一句话：提出 TRACE 流水线，用 OpenStreetMap 与 LLM 将 NHTSA 事故报告自动重建为高保真 CARLA 仿真场景，并发布 52 个事故基准。
验证自动驾驶系统需要暴露于罕见但安全攸关的场景，而日常驾驶数据中这类场景极为稀少。现有基准多通过生成合成冲突，或把事故描述映射到抽象道路几何来应对，无法刻画真实碰撞的拓扑复杂性。本文提出 TRACE 流水线，自动将 NHTSA 事故报告重建为高保真 CARLA 仿真：(1) 检索特定地点的 OpenStreetMap 数据以精确保留道路拓扑；(2) 利用大语言模型结合道路几何与碰撞前机动推断车辆初始状态；(3) 由半结构化报告数据生成仿真轨迹。基于该流水线，作者整理出包含 52 个多样化事故场景的基准，覆盖不同碰撞类型、道路拓扑与碰撞前机动，为在真实失效情形下测试自动驾驶系统提供了具有挑战性的开源资源。
Validating Autonomous Vehicles (AVs) requires exposure to rare, safety-critical scenarios, infrequent in routine driving data. Existing benchmarks address this by generating synthetic conflicts or mapping accident descriptions to abstract road geometries, failing to capture the topological complexity of real-world crashes. We introduce TRACE, a pipeline that automates the reconstruction of NHTSA crash reports into high-fidelity CARLA simulations by (1) retrieving site-specific OpenStreetMap data to preserve exact road topology, (2) leveraging Large Language Models to infer vehicles' initial state from road geometry and pre-crash maneuvers, and (3) generating simulation trajectories from semi-structured report data. Using this pipeline, we curated a benchmark of 52 diverse accident scenarios covering varied collision types, road topologies, and pre-crash maneuvers, providing a challenging open-source resource for testing AV systems against real-world failures.