AI-Powered Traffic Control for Emergency Response

An intelligent system that leverages computer vision and IoT to detect approaching ambulances, clear their path, and save critical time.

The Problem

In urban environments, traffic congestion is a major obstacle for emergency vehicles. Every second lost in transit can have life-threatening consequences. Traditional traffic systems lack the intelligence to detect and prioritize these vehicles automatically, leading to dangerous delays.

The Solution

This project provides a green corridor for ambulances. Using a camera feed, it identifies an ambulance up to 100 meters away and immediately instructs the traffic signal ahead to turn green, while turning other intersecting signals red. This ensures a seamless and efficient emergency route.

Key Features

Real-Time Ambulance Detection

Utilizes a camera and OpenCV to instantly identify ambulances with high accuracy.

Automatic Signal Control

An ESP32 microcontroller dynamically changes traffic lights to create a clear path.

LLM-Powered Reporting

Generates real-time incident summaries and predictive traffic flow analysis.

✨ Gemini AI Assistant Demo

This is a live demonstration of the LLM capabilities. Click the button to simulate an ambulance detection at a fictional intersection and see how the Gemini API generates an incident report and a traffic mitigation strategy in real-time.

Impact & Results

This system has the potential to make a significant real-world impact by drastically reducing ambulance travel times in congested areas.

Life-Saving Potential: Directly contributes to faster emergency response, improving patient outcomes.

Scalable Solution: The core technology can be adapted for other emergency vehicles.

Proven Detection Accuracy

Technology Stack

Python & OpenCV
Gemini API
ESP32 Microcontroller
IoT Protocols

My Role

  • Led end-to-end system design and hardware setup.
  • Developed the computer vision model for ambulance detection.
  • Integrated the Gemini API for intelligent reporting and automation.
  • Created the full-stack web dashboard for monitoring.