A Driver Activity Dataset with Multiple RGB-D Cameras and mmWave Radars

Driver activity recognition has become crucial for intelligent transportation and automotive safety systems. However, existing studies mainly focus on fatigue-related behaviors while neglecting other activities for analyzing driver behavior and intent. In this work, we introduce a novel dataset for fine-grained driver activities, utilizing diverse sensors such as mmWave radars, RGB, and depth cameras, each of which includes three camera angles: body, face, and hands. This multi-modal and multi-angle approach allows for comprehensive driver behavior analysis, including hand gestures, head movement, and object interactions. Moreover, including mmWave radars provides significant privacy advantages, as the sparse dynamic point clouds prevent the identification of the driver's face and other personal information. This dataset is valuable for researchers and developers on driver activity recognition and behavior analysis. It enables the development and evaluation of robust, privacy-conscious solutions for improving road safety, driver assistance, and in-vehicle interaction. Furthermore, the multi-modal nature of the data enables the exploration of sensor fusion techniques, unlocking the full potential of diverse sensing modalities to understand complex driver behaviors.