🚦 Smart & AI-Integrated Traffic Control System

An AI-driven adaptive traffic signal system that adjusts signal timings in real time using YOLO-based vehicle detection and a Master–Slave Arduino architecture. Designed to reduce congestion and improve traffic flow at urban intersections.

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1️⃣ Project Overview

This system replaces traditional fixed-time traffic lights with an intelligent adaptive model.
A live camera feed is processed using YOLO to count vehicles, and signal timings are updated dynamically based on real-time road density.

Key Highlights:

• Real-time vehicle detection (YOLO)
• Dynamic signal timing
• Master–Slave Arduino control
• Hardware synchronization
• Scalable multi-road support

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2️⃣ System Architecture

📷 AI Detection Layer

• YOLO processes live video to detect and count vehicles
• Python computes traffic density per road
• Data is transferred to the Master Arduino via serial communication

🧠 Decision Layer (Master Arduino)

• Computes adaptive green time using:
  Green_Time = Base_Time + k × Vehicle_Count
• Sends timing information to all Slave controllers
• Generates a synchronization (SYNC) signal to align the start of every cycle

🔌 Actuation Layer (Slave Controllers)

• Slave Arduinos control Red, Yellow, and Green LEDs
• Execute timing cycles based on Master instructions
• Run a state-machine sequence:
  • GREEN → YELLOW → RED
• All Slaves stay synchronized using the Master’s SYNC pulse

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3️⃣ Features

• 🔹 Adaptive green-time allocation
• 🔹 Real-time AI-based vehicle counting
• 🔹 Master–Slave embedded control
• 🔹 Multi-direction traffic light management
• 🔹 Synchronized signal switching
• 🔹 Efficient and modular architecture

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4️⃣ Circuit Diagram

The file must be located at: Block diagram.png

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5️⃣ Project Structure

smart-ai-traffic-control-system/ │ ├── software/ │ ├── python/ │ │ ├── yolo_inference.py │ │ └── serial_comm.py │ └── arduino/ │ ├── master_controller.ino │ └── slave_controller.ino │ ├── hardware/ │ └── circuit_diagram.png │ ├── docs/ │ ├── project_presentation.pptx │ └── report.pdf │ └── README.md

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6️⃣ How It Works (Step-by-Step)

1. 📸 YOLO detects vehicles from camera feed
2. 🔢 Python counts vehicles per lane
3. 🧠 Master Arduino computes green signal time
4. 📤 Sends timing to Slave Arduinos
5. ⚡ SYNC pulse aligns all controllers
6. 🚦 Traffic signals operate adaptively based on density

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7️⃣ Future Enhancements

• Emergency vehicle detection
• Pedestrian signal integration
• Deployment on Nvidia Jetson / Edge TPU
• Cloud-based monitoring dashboard
• Multi-intersection coordination

📊 System Architecture & Workflow

The following diagram represents the complete workflow of the Smart & AI-Integrated Traffic Signal Control System, showing how YOLO-based vehicle detection, air-quality monitoring, and Arduino-based signal control operate together in real time.

📌 Key Highlights of the Architecture

• Dual-lane real-time video processing using YOLO for vehicle counting
• Python engine handles detection logic + displays dashboard data
• Master Arduino UNO executes timing algorithms based on density and air-quality
• I²C communication enables synchronized operation across all Arduino controllers
• Slave Arduinos manage independent signal control for each lane
• MQ135 sensor continuously monitors air pollution levels (A0 input)
• Centralized logic ensures adaptive, efficient, congestion-aware signal switching

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👤 Author

Harshvardhan Shinde
Electronics & Telecommunication Engineering
Focus: Embedded Systems • IoT • AI-Vision

🔗 LinkedIn: https://www.linkedin.com/in/harshvardhan-shinde-063699345