Abstract: Driver fatigue is a well-documented risk factor for road accidents worldwide. Prolonged driving hours and inadequate rest significantly impact a driver's cognitive alertness, leading to a decline in reaction time and decision-making ability. This study provides an extensive review of current advancements in intelligent anti-sleep eyewear designed to detect and mitigate drowsiness in drivers. The paper examines key technologies such as Electroencephalography (EEG), Electrooculography (EOG), Photoplethysmography (PPG), eye-tracking, and head pose estimation, highlighting their efficacy, limitations, and feasibility for real-time application. Additionally, machine learning techniques for data analysis and classification are explored, focusing on their role in improving detection accuracy. The review further outlines future directions in multimodal sensing integration, adaptive learning algorithms, and human factors research to enhance the reliability and usability of these systems. Rigorous testing and real-world implementation remain crucial for ensuring the efficacy of these solutions in practical driving environments.
Keywords: Driver fatigue, anti-sleep eyewear, drowsiness detection, physiological signals, machine learning, wearable sensors, road safety.
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DOI:
10.17148/IJARCCE.2025.14223
