Advanced Tool for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction

<div>Traffic collision reconstruction traditionally relies on human expertise and, when performed properly, can be incredibly accurate. However, attempting to perform pre-crash reconstruction, i.e., reconstructing the driver and vehicle behaviors that preceded the actual crash, poses significantly more challenges. This study develops a multi-agent artificial intelligence (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 (CISS; 2017–2022), 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 the temporal event data recorder (EDR). This enables precise identification of striking and struck vehicles while isolating the EDR records most relevant to the collision moment, thereby revealing crucial pre-crash driving behaviors. For validation, we applied it to all LVD cases, focusing on a subset of 39 complicated EDR cases where multiple EDR records per collision introduced possible ambiguity (e.g., due to missing or conflicting data). Ground truth was established via consensus between manual annotations (two independent researchers), with a separate large language model (LLM) used only to flag possible conflicts for re-checking.</div> <div>In the full end-to-end evaluation, the framework achieved 100% accuracy across all 4155 trials (277 cases × 5 runs × 3 models), with three reasoning models producing identical outputs, confirming that performance derives from the structured prompt design rather than model-specific characteristics. In contrast, research analysts without specialized reconstruction training achieved 92.31% accuracy on the same 39 complex cases. In separate ablation experiments on the 39 complicated EDR cases, where one randomly selected Phase I output from the full end-to-end evaluation was fixed as the unified input for Phase II and each model was tested with 10 independent runs, removing the structured reasoning anchors reduced case-level accuracy from 99.7% to 96.5%, with errors spreading from a single output type to multiple analytical dimensions. The system maintained robust performance even when processing incomplete data. This zero-shot evaluation, conducted without any domain-specific training or fine-tuning, demonstrates that the framework’s effectiveness stems from its multi-agent architecture and prompt engineering, offering a scalable approach for AI-assisted pre-crash analysis.</div>