Self-Driving Car Simulation 🏎️🧠

A high-performance self-driving car simulation powered by NEAT (NeuroEvolution of Augmenting Topologies). This project demonstrates how neural networks can evolve to master complex navigation tasks through genetic algorithms.

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🌟 Key Features

• Premium Dashboard: A minimalist, glassmorphism-based web interface built with Streamlit.
• NEAT Evolution: Full implementation of neuroevolution including mutation, crossover, and speciation.
• Headless Engine: Physics core runs independently of display, optimized for real-time web streaming.
• Modular Design: Professionally refactored architecture for clarity and extensibility.
• Theme-Aware: Dashboard automatically adapts to light and dark browser modes.

📂 Project Structure

├── assets/          # Sprites and visual resources
├── config/          # Centralized constants and NEAT configuration
├── engine/          # Core physics, simulation logic, and NN visualizers
├── web/             # Modern dashboard interface and simulation bridge
└── main.py          # Standalone CLI simulation entry point (Legacy)

🚀 Quick Start

1. Installation

Clone the repository and install dependencies:

git clone https://github.com/Taaranjain/Self-Driven-Car.git
cd Self-Driven-Car
pip install -r requirements.txt

2. Launch the Dashboard

Experience the simulation with real-time analytics in your browser:

streamlit run web/app.py

Tip

Use the Control Panel in the sidebar to START/STOP the evolution process.

3. CLI Mode

Run the simulation in a native window:

python main.py

🧠 Technical Overview

Neural Network Inputs

• 8x Ray-cast Sensors: Measuring distance to road boundaries.
• Velocity: Current speed of the vehicle.

Outputs (Decisions)

• Accelerate / Brake
• Turn Left / Turn Right

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Built with Python, Pygame, and Streamlit.