  Autonomous vehicles must be comprehensively evaluated before deployed in
cities and highways. However, most existing evaluation approaches for
autonomous vehicles are static and lack adaptability, so they are usually
inefficient in generating challenging scenarios for tested vehicles. In this
paper, we propose an adaptive evaluation framework to efficiently evaluate
autonomous vehicles in adversarial environments generated by deep reinforcement
learning. Considering the multimodal nature of dangerous scenarios, we use
ensemble models to represent different local optimums for diversity. We then
utilize a nonparametric Bayesian method to cluster the adversarial policies.
The proposed method is validated in a typical lane-change scenario that
involves frequent interactions between the ego vehicle and the surrounding
vehicles. Results show that the adversarial scenarios generated by our method
significantly degrade the performance of the tested vehicles. We also
illustrate different patterns of generated adversarial environments, which can
be used to infer the weaknesses of the tested vehicles.
