Sustainable Real-Time Driver Gaze Monitoring for Enhancing Autonomous Vehicle Safety

Abstract

Despite advances in autonomous driving technology, current systems still require drivers to remain alert at all times. These systems issue warnings regardless of whether the driver is actually gazing at the road, which can lead to driver fatigue and reduced responsiveness over time, ultimately compromising safety. This paper proposes a sustainable real-time driver gaze monitoring method to enhance the safety and reliability of autonomous vehicles. The method uses a YOLOX-based face detector to detect the driver’s face and facial features, analyzing their size, position, shape, and orientation to determine whether the driver is gazing forward. By accurately assessing the driver’s gaze direction, the method adjusts the intensity and frequency of alerts, helping to reduce unnecessary warnings and improve overall driving safety. Experimental results demonstrate that the proposed method achieves a gaze classification accuracy of 97.3% and operates robustly in real-time under diverse environmental conditions, including both day and night. These results suggest that the proposed method can be effectively integrated into Level 3 and higher autonomous driving systems, where monitoring driver attention remains critical for safe operation.