RAPID: Recognition of Any-Possible DrIver Distraction via Multi-view Pose Generation Models

Driver distraction remains a pressing traffic safety issue. Drivers are often careless with their distraction behaviours, which may cause serious traffic accidents. However, current Driver Monitoring Systems (DMS) cannot be put into practical application well, which tend to have high latency, lack precision, and are unable to cover all distraction behaviours. In this paper, we assume driver distraction to be a One-Class Classification (OCC) problem and build an unsupervised learning baseline based on denoising diffusion probabilistic models (DDPM) called RAPID which aggregates future patterns generated by the diffusion process to detect distraction, considering the diversity of normal and abnormal situations. Besides, we propose a skeleton-based synchronized multi-view dataset with diverse distraction behaviours called sktDD (skeleton-based Driver Distraction dataset) to improve on existing datasets. RAPID facilitates a frame-level (0.03 second) and undefined prediction with AUC score beyond State-of-the-Art (SOTA) methods, surpassing currently typical DMS that rely on post-processing procedures and predefined actions. RAPID has the potential to bring significant advancements in the field of traffic safety, which can also be applied in future self-driving scenarios to determine whether the remote-driving operator’s current state is suitable to take over. Our dataset and code are available at https://github.com/jingyulei/rapid.