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.
Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework,
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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.