Adaptive Safety Evaluation for Connected and Automated Vehicles With Sparse Control Variates

作者：Jingxuan Yang, Haowei Sun, Honglin He, Yi Zhang, Henry Liu, Shuo Feng · 发表于：IEEE Transactions on Intelligent Transportation Systems · 年份：2023 · DOI：10.1109/tits.2023.3317078 · 被引用次数：13 · 研究领域：Software Reliability and Analysis Research、Autonomous Vehicle Technology and Safety、Safety Systems Engineering in Autonomy

Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances. However, significant differences between CAVs and prior knowledge could severely reduce the evaluation efficiency. Towards addressing this issue, most existing studies focus on the adaptive design of testing scenarios during the CAV testing process, but so far they cannot be applied to high-dimensional scenarios. In this paper, we focus on the adaptive safety performance evaluation by leveraging the testing results, after the CAV testing process. It can significantly improve the evaluation efficiency and be applied to high-dimensional scenarios. Specifically, instead of directly evaluating the unknown quantity (e.g., crash rates) of CAV safety performances, we evaluate the differences between the unknown quantity and known quantity (i.e., control variates). By leveraging the testing results, the control variates could be well-designed and optimized such that the differences are close to zero, so the evaluation variance could be dramatically reduced for different CAVs. To handle the high-dimensional scenarios, we propose the sparse control variates method, where the control variates are designed only for the sparse and critical variables of scenarios. According to the number of critical variables in each scenario,...