A Real-Time Vision Transformers-Based System for Enhanced Driver Drowsiness Detection and Vehicle Safety

Drowsy driving is a leading cause of fatal traffic accidents worldwide. Drowsy driving has emerged from modern societal trends such as long working hours, heavy reliance on vehicles, and insufficient sleep. Despite considerable efforts by researchers to develop efficient driver drowsiness detection systems, none so far has been widely adopted due to their high cost, intrusive nature, and ineffectiveness in challenging real-life situations. This paper presents a novel, real-time, non-intrusive, and cost-effective driver drowsiness detection system leveraging vision transformers (ViT). Our approach detects the driver’s face from each video frame and classifies the driver’s state as either ‘drowsy’ or ‘alert’ based on the entire facial image, as opposed to previous systems that rely on analyzing specific facial features. We demonstrate that the proposed Vision Transformers-based Driver Drowsiness Detection (ViT-DDD) system surpasses existing state-of-the-art methods, particularly in challenging scenarios such as drivers wearing glasses or sunglasses, or in different lighting conditions. The model was trained and evaluated on two widely used public drowsiness detection datasets, achieving classification accuracies of 98.89% on the NTHU-DDD dataset and 99.4% on the UTA-RLDD dataset. Furthermore, the system was successfully deployed on a Raspberry Pi microcomputer, integrated with an infrared camera, a GSM/GPS module, and a buzzer to alert the driver and report the drowsiness condition to the vehicle owner. Testing the prototype yielded highly promising results, with the system’s strong performance attributed to the ViT-DDD system and advanced hardware. The promising test results suggest the potential of this system in significantly reducing accidents caused by drowsy driving, with future work aiming to expand its capabilities and integration into broader vehicular systems.