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.

翻译：交通事故作为造成人员伤亡与财产损失的重要因素，长期以来一直是交通安全领域众多学者关注的焦点。然而，既有研究无论是聚焦于静态环境评估还是动态驾驶分析，无论是事故前预测还是事故后归因分析，通常均孤立展开，缺乏一个能够实现对交通安全全面认知与应用的有效框架。为填补这一空白，本文提出AccidentGPT——一个面向事故分析与预防的综合多模态大模型。AccidentGPT构建了基于多传感器感知的多模态信息交互框架，从而在交通安全领域实现了对事故分析与预防的整体性方法。具体而言，我们的能力可归纳如下：对于自动驾驶车辆，我们提供全面的环境感知与理解，以控制车辆并避免碰撞；对于人类驾驶车辆，我们提供主动式远距离安全预警与盲区警报，同时通过人机对话与交互提供安全驾驶建议与行为规范。此外，针对交通警察与管理部门，我们的框架通过多车辆与路测设备的协同感知，支持对行人、车辆、道路及环境的智能化实时交通安全分析；同时，该系统还能在车辆碰撞后提供事故原因与责任的全面分析。本框架是首个将全场景理解融入交通安全研究的综合大模型。