Learning physically grounded traffic accident reconstruction from public accident reports
Authors: Yanchen Guan, Haicheng Liao, Chengyue Wang, Zhenning Li
Organizations: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau SAR, 999078, China · Department of Civil Engineering, University of Macau, Macau SAR, 999078, China · Department of Computer and Information Science, University of Macau, Macau SAR, 999078, China
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.