Robust Deep Learning-Based Driver Distraction Detection and Classification

Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. In this paper, we propose a robust driver distraction detection system that extracts the driver's state from the recordings of an onboard camera using Deep Learning. We consider ten driving activities, which consist of one normal driving and nine distracted driving behaviors. Nine drivers were included in the experiments, and each one was asked to perform the ten activities in naturalistic and simulated driving situations. The main feature of the proposed solution is the extraction of the driver's body parts, using deep learning-based segmentation, before performing the distraction detection and classification task. Experimental results show that the segmentation module significantly improves the classification performance. The average accuracy of the proposed solution exceeds 96% on our dataset and 95% on the public AUC dataset.