A Trajectory-free Crash Detection Framework with Generative Approach and Segment Map Diffusion

Real - time crash detection is essential for developing proactive safety management strategy and enhancing overall traffic efficien cy . To address the limitations associated with trajectory acquisition and vehicle tracki ng, road segment maps recording the individual - level traffic dynamic data were di rectly served in crash detection. A novel two - stage trajectory - free crash detection framework, was present to generate the rational future road segment map and identify crashe s. The first - stage diffusion - based segment map generation model, Mapfusion, conducts a noisy - to - normal process that progressively adds noise to the road segment map until the map is corrupted to pure Gaussian noise. The denoising process is guided by seque ntial embedding c omponent s capturing the temporal dynamics of segment map sequence s . Furthermore, the generation model is designed to incorporate background context through ControlNet to enhance generation control. Crash detection is achieved by comparing the monitored segment map with the generat ions f rom diffusion model in second stage . Trained on non - crash vehicle motion data, Mapfusion successfully generates realistic road segment evolution maps based on learned motion patterns and remains robust across different sampling intervals . Experiments on real - world crashes indicate the effectiveness of the proposed two - stage method in accurately detecting crashes .