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

Traffic accidents, being a significant contributor to both human casualties
and property damage, have long been a focal point of research for many scholars
in the field of traffic safety. However, previous studies, whether focusing on
static environmental assessments or dynamic driving analyses, as well as
pre-accident predictions or post-accident rule analyses, have typically been
conducted in isolation. There has been a lack of an effective framework for
developing a comprehensive understanding and application of traffic safety. To
address this gap, this paper introduces AccidentGPT, a comprehensive accident
analysis and prevention multi-modal large model. AccidentGPT establishes a
multi-modal information interaction framework grounded in multi-sensor
perception, thereby enabling a holistic approach to accident analysis and
prevention in the field of traffic safety. Specifically, our capabilities can
be categorized as follows: for autonomous driving vehicles, we provide
comprehensive environmental perception and understanding to control the vehicle
and avoid collisions. For human-driven vehicles, we offer proactive long-range
safety warnings and blind-spot alerts while also providing safety driving
recommendations and behavioral norms through human-machine dialogue and
interaction. Additionally, for traffic police and management agencies, our
framework supports intelligent and real-time analysis of traffic safety,
encompassing pedestrian, vehicles, roads, and the environment through
collaborative perception from multiple vehicles and road testing devices. The
system is also capable of providing a thorough analysis of accident causes and
liability after vehicle collisions. Our framework stands as the first large
model to integrate comprehensive scene understanding into traffic safety
studies.

交通事故一直是交通安全领域的研究重点，然而以往的研究往往孤立进行，缺少综合性的交通安全分析与预防框架。为了填补这一空白，本研究引入了 AccidentGPT，这是一个综合事故分析和预防的多模态大型模型，通过多传感器感知提供全面的事故分析和预防方法，支持自动驾驶车辆、人驾驶车辆和交通管理机构的安全需求。该模型首次将全面场景理解融入交通安全研究。