ABSTRACT: It's a critical step to generate test cases widely covering High-risk scenarios in the real traffic environment for Autonomous Vehicle (AV) simulation test. This study aims to design a method of AV test scenario construction based on Monte Carlo method and Importance Sampling method. Two thousand and forty-nine cut-in events were extracted from the Shanghai Naturalistic Driving Study data, the corresponding scenario information for each event was used as scenario parameters. Time To Collision is used as the evaluation index. The results show that test scenarios generated by Monte Carlo method cover a larger area, however, it cannot increase the proportion of dangerous scenarios and highly-risky scenarios. Monte Carlo method combined with Importance Sampling are capable of generating scenarios covering all risk levels. The number of dangerous scenarios generated by this method is 1.61 times more than that simply generated by Monte Carlo method, which can better support autonomous driving testing.
Shuang Liu, Xuesong Wang. Cut-in Test Scenarios Generation Method for Autonomous Vehicles. Transportation Research Board 100th Annual Meeting, Washington D.C., USA, 2021. 1.25-29.