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[2511.10853] Advanced Assistance for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction
Summary
This article presents an AI-driven multi-agent framework for reconstructing traffic crash scenarios, enhancing the accuracy of pre-crash analysis through a two-phase collaborative approach.
Why It Matters
With traffic collisions being a significant concern for public safety, this research introduces an innovative AI solution that improves the accuracy and efficiency of crash reconstructions. By leveraging advanced machine learning techniques, it can assist investigators in understanding complex crash dynamics, ultimately aiding in accident prevention and policy formulation.
Key Takeaways
- The framework achieves 100% accuracy in reconstructing pre-crash scenarios from fragmented data.
- Utilizes a two-phase process combining natural language processing and event data for enhanced analysis.
- Demonstrates effectiveness even with incomplete data inputs, showcasing robustness in real-world applications.
- Research analysts without prior training achieved over 92% accuracy, indicating the system's user-friendliness.
- The study emphasizes the importance of structured reasoning in improving model performance.
Computer Science > Artificial Intelligence arXiv:2511.10853 (cs) [Submitted on 13 Nov 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Advanced Assistance for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction Authors:Gerui Xu, Boyou Chen, Huizhong Guo, Dave LeBlanc, Arpan Kusari, Efe Yarbasi, Ananna Ahmed, Zhaonan Sun, Shan Bao View a PDF of the paper titled Advanced Assistance for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction, by Gerui Xu and 8 other authors View PDF Abstract:Traffic collision reconstruction traditionally relies on human expertise and can be accurate, but pre-crash reconstruction is more challenging. This study develops a multi-agent AI framework that reconstructs pre-crash scenarios and infers vehicle behaviors from fragmented collision data. We propose a two-phase collaborative framework with reconstruction and reasoning stages. The system processes 277 rear-end lead vehicle deceleration (LVD) crashes from the Crash Investigation Sampling System (CISS, 2017 to 2022), integrating narrative reports, structured tabular variables, and scene diagrams. Phase I generates natural-language crash reconstructions from multimodal inputs. Phase II combines these reconstructions with Event Data Recorder (EDR) signals to (1) identify striking and struck vehicles and (2) isolate the EDR records most relevant to the collision moment, enabling inference of key pre-crash behaviors. Fo...