Intelligent Traffic Light Controller for Emergency Vehicle Priority with Audio -Visual Recognition

Abstract

Integrating intelligent traffic management systems (ITMS) with emergency vehicle prioritization has proven effective in reducing response times and enhancing public safety. Studies have demonstrated that such systems significantly improve the efficiency of emergency medical services by ensuring ambulances navigate urban traffic more swiftly. In Coimbatore, the city police are actively working towards enhancing traffic management infrastructure. The planned implementation of an ITMS powered by a dedicated 350 km local area network (LAN) aims to track and regulate vehicle movement, enforce traffic rules, and improve road safety. This system includes the installation of 6,000 poles to support optical-fiber cables and 1,400 cameras with advanced facial recognition software. By adopting Convolutional Neural Network (CNN)-based vehicle classification and integrating intelligent traffic light systems, Coimbatore can further enhance the efficiency of emergency response operations. Such measures would ensure that ambulances and other emergency vehicles navigate intersections swiftly, ultimately saving lives and improving public health outcomes. Studies have demonstrated the efficacy of Convolutional Neural Networks (CNNs) in detecting emergency vehicles through real-time image processing. For instance, a system trained on a dataset of Indian ambulance images can identify approaching emergency vehicles and communicate with traffic light controllers to adjust signal timings, ensuring expedited passage. Integrating vehicle density estimation with AI allows for dynamic adjustment of traffic signal durations based on real time traffic volumes. This approach alleviates congestion and prioritizes emergency vehicles by modifying signal timings to facilitate their swift movement through intersections. Challenges and Future Directions Implementing AI-driven traffic light priority systems necessitates addressing challenges such as ensuring system reliability, managing integration with existing traffic infrastructure, and complying with regulatory standards. Additionally, ethical considerations like bias and discrimination, transparency, data privacy, and public acceptance must be carefully evaluated. The integration of technologies such as GPS, RFID, and IoT enabled devices has been shown to facilitate real-time tracking and priority routing of emergency vehicles. For instance, systems that adjust traffic signals based on the real-time location of ambulances have demonstrated improvements in response times. A study titled "Traffic Management for Emergency Vehicle Priority Based on Visual Sensing and MAC Protocol" presents an approach that combines distance measurement between emergency vehicles and intersections using visual sensing methods, vehicle counting, and time sensitive alert transmission within the sensor network. The experimental results have shown that the proposed system outperforms existing solutions in terms of average end-to- end delay, throughput, and energy consumption. Implementing these technologies in Coimbatore could lead to more efficient emergency services, reduced congestion, and enhanced public safety. Collaboration among city planners, law enforcement, and technology providers will be essential to develop and deploy these advanced traffic management solutions effectively.