PCASim uses LLMs to integrate knowledge, data, and adversarial methods for generating promptable safety-critical urban traffic scenarios, with RL training for vehicle behaviors, reporting 12% better DSL accuracy, 8% higher scenario success rate, and 30% improved obstacle avoidance.
Kinetic analysis and numerical tests of an adaptive car-following model for real-time traffic in its,
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PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment
PCASim uses LLMs to integrate knowledge, data, and adversarial methods for generating promptable safety-critical urban traffic scenarios, with RL training for vehicle behaviors, reporting 12% better DSL accuracy, 8% higher scenario success rate, and 30% improved obstacle avoidance.