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@article{185769,
        author = {Manasarani N R and Dr. Zuhaib Baig and Savitri Shrishail Hasaraddi and Aruna H S and Shambhu H S},
        title = {Smart Vehicle Accident Detection And Alert System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {3597-3600},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185769},
        abstract = {Road accidents remain a leading cause of injuries and fatalities worldwide, frequently caused by driver drowsiness, alcohol consumption, or delayed emergency response. This paper presents a comprehensive, cost-effective, and intelligent safety system designed to minimize road accidents and provide immediate alerts in case of collisions. The proposed system integrates multiple modules Drowsiness Detection, Alcohol Detection, Accident Detection, GPS, GSM, and Motor Speed Control using both computer vision and embedded hardware. The laptop-based module utilizes facial landmark recognition via the Dlib library to compute the Eye Aspect Ratio (EAR), thereby identifying driver fatigue in real time. Concurrently, the Arduino Uno microcontroller handles sensor data from MQ-3 (alcohol), MPU6050 (accelerometer and gyroscope), GPS, and GSM modules to perform realtime safety monitoring. If drowsiness or alcohol levels are detected beyond threshold limits, the system triggers
alerts through a buzzer and reduces vehicle speed. Upon collision detection, GPS coordinates are automatically sent via GSM to emergency contacts. The proposed
prototype demonstrates a reliable, efficient, and affordable model to improve driver and passenger safety.},
        keywords = {Smart Vehicle, Accident Detection, Drowsiness Detection, Alcohol Sensor, GPS, GSM, Arduino Uno, IoT, Safety System, Road Accident Prevention.},
        month = {October},
        }