A unified experimental framework for estimating collision rates and occupant injury severity across different levels of driving automation

Abstract

Safety performance of autonomous vehicles is crucial for their adoption and market acceptance. However, the lack and imbalance of real-world accident data have prevented a rigorous verification of the safety performance of autonomous vehicles across manufacturers and automation levels in safety–critical scenarios. This study aimed to bridge this gap by establishing a unified experimental framework that enables fair comparisons of vehicle safety across automation levels under comparable road scenarios, similar urgency levels and consistent evaluation metrics. Vehicles at different automation levels were evaluated in simulated highway scenarios, with hazard-triggering algorithms generating safety–critical events and occupant injury severity estimated under specific collision conditions. The results show that in our designed safety–critical scenarios, vehicles operating at automation levels 2, 3, and 4 have collision rates of 24.3%, 21.4%, and 14.1%, respectively, with corresponding probabilities of severe occupant injuries of 11.1%, 21.6%, and 9.2%. Among the findings, Level 3 autonomous vehicles can reduce collision rates but may result in more severe occupant injuries compared to Level 2 vehicles, thus leading to a comparable unified safety benefit. Level 4 autonomous vehicles show improved safety benefits over Level 2, primarily due to lower collision rates, while the severity of occupant injuries remains similar once a collision occurs. This study offers a unified experimental framework to robustly evaluate safety performance of autonomous vehicles in safety–critical scenarios, and support large-scale deployment of autonomous vehicles in the future.