AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident
  Analysis
Kebin Wu Wenbin Li Xiaofei Xiao
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.