Evaluating Autonomous Vehicle Safety Performance Through Analysis of Pre-Crash Trajectories of Powered Two-Wheelers

To ensure the safety of Autonomous Vehicles (AVs), thorough testing across virtual simulation environments, closed facilities, and public roads is essential. Scenario-based testing stands out as a crucial method for evaluating AVs, with a key focus on constructing appropriate testing scenarios. Given the vulnerability of Powered Two-Wheelers (PTWs) riders, it is essential to investigate typical and representative car-to-PTWs crash scenarios and validate AV system safety performance in such situations. This study introduces a new method for generating high-risk scenarios by extracting typical testing scenarios from real-world crashes, thereby enhancing the realism of testing conditions. To evaluate the safety performance of AV systems, a crash-based testing approach is proposed. First, 222 car-to-PTWs crashes were extracted from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database and the pre-crash trajectories of crash-involved parties were accurately obtained by reconstructing the crashes case-by-case. Second, utilizing the $k$ -medoids algorithm based on the Hausdorff distance to cluster these trajectories, six typical pre-crash trajectory clusters were extracted. Third, six sets of high-risk scenarios were generated using parameter discretization and combination testing based on the cluster centers. Finally, we conducted safety testing on the black-box automated driving system, Baidu Apollo, using the SVL Simulator virtual simulation platform. We evaluated its performance by subjecting it to the six sets of high-risk scenarios generated in this study. The experimental results demonstrate that Apollo can operate safely in most high-risk scenarios, indicating that automated driving systems can handle crashes that some human drivers cannot avoid, thereby improve traffic safety.