A Data-Driven Control-Policy-Based Driving Safety Analysis System for Autonomous Vehicles
Liuwang Kang, Haiying Shen, Yezhuo Li, Shiwei Xu
Abstract
An autonomous vehicle (AV) is a combination of subsystems, measuring its driving environments with different sensors (e.g., camera, RADAR, and LiDAR) in real time. AVs follow their control policies and make real-time control decisions based on sensor measurements to ensure driving safety. Control policies in an AV are usually implemented with codes and not open to the public and drivers, which results in people’s strong concerns about driving safety. In this article, we propose a data-driven control policy-based driving safety analysis system (PoSa) to analyze the driving safety of a target AV. In PoSa, we first build a data-driven control policy extraction method to extract control policies of a target AV based on its historical driving data. Then, we develop a hazard driving scenario identification method to identify possible hazard driving scenarios of a target AV by executing the extracted control policies under different driving scenarios. Finally, we use vehicle driving data from one industry-standard AV platform (Baidu Apollo) to evaluate PoSa’s hazard driving scenario identification performance. We compared its identification results with Baidu AV accident reports from California DMV and the hazard driving scenario identification results cover as many as 89% hazard driving scenarios in the Baidu AV accident report, which demonstrates that PoSa has good performance on identifying hazard driving scenarios and its identification results can be used to optimize control policies for driving safety improvement.