End-to-End-Learning-for-Self-Driving-Cars

Modified based on https://github.com/Nuclearstar/End-to-End-Learning-for-Self-Driving-Cars

tested on Ubuntu 22

Dataset

You can download the dataset from here for Implementation 1.
You can download the dataset and extract into the repository folder from here for Implementation 2.

1. Implementation 1
2. Implementation 2

Code Requirements

pip install requirements.txt

Python Implementation

1. Network Used- Convolutional Neural Network(CNN)
2. Keras-Tensorflow Architecture

Procedure in Implementation 1

1. First use python LoadData.py which will get dataset from folder and store it in a pickle file.
2. Now you need to have the data, use python TrainModel.py which will load data from pickle and augment it. After this, the training process begins.
3. For testing it on the video you need to use python DriveApp.py

Procedure in Implementation 2

1. Use python train.py to train the model.
2. Use python run.py to run the model on a live webcam feed.
3. Use python run_dataset.py to run the model on the dataset

References and Credits:

1. Modified based on Nuclearstar's Repo
2. This implementation is inspired by Akshay Bahadur's project
3. This implementation also took a lot of inspiration from the Sully Chen's work.