4 papers
KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution
Cheng Wang, Chen Xiong, Ziwen Wang +2
Safety validation of autonomous driving systems requires high-risk scenario coverage, clear collision semantics, executable trajectories, and attributable multi-vehicle interaction…
Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation
Chen Xiong, Cheng Wang, Yuhang Liu +2
In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely…
Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving
Chen Xiong, Ziwen Wang, Deqi Wang +4
Autonomous driving testing increasingly relies on mining safety critical scenarios from large scale naturalistic driving data, yet existing screening pipelines still depend on manu…
AnchorDrive: LLM Scenario Rollout with Anchor-Guided Diffusion Regeneration for Safety-Critical Scenario Generation
Zhulin Jiang, Zetao Li, Cheng Wang +2
Autonomous driving systems require comprehensive evaluation in safety-critical scenarios to ensure safety and robustness. However, such scenarios are rare and difficult to collect…