Publication Details

Abstract

This project presents a Vision-Enhanced Traffic Control System designed to provide rapid clearance for emergency vehicles such as ambulances. The system employs YOLOv8, a deep learning-based object detection algorithm, trained on a custom dataset annotated using LabelImg, to accurately identify ambulances in real time. Upon detection, the system communicates with traffic signals through Zigbee modules and Arduino, automatically granting a green signal in the direction of the emergency vehicle. Built on an embedded system architecture, the design is non-invasive, scalable, and adaptable to various traffic environments. By integrating artificial intelligence with traffic management, this project aims to reduce emergency response time, prevent traffic congestion, and enhance public safety, paving the way for smart and responsive urban infrastructure.