  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.
