SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling

Researchers propose SaFeR, a new AI system for generating safety-critical scenarios to test autonomous driving systems. The approach uses transformer-based models with a novel resampling strategy to balance adversarial testing, physical feasibility, and realistic behavior in autonomous vehicle simulations.