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@article{173675,
        author = {Allaka Durga Venkatesh and Dude Naga venkata Satyanarayana and Ganipisetty Sai Ganesh and Petrum Sandhya Pravallika and Banda Srinivas Raja},
        title = {Vision-Based Anti-Collision System For Autonomous Vehicles In Toxic Gas Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1062-1067},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173675},
        abstract = {The work signifies a Vision-Based Anti-Collision System for Autonomous Vehicles in Toxic Gas Environments focuses on enhancing the safety and operational efficiency of autonomous vehicles in hazardous conditions. It integrates computer vision, machine learning, and sensor-based technologies to navigate complex paths while detecting obstacles and monitoring air quality. A Raspberry Pi microcontroller processes video captured by a USB web camera and displays it on an HTML interface for real-time monitoring. The ultrasonic sensor detects obstacles, triggering alerts and ensuring collision prevention, while the MQ4 gas sensor continuously monitors the environment for toxic gases, uploading air quality data to the web page. The system employs a Random Forest machine learning algorithm to analyze environmental data, improving gas detection accuracy and alert generation. A buzzer provides audible warnings in abnormal conditions. The robot platform, powered by DC motors and controlled by a motor driver, enables autonomous movement. This comprehensive solution is designed to facilitate safe navigation in environments where toxic gases and physical obstacles pose significant risks.},
        keywords = {Autonomous Vehicles, Anti-Collision System, Random Forest Algorithm.},
        month = {March},
        }