Driver Drowsiness Detection System

Abstract

Abstract: Drowsy driving poses a significant threat to road safety, necessitating the development of effective drowsiness detection systems. This research paper provides a thorough review of current technologies employed in drowsiness detection, encompassing image-based, physiological, and behavioural approaches. Evaluating the strengths and limitations of existing systems, the study identifies key challenges, including false positives and negatives, adaptability to diverse conditions, and integration complexities. Recent advancements, such as deep learning, sensor fusion, and real-time processing, are explored, offering insights into their impact on system accuracy and usability. The paper proposes hybrid approaches, personalized algorithms, and integration with smart infrastructure as potential enhancements. Through case studies, the effectiveness of drowsiness detection in real-world scenarios is highlighted, emphasizing the positive influence on road safety. The research concludes with a discussion on future directions, outlining emerging technologies and identifying research gaps. This paper aims to contribute to the ongoing evolution of drowsiness detection systems, fostering innovation for improved safety on the roads