Virtual Booth

Traffic congestion at road intersections often causes significant delays for emergency

vehicles, particularly ambulances, which can negatively impact response time and patient

outcomes. This project presents the design and development of an IoT-based smart traffic

light system with AI-assisted ambulance detection to prioritize emergency vehicles at

signalized intersections. The system utilizes an ESP32-CAM module to capture real-time

traffic images and applies an AI object detection model trained using the Roboflow 3.0

Object Detection framework to identify ambulances accurately.

Upon detecting an ambulance, the ESP32 microcontroller automatically overrides the

normal traffic signal sequence and provides a green light to the corresponding lane while

switching other lanes to red, ensuring a safe and unobstructed path. A safe transition

mechanism using a yellow-light phase is implemented to enhance road user safety. The

system is integrated with the Blynk IoT platform for real-time monitoring, status

visualization, and manual override capability.

Experimental results indicate that the proposed system operates reliably in real-time

conditions, successfully detecting ambulances and dynamically controlling traffic signals.

This approach demonstrates a cost-effective and scalable solution for intelligent traffic

management systems and has strong potential for deployment in smart city applications.

Keywords; Smart Traffic Light, Ambulance Detection, ESP32-CAM, Internet of Things (IoT),

Roboflow Object Detection, Emergency Vehicle Priority