A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning.

• A dynamic test scenario generation method for AVs is proposed in this paper. The proposed method has the ability to generate a realistic driving environment and apply to more complex scenarios like lane changing scenarios, which is of significant value for AV testing and evaluation.** • Instead of reconstructing expert behavior based on the assumption of single modality, this method combines Hierarchical Dirichlet Process Hidden Semi-Markov model (HDP-HSMM) and GAIL, obtains the main modes through clustering, and directs the scenario generation process by conditioning the model on scenario class labels, which improves the scenarios diversity and generation efficiency. • A typical lane-changing scenario is used for the evaluation of the proposed method. The results show that this method can generate rich test scenarios, and test AVs' ability to deal with different kinds of dynamic scenarios. Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways. However, most existing evaluation approaches for autonomous vehicles are static and model environmental vehicles with predefined trajectories, which ignore the time-sequential interactions between the ego vehicle and environmental vehicles. In this paper, we propose a dynamic test scenario generation method to evaluate autonomous vehicles by modeling environmental vehicles as agents with human behavior and simulating the interaction process between the autonomous vehicle and environmental vehicles. Considering the multimodal features of traffic scenarios, we cluster the real-word traffic environments, and integrate the scenario class labels into the conditional generative adversarial imitation learning (CGAIL) model to generate different types of traffic scenarios. The proposed method is validated in a typical lane-change scenario that involves frequent interactions between ego vehicle and environmental vehicles. Results show that the proposed method further test autonomous vehicles' ability to cope with dynamic scenarios, and can be used to infer the weaknesses of the tested vehicles. [ABSTRACT FROM AUTHOR]