Advanced Tool for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction
This provides a more precise tool for traffic safety analysis, though it appears incremental as it applies existing AI methods to a specific domain problem.
The study tackled the problem of inconsistent traffic collision reconstruction from incomplete multimodal data by developing a multi-agent AI framework that processes crash reports, tabular data, and visual diagrams. The framework achieved perfect accuracy in identifying relevant EDR events and distinguishing striking versus struck vehicles in 39 complex rear-end crashes, surpassing human researchers' 92% accuracy.
Traffic collision reconstruction traditionally relies on human expertise, often yielding inconsistent results when analyzing incomplete multimodal data. This study develops a multi-agent AI framework that reconstructs pre-crash scenarios and infers vehicle behaviors from fragmented collision data. We present a two-phase collaborative framework combining reconstruction and reasoning phases. The system processes 277 rear-end lead vehicle deceleration (LVD) collisions from the Crash Investigation Sampling System, integrating textual crash reports, structured tabular data, and visual scene diagrams. Phase I generates natural-language crash reconstructions from multimodal inputs. Phase II performs in-depth crash reasoning by combining these reconstructions with temporal Event Data Recorder (EDR).For validation, we applied it to all LVD cases, focusing on a subset of 39 complex crashes where multiple EDR records per collision introduced ambiguity (e.g., due to missing or conflicting data).The evaluation of the 39 LVD crash cases revealed our framework achieved perfect accuracy across all test cases, successfully identifying both the most relevant EDR event and correctly distinguishing striking versus struck vehicles, surpassing the 92% accuracy achieved by human researchers on the same challenging dataset. The system maintained robust performance even when processing incomplete data, including missing or erroneous EDR records and ambiguous scene diagrams. This study demonstrates superior AI capabilities in processing heterogeneous collision data, providing unprecedented precision in reconstructing impact dynamics and characterizing pre-crash behaviors.