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

Kebin Wu, Wenbin Li, Xiaofei Xiao

2024

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 unla-belled 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’s attention for automatic, objective, and privacy-preserving traffic accident analysis.

Paper Citation

in Harvard Style

Wu K., Li W. and Xiao X. (2024). AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 943-950. DOI: 10.5220/0012422100003636

in Bibtex Style

@conference{icaart24,
author={Kebin Wu and Wenbin Li and Xiaofei Xiao},
title={AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={943-950},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012422100003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis
SN - 978-989-758-680-4
AU - Wu K.
AU - Li W.
AU - Xiao X.
PY - 2024
SP - 943
EP - 950
DO - 10.5220/0012422100003636
PB - SciTePress