A Robust Driver Distraction Estimation Technique for ADAS Applications

Road accidents account for significant economic and personal costs, and the cognitive state of the driver is one of its major causes. We propose a novel approach for a driver monitoring system (DMS) to detect the cognitive state of the driver in real time using object detection. For this, we have used data from 19 different drivers in diverse traffic conditions in Bengaluru with over 7 h of driving time. We have chosen the YOLOv5 algorithm for our classification model with three categories, namely focused, sleepy (eyes closed or yawning), and distracted (looking away from the road). A magnetometer integrated with this model effectively categorizes distracted head turns while discerning them from deliberate and desirable head movements; this improves the robustness of classification. Velocity is computed using an onboard GPS unit, and the readings are used to determine if the vehicle is stationary in which case the detections are ignored. Experiments conducted on 19 people showed that our system has an average accuracy of 96.90% for this three-class classification model.