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. Project page: https://accidentgpt.github.io

翻译：交通事故作为造成人员伤亡和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的研究焦点。然而，以往的研究，无论是关注静态环境评估还是动态驾驶分析，以及事故前预测还是事故后规则分析，通常都是孤立进行的，缺乏一个用于全面理解和应用交通安全的有效框架。为解决这一空白，本文提出了AccidentGPT，一个综合性的事故分析与预防多模态大模型。AccidentGPT建立了一个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全方位方法。具体而言，我们的能力可分为以下几类：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，我们提供主动的远程安全预警和盲点警报，同时通过人机对话与交互提供安全驾驶建议和行为规范；此外，对于交通警察和管理机构，我们的框架通过多车辆及道路测试设备的协同感知，支持对行人、车辆、道路及环境的智能化、实时交通安全分析——该系统还能够对车辆碰撞后的事故原因及责任进行详尽分析。我们的框架是首个将综合场景理解融入交通安全研究的大模型。项目页面：https://accidentgpt.github.io