Novel Test Scenario Generation Technology for Performance Evaluation of Automated Vehicle

As one of the critical technologies for performance evaluation of automated vehicles, the test scenario generation has been widespread concerned. In this paper, we propose a novel test scenario generation technology based on optimized Latin Hypercube Sampling (OLHS) and Test Matrix method (TM), named HIS-MPSO, which is efficient to generate the test scenario that consider the complexity, coverage, and potential relationships of factors. Based on naturalistic driving data, numerous car-following scenarios are generated by HIS-MPSO. Then, an adaptive cruise control system (ACC) are evaluated in terms of the tracking errors, comfort, and safety using the generated scenarios. Results show that compared with other existing OLHS algorithms, the HIS-MPSO can better restore the relationships among test factors existed in realistic traffic scenarios.