The Self Driving Car Simulation and Visualizer is a project centered around the implementation of a neural network for self-driving car behavior.
The project implements car physics to simulate realistic vehicle movement and behavior within the environment.
The road generation utilizes linear interpolation algorithms to create road structures for the car to navigate.
Collision detection mechanisms are implemented to ensure the car interacts appropriately with the environment, avoiding collisions.
The car utilizes sensors based on ray casting techniques to perceive the environment and make informed decisions based on this input.
Traffic simulation is incorporated to introduce dynamic elements for a more realistic driving experience.
A visualizer is provided to display the car's decision-making process and interactions within the environment.
The core of the project involves implementing a neural network responsible for decision-making in the simulated self-driving car.
Genetic algorithms are used to optimize and train the neural network's parameters for better performance.
To set up the project locally, follow these steps:
- Clone this repository:
git clone https://github.com/abhie7/self-driving-car-simulation.git - Navigate to the project directory:
cd self-driving-car-simulation
If you wish to contribute to this project, follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature/add-new-feature). - Make your changes and commit them (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature/add-new-feature). - Create a pull request.
This project is licensed under the MIT License.
For any inquiries or feedback, please reach out at email.