CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis

Nishq Poorav Desai, Ali Etemad, Michael Greenspan; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6688-6698

Self-driving research often underrepresents cyclist collisions and safety. To address this we present CycleCrash a novel dataset consisting of 3000 dashcam videos with 436347 frames that capture cyclists in a range of critical situations from collisions to safe interactions. This dataset enables 9 different cyclist collision prediction and classification tasks focusing on potentially hazardous conditions for cyclists and is annotated with collision-related cyclistrelated and scene-related labels. Next we propose Vid- NeXt a novel method that leverages a ConvNeXt spatial encoder and a non-stationary transformer to capture the temporal dynamics of videos for the tasks defined in our dataset. To demonstrate the effectiveness of our method and create additional baselines on CycleCrash we apply and compare 7 models along with a detailed ablation. We release the dataset and code at https://github.com/ DeSinister/CycleCrash/.