Dynamic Testing for Autonomous Vehicles Using Random Quasi Monte Carlo

The substantial resource usage required to create ample scenarios for testing Autonomous Vehicles (AV) presents a bottleneck in their implementation. At present, research relies on sampling the driving behaviour of Surrounding Vehicles (SV) based on naturalistic datasets in simulation. However, these methods still generate huge amounts of scenarios, making it impossible to synthetically evaluate AV intelligence in a very small number of tests (especially in real-world situations). Simultaneously, the unknown distribution of critical scenarios leads to the problem that more critical scenarios cannot be accurately sampled. In this paper, a novel optimization problem is described and a dynamic scenario sampling method is proposed to cover more critical scenarios with finite samples. First, the sampling space is constructed by extracting the behavioural model parameters of the SVs. Second, multiple rounds of sampling are carried out successively to learn the distribution of critical scenarios, which in turn gradually improves the coverage of the critical scenarios. To do this, in each round, we divide the sampling space into several subspaces using two-step sampling, sample the scenarios using Random Quasi Monte Carlo (RQMC), evaluate the criticality of the subspace, and then use the evaluation results to guide the selection of the sampling space for the next round. The purpose of RQMC is to uniformly sample in the critical subspace rather than Standard Monte Carlo (SMC). Experimental results show that our method can better narrow the gap with the distribution of critical scenarios and discover more critical scenarios when compared to the baseline method.