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AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis

Kebin Wu, Wenbin Li, Xiaofei Xiao

TL;DR

AccidentGPT introduces a multi-modal foundation approach to traffic accident analysis, addressing the limitations of manual, uni-modal, and privacy-sensitive methods. It enables automatic reconstruction of accident processes through video and vehicle dynamics while delivering multi-task outputs for safety, regulatory, and response applications. The architecture features edge-device preprocessing with cloud-based decoding, cross-modal alignment and fusion, and a hybrid training regime that leverages labeled and unlabeled data. The paper outlines six research opportunities—data integration, model structure, multi-modal reasoning, data-efficient training, prompting with feedback, and validation metrics—advancing toward objective, privacy-preserving, and scalable accident analysis.

Abstract

Traffic accident analysis is pivotal for enhancing public safety and developing road regulations. Traditional approaches, although widely used, are often constrained by manual analysis processes, subjective decisions, uni-modal outputs, as well as privacy issues related to sensitive data. This paper introduces the idea of AccidentGPT, a foundation model of traffic accident analysis, which incorporates multi-modal input data to automatically reconstruct the accident process video with dynamics details, and furthermore provide multi-task analysis with multi-modal outputs. The design of the AccidentGPT is empowered with a multi-modality prompt with feedback for task-oriented adaptability, a hybrid training schema to leverage labelled and unlabelled data, and a edge-cloud split configuration for data privacy. To fully realize the functionalities of this model, we proposes several research opportunities. This paper serves as the stepping stone to fill the gaps in traditional approaches of traffic accident analysis and attract the research community attention for automatic, objective, and privacy-preserving traffic accident analysis.

AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis

TL;DR

AccidentGPT introduces a multi-modal foundation approach to traffic accident analysis, addressing the limitations of manual, uni-modal, and privacy-sensitive methods. It enables automatic reconstruction of accident processes through video and vehicle dynamics while delivering multi-task outputs for safety, regulatory, and response applications. The architecture features edge-device preprocessing with cloud-based decoding, cross-modal alignment and fusion, and a hybrid training regime that leverages labeled and unlabeled data. The paper outlines six research opportunities—data integration, model structure, multi-modal reasoning, data-efficient training, prompting with feedback, and validation metrics—advancing toward objective, privacy-preserving, and scalable accident analysis.

Abstract

Traffic accident analysis is pivotal for enhancing public safety and developing road regulations. Traditional approaches, although widely used, are often constrained by manual analysis processes, subjective decisions, uni-modal outputs, as well as privacy issues related to sensitive data. This paper introduces the idea of AccidentGPT, a foundation model of traffic accident analysis, which incorporates multi-modal input data to automatically reconstruct the accident process video with dynamics details, and furthermore provide multi-task analysis with multi-modal outputs. The design of the AccidentGPT is empowered with a multi-modality prompt with feedback for task-oriented adaptability, a hybrid training schema to leverage labelled and unlabelled data, and a edge-cloud split configuration for data privacy. To fully realize the functionalities of this model, we proposes several research opportunities. This paper serves as the stepping stone to fill the gaps in traditional approaches of traffic accident analysis and attract the research community attention for automatic, objective, and privacy-preserving traffic accident analysis.
Paper Structure (13 sections, 2 figures)

This paper contains 13 sections, 2 figures.

Table of Contents

  1. INTRODUCTION
  2. GAPS IN CURRENT TRAFFIC ACCIDENT ANALYSIS
  3. Data Integration and Analysis
  4. Model Modality and Generalization
  5. ACCIDENTGPT OVERVIEW
  6. RESEARCH OPPORTUNITIES
  7. Opportunity 1: Multi-Modal Traffic Data Collection and Integration
  8. Opportunity 2: Multi-Modal Model Structure and Core Components
  9. Opportunity 3: Multi-Modal Reasoning
  10. Opportunity 4: Data Efficient Training Paradigm
  11. Opportunity 5: Task-Oriented Multi-Modal Prompt with Feedback
  12. Opportunity 6: Validation Methods and Reliability Metrics
  13. Conclusion

Figures (2)

  • Figure 1: AccidentGPT Overview.
  • Figure 2: Framework for Pretraining.