Development & Testing of a Camera-Based Driver Monitoring System

<div class="section abstract"><div class="htmlview paragraph">One of the primary reasons for road accidents is driving while distracted or drowsy. Often, long and monotonous road journeys lead to distracted or drowsy driving. Therefore, there is a need for a system which alerts a distracted or drowsy driver. Moreover, as the levels of autonomy move beyond SAE Level 2, the system assumes a larger share of the dynamic driving task. Under challenging circumstances, the system might ask the driver to take back vehicle control. To guarantee safety, it’s crucial to monitor the driver’s condition in order to assess their readiness to regain control of the vehicle. An advanced safety feature known as a driver monitoring system (DMS), sometimes referred to as a driver state sensing (DSS) system, is designed to monitor a driver’s attentiveness and alertness, providing warnings or alerts to refocus their attention on driving when drowsiness or distraction is detected. This paper presents a novel camera-based driver drowsiness system developed using a standard webcam and an embedded hardware. This system utilizes deep learning for tasks such as detecting faces, extracting facial landmarks, and estimating head pose. Key facial landmarks are used to determine blink rate and track eye gaze. To improve robustness against brightness changes, the software also implements an auto calibration feature. The system uses multiprocessing capabilities of the embedded hardware for improved performance. The system was tested for functionality and performance inside the lab and on a real vehicle.</div></div>