Abstract

Prioritizing and taking the necessary procedures to ensure human safety is critical. One of the many challenges we face in modern life is the increased risk of traffic congestion. Congestion on the streets may be problematic in large cities, especially for emergency vehicles such as fire engines and ambulances. Traffic delays are caused by a range of factors, including the weather, the time of day, the season, and unexpected occurrences such as accidents or construction projects. This creates delays in responding to emergencies, which leads to several deaths. A traffic control system limits the number of cars that can pass on a route without colliding with or impeding traffic flow. To minimize traffic congestion, a busier lane requires a longer green light than others. Systems with large traffic volumes require an adaptive system with traffic density control capabilities. A generic traffic control system may struggle to distinguish between emergencies and high-priority scenarios. This project proposes an intelligent traffic control system that prioritizes emergency vehicles utilizing artificial intelligence and image processing to address this issue. This allows these automobiles to drive through busy regions of traffic without stopping. The ESP32-CAM used in this project captures images of vehicles and then uses AI and an image processing-based model to detect emergency vehicles (Ambulances). If it identifies the presence of any emergency vehicle, the corresponding lane will be cleared till the vehicle passes the signal point. After the emergency vehicles pass the road, the system will return to normal mode.