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@article{177114,
        author = {Akshay Shingade and Manjiri  Pawar and Aqdas Usmani and Ansh Thakre and Aniket Bhange and Charan Pote},
        title = {Driver Drowsiness Detection System Software : An Auto Emergency},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2318-2321},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177114},
        abstract = {Drowsy driving is one of the major causes of road accidents worldwide, leading to severe injuries and fatalities. Traditional methods of driver monitoring rely on manual observation, which is ineffective and impractical for real-time detection. To address this, an AI-powered driver drowsiness detection system is proposed, leveraging computer vision, deep learning, and image processing to identify signs of driver fatigue and alert them in real time.The system captures facial images using a camera module and processes them through a convolutional neural network (CNN) model trained to detect early signs of drowsiness, such as eye closure duration, yawning, and head position. Upon detecting drowsiness, an alert mechanism, including audible alarms and dashboard notifications, is activated to ensure immediate corrective action.The proposed model has been tested on standard drowsiness datasets, achieving high accuracy in fatigue detection and rapid response times. Real-world testing demonstrates that the system can effectively reduce the risk of drowsy driving accidents by providing timely alerts. Future advancements will incorporate multi-modal analysis, combining physiological signals with facial recognition for enhanced reliability.},
        keywords = {AI, Computer Vision, Driver Drowsiness Detection, Deep Learning, Image Processing, Road Safety},
        month = {May},
        }