Smart Traffic Control for Emergency Vehicles Prioritization using Video and Audio Processing

Meeting gridlocks at busy traffic signals is major obstacle for Emergency Vehicles (EmV). High priority vehicles like ambulances and fire trucks are made to snag through bottleneck traffic. It is crucial to detect their arrival at traffic signal and clear the traffic in that particular lane. In this paper, a solution for easing the movement of EmVs and prioritizing them in congested traffic lanes is addressed using YOLO V5 for video detection, and VGGNet architecture with Linear classifier model in audio detection. Though there are few existing solutions which solve this issue to some extent, none of them addressed the major problem of prioritization. Prioritizing the fastest EmV by detecting its speed using image processing, when multiple EmVs are encountered at a single traffic signal is a unique feature of the proposed work. The proposed model was tested with real-time videos, demonstrating improved performance when compared to existing solutions.