A Driver Fatigue Detection Scheme Using 3-D mmWave Imaging Radar

Driver fatigue is a critical factor in road accidents, often resulting in severe consequences due to delayed reaction times and impaired decision-making. Traditional fatigue detection methods, such as camera-based systems, have significant challenges related to intrusiveness, privacy concerns, and reliability under varying environmental conditions, associated with them. This paper introduces an innovative driver fatigue detection system, 3D-DFD, which leverages advanced 3D millimeter-wave (mmWave) imaging radar and artificial intelligence algorithms for driver fatigue detection. By monitoring physiological and behavioral indicators, such as normal posture, yawning, nodding, and rapid blinking, using high-resolution 3D radar imagery, we enable non-invasive and privacy-preserving detection. The integration of 3D radar enhances spatial feature extraction, providing robust classification across a wide range of diverse detection scenarios. The system demonstrates an average accuracy of 93.16%, with precision rates of 92.5% for yawning, 94.2% for nodding, and 93.8% for rapid blinking based on testing with 19 volunteers across three different scenarios, showcasing its effectiveness and reliability. This research underscores the potential of 3D mmWave radar technology in driver fatigue detection and lays a strong foundation for safer and more intelligent automotive systems.