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 article introduces an innovative driver fatigue detection system, 3D-DFD, which leverages advanced 3-D 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 3-D radar imagery, we enable noninvasive and privacy-preserving detection. The integration of 3-D 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 3-D mmWave radar technology in driver fatigue detection and lays a strong foundation for safer and more intelligent automotive systems.