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.
Towards full-scenario safety evaluation of automated vehicles: A volume-based method,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.