Intelligent Traffic Signal Prioritization for Emergency Vehicle Diversion in Urban Environments using Multi-modal Deep Learning

This research work introduces an innovative approach for diverting traffic in urban areas by prioritizing traffic signals for emergency vehicles. The proposed method leverages deep learning techniques to identify and prioritize emergency vehicles, thereby enabling the manipulation of signal sequences at local traffic intersections along their path. The identification of emergency vehicles is achieved through the integration of multiple artificial intelligence(AI)-based algorithms, combining audio and video data analysis to enhance accuracy and reliability in urban settings. This research contributes to the enhancement of urban traffic management, reducing response times for emergency services, and improving overall road safety.