Evaluating safety performance of autonomous vehicles via mode-dependent collision dynamics

Autonomous vehicles (AVs) are expected to significantly enhance transportation safety in the coming decades. As the development and deployment of AVs continue to grow, it is crucial to understand the risks and factors contributing to collisions, particularly across different operational modes. Most prior studies aggregate AV crash records without distinguishing whether or not the vehicle was operating autonomously or manually at the time of the crash, limiting insights into mode-specific crash dynamics. This study addresses this gap by using crash records from the California Division of Motor Vehicles, statistically segmented by operational mode and analyzed with advanced statistical methods. Key findings include: (1) rear-end collisions are the most common crash type across both operational modes; (2) autonomously operated AVs are more likely to be involved in severe crashes under poor lighting conditions; (3) head-on collisions significantly increase the likelihood of severe outcomes for autonomously operated AVs, even during daylight conditions; and (4) on average, the probability of a severe crash is 25.70% lower when a vehicle is operating autonomously compared to manual control. The implications of these findings are extensively discussed to provide valuable guidance for transportation policymakers and researchers in addressing AV deployment and developing effective strategies to mitigate collision risks in mixed-fleet conditions.