Driver Monitoring

Driver monitoring systems aim to enhance road safety by detecting driver impairment, such as drowsiness or distraction, in real-time. Current research heavily utilizes computer vision techniques, including facial landmark detection and deep learning models like convolutional neural networks, to analyze video feeds for behavioral cues. Furthermore, research explores the integration of physiological sensors (e.g., ECG, EMG, GSR) and federated learning approaches to improve accuracy and address data privacy concerns. These advancements hold significant potential for improving road safety and informing the design of safer, more adaptive human-machine interfaces in vehicles.