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.

翻译：交通事故分析对于提升公共安全和制定交通法规至关重要。传统方法虽被广泛应用，但常受限于人工分析流程、主观决策、单模态输出，以及与敏感数据相关的隐私问题。本文提出AccidentGPT的概念——一种交通事故分析的基础模型，该模型融合多模态输入数据，可自动重构包含动力学细节的事故过程视频，并进一步提供多任务分析与多模态输出。AccidentGPT的设计具备以下能力：结合反馈的多模态提示以实现面向任务的适应性、利用标注与非标注数据的混合训练方案，以及保障数据隐私的边缘-云分割配置。为充分实现该模型的功能，我们提出了若干研究机遇。本文旨在填补传统交通事故分析方法的不足，并引导研究界关注自动化、客观化且保护隐私的交通事故分析。