  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
