Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{160577,
        author = {Darshan Biradar and Devang Bissa and Sarthak Biyani and Bilal Khan and Pratham Bisen and Saif Bichu and Vikas Nandeshwar and Surabhi Kakade},
        title = {AI Based Sleep Detection System For Enhanced Road Safety},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {1},
        pages = {891-893},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160577},
        abstract = {Main causes of traffic accidents include driver fatigue and drowsiness. Across the globe, increasing the number of accidents and injuries each year is due to one of the causes known as driver’s drowsiness. The Advanced Driving Assistance System (ADAS) module described in this study deals with automatic driver sleepiness detection based on optical data and artificial intelligence. This approach seeks to improve transportation safety by reducing the number of accidents brought on by fatigued drivers. We suggest an algorithm to find, follow, and examine the driver's face and eyes in order to evaluate PERCLOS, a sleepiness indicator linked to sluggish eye closure that has scientific validity. One of the main tool used here in the project is Open Computer Vision with the help of python which provides a basic platform for the work to be done.},
        keywords = {Artificial Intelligence, Drowsiness Detection, Machine Learning, Open Computer Vision},
        month = {},
        }