Diverse critical interaction generation for planning and planner evaluation
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 …
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 …
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