Dual-Modal Drowsiness Detection to Enhance Driver Safety

In the modern world, the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems. This study aims to enhance road safety by proposing a dual-modal solution for detecting driver drowsiness, which combines heart rate monitoring and face recognition technologies. The research objectives include developing a non-contact method for detecting driver drowsiness, training and assessing the proposed system using pre-trained machine learning models, and implementing a real-time alert feature to trigger warnings when drowsiness is detected. Deep learning models based on convolutional neural networks (CNNs), including ResNet and DenseNet, were trained and evaluated. The CNN model emerged as the top performer compared to ResNet50, ResNet152v2, and DenseNet. Laboratory tests, employing different camera angles using Logitech BRIO 4K Ultra HD Pro Stream webcam produces accurate face recognition and heart rate monitoring. Real-world vehicle tests involved six participants and showcased the system’s stability in calculating heart rates and its ability to correlate lower heart rates with drowsiness. The incorporation of heart rate and face recognition technologies underscores the effectiveness of the proposed system in enhancing road safety and mitigating the risks associated with drowsy driving.