用真实事故报告生成高保真自动驾驶测试场景
TRACE: Topology-aware Reconstruction of Accidents in CARLA for AV Evaluation
- 基于地图和大模型还原事故现场道路拓扑与车辆初始状态
- 构建52个多样事故场景，涵盖不同碰撞类型与道路结构
- 适合评估自动驾驶系统在复杂真实事故下的应对能力
验证自动驾驶汽车需接触罕见且危及安全的场景，而常规驾驶数据中此类情况极少。现有基准通过生成合成冲突或将事故描述映射到抽象道路几何结构来解决，但无法捕捉真实事故的拓扑复杂性。我们提出TRACE，一个自动化管道，将NHTSA事故报告重构为高保真CARLA仿真：(1) 获取特定地点的OpenStreetMap数据以保留精确道路拓扑；(2) 利用大语言模型从道路几何与事故前动作推断车辆初始状态；(3) 从半结构化报告数据生成仿真轨迹。利用该管道，我们构建了一个包含52个多样化事故场景的基准，覆盖多种碰撞类型、道路拓扑和事故前动作，为测试自动驾驶系统在真实事故失败情形下的表现提供了具有挑战性的开源资源。
原文摘要 · Abstract (English)
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.
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