CNN-Self-Driving-Cars-with-Udacity-Simulator

Descirption:

This GitHub repository contains the code and documentation for my research project on building a Deep Learning model for Self-Driving Cars using the Udacity Simulator. The goal of this project is to develop a robust and reliable autonomous driving system that can navigate simulated environments. 🚘🎮

Abstract:

This research/ project presents the process and development of an autonomous vehicle prototype using Udacity Simulator with the Convolutional Neural Network (CNN) method. This research uses NVIDIA CNN Architecture for its convolutional neural network architecture. The objective of this research is to design and generate a machine learning model to run the Udacity simulator in autonomous mode, which allowing car objects on the simulator to move autonomously. The proposed approach involves training a CNN model using a labeled dataset of tracks images captured through training mode. The output of the CNN model is then used to control the steering and acceleration commands of the vehicle. The performance of the machine learning model is evaluated using MSE, RMSE, and MAE parameters. In addition, it is also evaluated on its ability to navigate one of the tracks in the simulator.

Key Features:

1. Deep Learning Model with Approach of Convolutional Neural Network (CNN) - use NVIDIA CNN Architecture
2. Integration with Udacity Simulator through Python File: Drive.py
3. I use 2 traks: The Jungle Track (Advanced) and The Lake Track (Basic Track). For each track, I had create the Machine Learning Model
4. For Integration with Udacity Simulator (through Python File Drive.py) uses a client-server architecture with socket.io Server and Flask library

Folder Information

1. beta_simulator_windows: it provides the Udacity Simulator that can be downloaded free - as it is open source
2. Dataset: it provides the Dataset for each track: The Jungle Track and The Lake Trak. It contains an Image and CSV File for Exploratory Data Analysis (EDA)
3. Python_File: it contains a Colab/Notebook File (with format .ipnyb) for each Machine Learning model for each Track that used. Beside that on this folder has Python File Drive.py for integrating Simulator Udacity and its Machine Learning Models

Libraries Requires:

1. TensorFlow 2. Keras 3. Sklearn 4. OpenCV 5. Pandas 6. NumPy 7. Matplotlib 8. Eventlet 9. Imgaug 10. Flask 11. socket.io