🚦 Next-generation AI Traffic Management System with real-time computer vision, reinforcement learning optimization, emergency vehicle detection, and immersive 3D visualization

An AI-driven Adaptive Traffic Signal Control System (ATSCS) that replaces static timers with dynamic green-light phases. Utilizes YOLOv8 for real-time vehicle density estimation and multi-class classification, achieving 96.4% mAP. Optimized for low-latency inference (>30 FPS) on edge devices to reduce urban congestion and commuter wait times.

SUMO

AI-powered dynamic traffic management system using PyTorch DQN and SUMO. Built for SIH.

A trajectory-aware emergency corridor orchestration prototype for smart cities. Integrates SUMO simulation, FastAPI, and React to automate traffic signal preemption and reduce ambulance response times.

A centralized deep reinforcement learning framework for adaptive urban traffic signal control, leveraging simulation-based environments to minimize congestion and optimize traffic flow.

An intelligent traffic management agent developed with Deep Reinforcement Learning (DQN) and PyTorch. This project optimizes signal timings in real-time, achieving a 26% reduction in average vehicle wait time and 17% queue reduction in SUMO simulations compared to fixed-timer baselines.