What patterns contribute to autonomous vehicle crashes? A study of Level 2 and 4 automation via association rule analysis

With increasing autonomous vehicle (AV) penetration, understanding the factors contributing to AV crashes is crucial for addressing ongoing road safety challenges. This study aims to reveal the effects of vehicle characteristics, road conditions, environmental factors, and precrash movements on the occurrence of head-on, rear-end, and side-impact crashes. In particular, factors associated with various types of Level 2 and Level 4 AV crashes were analyzed. Our data, obtained from the California Department of Motor Vehicles and the National Highway Traffic Safety Administration, spans from October 2014 to March 2024. Association rule mining techniques identify the significant patterns and interdependencies among the factors contributing to AV crashes. The findings demonstrate that the factors influencing crash types include weather, roadway surface, lighting, and vehicle precrash movements. For example, head-on crashes frequently occur at intersections under poor lighting conditions, whereas rear-end crashes occur more frequently on high-speed highways, particularly because of unexpected braking by the vehicle ahead. Side-impact crashes commonly result from merging maneuvers, especially under adverse weather and lighting conditions. These findings provide new insights into the causal mechanisms behind different types of AV crashes and underscore the need to strengthen traffic management, enhance AV sensing and decision-making capabilities, and implement targeted safety measures in high-risk areas.