用公开事故报告重建真实交通事故，实现高精度物理还原。
Learning physically grounded traffic accident reconstruction from public accident reports
- 从文本报告与场景数据中学习多模态参数化重建方法
- 在6217个事故案例上实现更高碰撞点精度与一致性
- 适合交通仿真、自动驾驶和交通安全研究使用
交通事故常以文字报告形式记录，但因缺乏详细场景测量与专家重建，物理可信的事故复原仍具挑战。本文将基于公开报告与场景数据的事故重建建模为参数化多模态学习问题。构建了包含6,217个真实事故案例的CISS-REC数据集，源自NHTSA Crash Investigation Sampling System。提出一种框架，将报告语义与道路拓扑、参与者属性对齐，重构符合车道规则的碰撞前运动，并通过局部几何推理与时间分配优化碰撞交互。在CISS-REC上优于代表性基线，整体重建保真度最高，显著提升事故点定位精度与碰撞一致性。结果表明，公共事故报告可作为可扩展、可量化验证的事故重建计算基础，对交通安全管理、仿真及自动驾驶研究具有潜在价值。
原文摘要 · Abstract (English)
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. Here we formulate accident reconstruction from publicly accessible reports and scene measurements as a parameterized multimodal learning problem. 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. Our method outperforms representative baselines on CISS-REC, achieving the strongest overall reconstruction fidelity, including improved accident point accuracy and collision consistency. 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.
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