Learning physically grounded traffic accident reconstruction from public accident reports

基于公开事故报告学习物理基础的交通事故重构

Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reconstructions are scarce, costly and hard to scale. 交通事故通常以文本报告的形式记录，但基于物理的事故重构仍然困难重重，因为详细的现场测量数据和专家重构案例稀缺、成本高昂且难以规模化。

Here we formulate accident reconstruction from publicly accessible reports and scene measurements as a parameterized multimodal learning problem. 在此，我们将基于公开报告和现场测量的事故重构问题，构建为一个参数化的多模态学习问题。

We construct CISS-REC, a dataset of 6,217 real-world accident cases curated from the NHTSA Crash Investigation Sampling System, and develop a reconstruction framework that grounds report semantics to road topology and participant attributes, reconstructs lane consistent pre-impact motion, and refines collision relevant interactions through localized geometric reasoning and temporal allocation. 我们构建了 CISS-REC 数据集，其中包含从美国国家公路交通安全管理局（NHTSA）事故调查抽样系统中精选出的 6,217 个真实事故案例。同时，我们开发了一个重构框架，该框架将报告语义与道路拓扑及参与者属性相结合，重构出符合车道的碰撞前运动轨迹，并通过局部几何推理和时间分配来优化与碰撞相关的交互过程。

Our method outperforms representative baselines on CISS-REC, achieving the strongest overall reconstruction fidelity, including improved accident point accuracy and collision consistency. 我们的方法在 CISS-REC 上优于现有的代表性基准模型，实现了最强的整体重构保真度，包括提高了事故点定位的准确性和碰撞一致性。

These results show that public accident reports can serve as scalable computational substrates for quantitatively verifiable accident reconstruction, with potential value for traffic safety analysis, simulation and autonomous driving research. 这些结果表明，公开的事故报告可以作为可量化验证的事故重构的可扩展计算基础，在交通安全分析、仿真模拟和自动驾驶研究方面具有潜在价值。