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.

交通事故分析是提升公共安全和制定道路规章的关键。本文介绍了 AccidentGPT 的概念，它是一个交通事故分析的基础模型，可以通过多模态输入数据自动重建事故过程视频，并提供多任务分析和多模态输出。AccidentGPT 的设计包括多模态输入、任务导向的自适应性、标记和未标记数据的混合训练模式以及边缘 - 云拆分配置，以实现数据隐私保护。为了充分实现此模型的功能，我们提出了一些研究机会。本文作为填补传统交通事故分析方法的空白和引起研究界对自动、客观和隐私保护的交通事故分析的关注的基石。

事故 GPT：用于交通事故分析的大型多模态基础模型

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

In this age of information, images are a critical medium for storing and
transmitting information. With the rapid growth of image data amount, visual
compression and visual data perception are two important research topics
attracting a lot attention. However, those two topics are rarely discussed
together and follow separate research path. Due to the compact compressed
domain representation offered by learning-based image compression methods,
there exists possibility to have one stream targeting both efficient data
storage and compression, and machine perception tasks. In this paper, we
propose a layered generative image compression model achieving high human
vision-oriented image reconstructed quality, even at extreme compression
ratios. To obtain analysis efficiency and flexibility, a task-agnostic
learning-based compression model is proposed, which effectively supports
various compressed domain-based analytical tasks while reserves outstanding
reconstructed perceptual quality, compared with traditional and learning-based
codecs. In addition, joint optimization schedule is adopted to acquire best
balance point among compression ratio, reconstructed image quality, and
downstream perception performance. Experimental results verify that our
proposed compressed domain-based multi-task analysis method can achieve
comparable analysis results against the RGB image-based methods with up to
99.6% bit rate saving (i.e., compared with taking original RGB image as the
analysis model input). The practical ability of our model is further justified
from model size and information fidelity aspects.

本文介绍了一种层次生成式图像压缩模型，该模型支持多任务分析，通过学习感知图像压缩方法提供可压缩的紧凑表示，并实现高分辨率图像重建以及压缩率和感知性能的平衡。实验结果表明，该模型对于 RGB 基于方法可以降低 99.6% 的比特率并实现可比拟的分析结果。