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.
Chatscene: Knowledge-enabled safety- critical scenario generation for autonomous vehicles
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KG-ASG generates closed-loop adversarial driving scenarios guided by collision knowledge and primary-support attribution to improve effectiveness, interpretability, and single-collider executability for autonomous vehicle safety validation.
citing papers explorer
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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.
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KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution
KG-ASG generates closed-loop adversarial driving scenarios guided by collision knowledge and primary-support attribution to improve effectiveness, interpretability, and single-collider executability for autonomous vehicle safety validation.