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@article{196639,
        author = {BANDARU GANESH and SANDEEP KUMAR and YOGONANDHA REDDY and Ms. C. Merlyne Sandra Christina},
        title = {A Vision-Based Deep Learning System for Traffic Signal Control with Emergency Vehicle Priority},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3951-3957},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196639},
        abstract = {Efficient traffic signal control is vital for smart city infrastructure, particularly in ensuring priority passage for emergency vehicles. This paper introduces a vision-based deep learning system that integrates real-time object detection with intelligent traffic management. Using convolutional neural networks (CNNs) and YOLO-based architectures [1], the system accurately identifies ambulances and fire trucks from live intersection video feeds under diverse traffic and environmental conditions. Once detected, the traffic control module dynamically overrides conventional cycles to establish a green corridor. To optimize route selection and minimize travel time,[2] the A* search algorithm is employed, enabling the system to compute the most efficient path for emergency vehicles through congested networks. Experimental evaluations demonstrate high detection accuracy, reduced response delays, and improved overall traffic flow compared to traditional sensor-based approaches. The integration of deep learning with A* pathfinding highlights the potential of scalable, vision-based solutions for enhancing emergency response and urban mobility in smart city environments.},
        keywords = {Traffic signal control; Emergency vehicle priority; Vision-based detection; Face detection; Convolutional Neural Network (CNN); Deep learning; YOLO object detection; A* algorithm.},
        month = {April},
        }