Diverse Critical Interaction Generation for Planning and Planner Evaluation
This work addresses the need for safe and robust planning in autonomous vehicles by providing a method to generate natural and critical test interactions, though it is incremental as it builds on existing generative models for traffic scenarios.
The paper tackles the problem of generating diverse and critical interactions for evaluating autonomous vehicle planners by proposing RouteGAN, a styled generative model that produces trajectories with varying safety levels, achieving a reduction in collision rates for tested planners.
Generating diverse and comprehensive interacting agents to evaluate the decision-making modules is essential for the safe and robust planning of autonomous vehicles~(AV). Due to efficiency and safety concerns, most researchers choose to train interactive adversary~(competitive or weakly competitive) agents in simulators and generate test cases to interact with evaluated AVs. However, most existing methods fail to provide both natural and critical interaction behaviors in various traffic scenarios. To tackle this problem, we propose a styled generative model RouteGAN that generates diverse interactions by controlling the vehicles separately with desired styles. By altering its style coefficients, the model can generate trajectories with different safety levels serve as an online planner. Experiments show that our model can generate diverse interactions in various scenarios. We evaluate different planners with our model by testing their collision rate in interaction with RouteGAN planners of multiple critical levels.