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@article{202081,
        author = {VIGNESH S and Dr. P. JANARTHANAN and KARUNAKARAN K},
        title = {AI-Based Intelligent Traffic Management System and Accident Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {5637-5645},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202081},
        abstract = {Fatigue, drowsiness, and sleepiness are often used interchangeably when describing the driving condition. It is multifaceted in nature, including many human variables, and has proven challenging for academics to describe throughout the last decades. Regardless of the ambiguity associated with tiredness, it is a significant element in driving safety. Fatigue is a major cause in road accidents globally, according to studies. It is especially important for occupational drivers, such as bus and heavy truck drivers, since they may be required to operate for an extended length of time during peak sleepiness times. Driver tiredness or sleepiness is a significant contributor to road accidents. Fatigue is a term that refers to a state of diminished ability, which may be physical or mental. While fatigue may be caused by a variety of external causes, sleepiness is a natural condition associated with the desire to sleep. As a result of the complexity and diversity of fatigue characteristics, identification may be more challenging. In this work, driver drowsiness is detected using eye and mouth detection with Euclidean distance measurement and SVM classifier},
        keywords = {Driver Drowsiness Detection System, Support Vector Machine, Machine Learning, Electroencephalogram, Electronic Control Unit, Internet of Things, Intraocular Pressure},
        month = {May},
        }