Tag
This paper proposes a modular LLM-based pipeline that generates diverse test scenarios for autonomous driving systems using historical failure records (e.g., NHTSA crash data), enabling effective failure discovery within limited testing budgets.
This research paper proposes a transformer-based reinforcement learning framework to automatically generate safety-critical test scenarios for Unmanned Traffic Management (UTM) systems, achieving an 8× improvement in vulnerability discovery efficiency over expert-guided testing.
ScenePilot proposes a feasibility-guided, boundary-driven framework for generating safety-critical scenarios for autonomous driving, using constrained multi-objective reinforcement learning to produce physically valid yet failure-inducing scenarios.