AmbuRouteAI: An AI-Based Traffic Management System for Emergency Vehicles
Mantresh Khurana is the Founder & CEO of Spyxpo, a visionary entrepreneur, and a full-stack developer with a passion for pushing the boundaries of technology. With expertise in web, mobile, desktop, and embedded systems, he excels at mastering emerging technologies that drive innovation. His work is defined by a commitment to developing cutting-edge solutions that transform industries and shape the future.
AmbuRouteAI: An AI-Based Traffic Management System for Emergency Vehicles
Traffic congestion is one of the biggest challenges emergency vehicles face in cities. Even a few minutes of delay can make a significant difference during medical emergencies. I built AmbuRouteAI as an experimental project to explore how AI can help manage traffic and give ambulances a clear path.
The project uses YOLOv8 for ambulance detection and OpenCV to simulate intelligent traffic light control. When an ambulance is detected from a live video feed, the system automatically changes the traffic signal to green, allowing it to pass through with minimal delay.
Features
Detects ambulances in real time using YOLOv8
Automatically changes traffic lights based on ambulance detection
Supports live webcam or CCTV video feeds
Built with Python and OpenCV
Can be extended for IoT-based smart traffic systems
Designed with future support for Google Maps route optimization
Tech Stack
Python
YOLOv8 (Ultralytics)
OpenCV
NumPy
How It Works
The system continuously analyzes a live video feed using the YOLOv8 object detection model.
When an ambulance is detected, the traffic signal in the simulation switches to green to provide a clear route. The interface also displays detection boxes around the ambulance and shows the current traffic light status.
Although this project currently runs as a simulation, the same concept can be expanded for real-world smart traffic systems using cameras, IoT devices, and traffic controllers.
Installation
Clone the repository:
git clone https://github.com/mantreshkhurana/AmbuRouteAI.git cd AmbuRouteAI
Create a virtual environment:
python -m venv venv
Activate it:
source venv/bin/activate
On Windows:
venv\Scripts\activate
Install the required packages:
pip install -r requirements.txt
Download the yolov8n.pt model from Ultralytics and place it in the project directory.
Run the application:
python main.py
Future Plans
Some improvements I’d like to add include:
Google Maps integration for route optimization
IoT-enabled smart traffic signals using Raspberry Pi or Arduino
Hospital notifications for incoming emergency vehicles
A mobile app for live ambulance tracking and traffic updates
Final Thoughts
AmbuRouteAI is an experimental project that explores how computer vision and AI can improve emergency response systems. While it’s currently a prototype, it demonstrates how technologies like YOLOv8 and OpenCV can be used to build smarter traffic management solutions.
The project is open source, and feedback or contributions are always welcome.
