🚗 Self-Driving Car using First-Principles Machine Learning

A simple self-driving car simulation built completely from scratch — without using any external machine learning libraries.
This project demonstrates how a car can learn to navigate through traffic (obstacles) using a basic Genetic Algorithm, implemented purely from first principles.

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📌 Table of Contents

• Overview
• How It Works
• Installation
• What Can It Be Used For
• Project Structure
• Contribution
• Acknowledgement
• License
• Author

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📖 Overview

This project is a first-principles machine learning implementation where:

• A car attempts to drive forward.
• Multiple obstacles (traffic) appear in its path.
• The car learns to avoid obstacles over generations.
• Learning is achieved using a Genetic Algorithm (GA).
• No external ML frameworks or libraries are used.

The goal is to demonstrate:

• Evolution-based learning
• Neural network basics (if implemented)
• Selection, mutation, and crossover
• Fitness-based improvement across generations

This project focuses on understanding how ML works internally, rather than relying on high-level libraries.

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⚙️ How It Works

1️⃣ Initialization

• A population of cars is created.
• Each car has its own “brain” (a simple neural model or decision parameters).
• Parameters are randomly initialized.

2️⃣ Simulation

• All cars attempt to drive forward.
• Sensors (or distance calculations) detect obstacles.
• Based on inputs, cars decide:
  • Move forward
  • Turn left/right
  • Accelerate/brake (if implemented)

3️⃣ Fitness Calculation

Each car is evaluated based on:

• Distance traveled
• Time survived
• Obstacles avoided

Higher survival = higher fitness.

4️⃣ Selection

• The best-performing cars are selected.
• Weak performers are removed.

5️⃣ Crossover

• Selected parents combine parameters.
• Offspring inherit mixed traits.

6️⃣ Mutation

• Small random changes are introduced.
• Encourages exploration and prevents stagnation.

7️⃣ Repeat

• New generation starts.
• Over multiple generations, driving improves.

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🛠 Installation

Clone the Repository

git clone https://github.com/your-username/driving-car-without-driver.git
cd driving-car-without-driver

Run the Project

open index.html

No external libraries are required.

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🎯 What Can It Be Used For

This project can be used for:

• Learning Genetic Algorithms from scratch
• Understanding evolutionary computation
• Teaching first-principles machine learning
• Academic mini-projects
• Demonstrating AI concepts without frameworks
• Interview discussions on ML fundamentals
• Classroom demonstrations

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📂 Project Structure

├── main.(js/py)
├── car.(js/py)
├── genetic_algorithm.(js/py)
├── environment.(js/py)
├── utils.(js/py)
└── README.md

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🤝 Contribution

Contributions are welcome!

To contribute:

1. Fork the repository

2. Create a new branch

   git checkout -b feature-name

3. Commit your changes

   git commit -m "Added new feature"

4. Push to your branch

   git push origin feature-name

5. Open a Pull Request

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🙏 Acknowledgement

A special thanks to Radu Mariescu-Istodor for the inspiration and project idea. His tutorials and teachings laid the foundation for this project and helped guide its development.

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📜 License

This project is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You may use the code for personal projects or non-commercial purposes. For any commercial use, including reselling or distributing the code, please contact the author for permission.

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👤 Author

• GitHub: @Anonav0
• LinkedIn:Swarnavo Khanra

Feel free to reach out with any questions or feedback!