is similar to the conventional human driving mode in terms of collision risks, but there are still certain safety hazards, such as a higher probability of a broadside collision. CONCLUSIONS: These findings show that AV technology should be continuously improved in many aspects such as environmental perception, decision-making algorithms, and safety mechanism design to improve the overall safety and reliability of AV and make it better integrate into daily life.