AccidentX: A Large-Scale Multimodal BEV Dataset for Traffic Accident Analysis and Prevention

Abstract

With the rapid development and widespread application of autonomous driving technology, the accurate analysis and prevention of traffic accidents have become critical challenges. However, current traffic accident datasets are often constrained by limited scale and diversity, impeding progress in this field. To address these limitations, we introduce AccidentX, a large-scale multimodal dataset specifically curated for comprehensive traffic accident analysis and prevention. Our AccidentX comprises over 10,000 bird’s-eye view (BEV) videos generated using the CARLA simulator, with detailed annotations covering a wide range of traffic scenarios. In comparison to existing datasets such as nuScenes, our AccidentX offers seven times more video frames and leverages Vision-Language Models (VLMs) and GPT-4o for enhanced scene understanding and decision-making. We also establish a benchmark for state-of-the-art Multimodal Large Language Models (MLLMs) on AccidentX, fostering further research and innovation within the community. AccidentX will be made available as a fully open source resource for the advancement of the autonomous driving safety algorithm community.