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@article{178390,
        author = {Aditya Pakde and Kishori Shekokar and Shubham Patel and Chhavi Prasad},
        title = {DRIVER DROWSINESS DETECTION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4748-4754},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178390},
        abstract = {Driver drowsiness is a major factor in road accidents, underscoring the critical need for efficient detection and prevention mechanisms. The author of this paper reviews various methods that researchers have explored to address this challenge. These include monitoring physiological data such as brain activity, heart rate, and skin responses, alongside computer vision techniques like facial landmark detection, eye aspect ratio calculation, and blink pattern analysis. Sensor fusion strategies incorporate touch sensors and facial feature extraction, while multimodal approaches combine facial analysis, hand gesture recognition, deep learning models, and physiological signals to enhance accuracy and minimize false alarms.
The paper also examines specific methods like respiratory rate variability, lane line detection, and deep learning approaches—including CNNs, ensemble models, and RNNs. Custom driver profiling, algorithm fusion, and the integration of contextual factors such as weather and vehicle state are explored to improve personalization and awareness. Innovative concepts, such as glass-mounted warning systems and explainable AI, are also discussed to enhance user experience and transparency. Furthermore, the paper looks ahead to future advancements, including integration with Advanced Driver Assistance Systems (ADAS) and adversarial networks, which may lead to more reliable drowsiness detection systems.
This work aims to present an effective driver drowsiness detection solution, building upon existing research and integrating multiple methodologies. The proposed system, in conjunction with a review of various approaches, seeks to contribute to improved road safety and a reduction in accidents caused by fatigue.},
        keywords = {Driver Drowsiness Detection, Computer Vision, Deep Learning, Multimodal Approaches, Road Safety.},
        month = {May},
        }