Learning physically grounded traffic accident reconstruction from public accident reports

TL;DR

This paper introduces a multimodal learning framework and a new dataset, CISS-REC, for physically grounded traffic accident reconstruction from public reports, improving accuracy and scalability.

Contribution

It formulates accident reconstruction as a parameterized multimodal learning problem and creates the CISS-REC dataset, enabling scalable, verifiable accident analysis.

Findings

Outperforms baselines on CISS-REC with higher reconstruction fidelity.

Achieves improved accident point accuracy and collision consistency.

Demonstrates public reports can be used for scalable accident reconstruction.

Abstract

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.