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主要原因之一，长期以来一直是交通安全领域众多学者研究的焦点。然而，以往的研究，无论是侧重于静态环境评估还是动态驾驶分析，抑或是事故前预测或事故后规则分析，通常都是孤立进行的。缺乏一个有效的框架来全面理解及应用交通安全。为填补这一空白，本文引入了AccidentGPT，一个综合性的事故分析与预防多模态大模型。AccidentGPT构建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事故分析与预防的整体化方法。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，我们提供主动式的远程安全预警和盲区提醒，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，针对交警及管理机构，我们的框架通过多车辆和路侧测试设备的协同感知，支持对行人、车辆、道路及环境进行智能化、实时化的交通安全分析。该系统还能在车辆碰撞后提供事故原因和责任的全面分析。我们的框架是首个将综合场景理解整合到交通安全研究中的大模型。项目页面：https://accidentgpt.github.io