Drowsy Driver Detection Using Deep Learning and Multi-Sensor Data Fusion

We develop a drowsy driver detection system using visual and radar sensors combined with machine learning. Our aim is to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera, and a micro-Doppler radar sensor, our system offers high reliability over 95% accuracy in drowsy driver detection. Through data fusion and deep learning, the ability to quickly analyze and classify driver’s behavior under various conditions such as lighting and facial expression in a real-time monitoring system is achieved.