Self-Driving Car Project

Project Overview

This project involves developing a self-driving car model using deep learning techniques. The main goal is to create a model that can successfully navigate a car through a simulated environment.

Technologies Used

• Python: The primary programming language used for developing the model.
• TensorFlow/Keras: Deep learning frameworks used for building and training the neural network.
• OpenCV: Used for image processing.
• Numpy: For numerical computations.
• Matplotlib: For plotting graphs and visualizing data.
• Udacity Self-Driving Car Simulator: The environment used for testing the model.

Project Structure

• IMG/: Contains images used in the project.
• finding-lanes/: Contains scripts for lane finding.
• Deep_Neural.ipynb: Jupyter notebook for deep neural network implementation.
• Multiclass.ipynb: Jupyter notebook for multiclass classification.
• Perceptron.ipynb: Jupyter notebook for perceptron implementation.
• README.md: Project documentation.
• driving_log.csv: Log file for driving data.
• test_image.jpg: Sample test image.

Getting Started

Prerequisites

• Python 3.x
• TensorFlow/Keras
• OpenCV
• Numpy
• Matplotlib
• Udacity Self-Driving Car Simulator

You can install the required Python packages using the following command:

pip install -r requirements.txt