Smart Traffic Management - TechForSociety Hackathon by Siemens & NASSCOM Foundation

Currently working on an exciting project as part of the TechForSociety hackathon conducted by Siemens and NASSCOM Foundation. This initiative focuses on developing technology solutions for social impact.

Project Overview:

Our Smart Traffic Management system is designed to revolutionize traffic control at junctions by automatically detecting emergency vehicles and prioritizing their movement through intelligent signal control.

Key features being developed:

• Emergency vehicle detection using YOLO deep learning models

• Real-time traffic density analysis for optimal flow control

• Automated signal control integration with Raspberry Pi

• Sensor-based traffic monitoring and data collection

• Manual override capability for traffic authorities

Technical Implementation:

• Deep Learning: YOLO algorithm implementation on Hailo 8L for ambulance detection

• Hardware Integration: Raspberry Pi for signal control and sensor management

• Frontend: QML-based application for authority interface

• Backend: Python-powered system for real-time processing

• IoT Integration: Multiple sensors for comprehensive traffic monitoring

The project timeline spans from October 2024 to April 2025, giving us ample time to develop, test, and refine the system. This hackathon is providing valuable experience in developing technology solutions that can make a real difference in emergency response times and urban traffic management.

Working on this project has deepened my understanding of computer vision, IoT systems, and the challenges of real-world deployment of AI solutions.