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@article{177454,
        author = {Satyam Kumar and Moksha Bhandari and Nandini Rathod and Ishra Fathima and Tirth Lalani and Dr. Jeeva Selvaraj},
        title = {Multimodal Driver Drowsiness Detection System Using Deep Learning and Sensor Fusion},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1861-1869},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177454},
        abstract = {Driver fatigue is one of the leading causes of road accidents globally, often resulting in serious injuries or fatalities. This paper presents a real-time drowsiness detection system designed to monitor driver alertness and issue timely warnings to prevent accidents. The system utilizes computer vision techniques based on Python, OpenCV, and dlib to analyze facial features, particularly eye movement and blink duration. A convolutional neural network (MobileNet-SSD) is employed to accurately detect closed-eye states from a live video stream captured via webcam. The eye aspect ratio (EAR) is calculated to differentiate between normal blinking and prolonged eye closure, a key indicator of fatigue. Upon detecting signs of drowsiness, an auditory alarm is triggered using a Raspberry Pi, and provisions are made to interface with vehicle braking systems. The system is lightweight, cost-effective, and capable of running efficiently on embedded platforms, making it a practical solution for enhancing driver safety in real-world conditions. Extensive testing under varied lighting and user scenarios demonstrates its reliability and responsiveness, paving the way for future integration into commercial vehicle systems.},
        keywords = {Driver drowsiness detection, computer vision, steering behavior analysis, sensor fusion, deep learning.},
        month = {May},
        }