Self-Driving Car Simulation

This project is a simulation of self-driving cars navigating through a road environment. The cars utilize neural networks for decision-making and are equipped with sensors to perceive their surroundings. The simulation provides a platform for experimenting with different neural network architectures, sensor configurations, and car behaviors.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Customization
6. Contributing

Introduction

Self-driving cars have emerged as a significant area of research and development in recent years. This simulation aims to provide a sandbox environment for exploring various aspects of autonomous vehicle technology, including:

• Neural network-based decision-making
• Sensor data processing and interpretation
• Collision detection and avoidance strategies

The simulation allows users to observe and analyze the behavior of self-driving cars under different conditions, facilitating learning and experimentation in the field of autonomous systems.

Features

• Simulation of multiple self-driving cars navigating through a road environment.
• Implementation of neural networks for decision-making by individual cars.
• Integration of sensors for detecting obstacles and boundaries on the road.
• Visualization of neural network architectures and sensor data for analysis.
• Customizable parameters for adjusting the behavior and performance of cars.

Installation

To run the self-driving car simulation locally, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/Ismat-Samadov/self_driving_car.git

2. Navigate to the project directory:

   cd self_driving_car

3. Open the index.html file in a web browser to launch the simulation.

Usage

• Upon launching the simulation, you'll be presented with a visual representation of the road environment and the self-driving cars.
• Use the browser console to interact with the simulation:
  • Call the save() method to store a car's neural network in local storage.
  • Call the discard() method to remove the saved neural network from local storage.
• Experiment with different parameters such as the number of cars simulated in parallel (N) and the mutation amount to observe changes in behavior.

Customization

The simulation is highly customizable, allowing users to tweak various parameters to suit their needs:

• Adjust the number of cars simulated in parallel (N) to observe the collective behavior of multiple cars.
• Modify the mutation amount to control the rate of mutation in neural networks.
• Customize the neural network architecture, sensor configurations, and car behaviors by modifying the corresponding JavaScript files.

Contributing

Contributions to the project are welcome! If you have any ideas, suggestions, or improvements, feel free to open an issue or submit a pull request. Your contributions will help enhance the simulation and make it more valuable for the community.