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@article{197454,
        author = {Swati Shinde and Vaishnavi Patil and Chaitanya salunke and Neha Patil},
        title = {An Intelligent Driver Safety System Using Raspberry Pi for Drowsiness and Alcohol Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7598-7603},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197454},
        abstract = {In the world, Road traffic accidents caused due to driver fatigue and alcohol intoxication continue to be a major concern that results in significant loss. The increased alcohol intake by people, which negatively impacts judgment, coordination, and vehicle control, while fatigue of the driver is known to increase the reaction time. This paper presents the design and implementation of a low-cost intelligent driver safety system using a Raspberry Pi edge computing platform. The proposed system integrates vision-based drowsiness detection, alcohol detection, and driving behaviour monitoring to identify unsafe driving conditions in real time. The eye closure behaviour is analysed through the Eye Aspect Ratio (EAR) derived from facial landmarks. The alcohol presence is detected through an MQ-3 alcohol sensor. Once the alcohol level is found to be greater than a predetermined threshold level, the vehicle’s ignition is prevented through a relay. Experimental results show that the system is able to detect the driver’s unsafe behaviour with a reduced latency period.},
        keywords = {Driver Monitoring System, Internet of Things (IoT), Raspberry Pi, Computer Vision, Drowsiness Detection, Alcohol Detection, Edge Computing, Intelligent Transportation Systems},
        month = {April},
        }