End-to-End Driving via Imitation Learning

1. Introduction

In this project, we use deep neural networks and convolutional neural networks to clone the human driver's driving behavior. The trained end-to-end driving model outputs steering angles to keep the car driving within the track.

2. Requirements

• Ubuntu 16.04
• Virtual environment with python 2.7
• Tensorflow
• Keras

Installation

pip install opencv-python
pip install -U scikit-learn scipy matplotlib
# the tools for running drive.py
pip install python-socketio
pip install eventlet
pip install Pillow
pip install flask

• This lab requires CarND Term1 Starter Kit
• The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.

3. Data

• The driving images and steering angles are collected in the Udacity's simulator

4. Train & Run

# train and save model
python train.py 

#Once the model has been saved, it can be used with drive.py using this command:
python drive.py model.h5

• drive.py will load the trained model and use the model to make predictions on individual images in real-time and send the predicted angle back to the server via a websocket connection.

Note: There is known local system's setting issue with replacing "," with "." when using drive.py. When this happens it can make predicted steering values clipped to max/min values. If this occurs, a known fix for this is to add "export LANG=en_US.utf8" to the bashrc file.

5. Saving a video of the autonomous agent

# save image
python drive.py model.h5 run1
# crate video
python video.py run1
# optionally
python video.py run1 --fps 48 # The default FPS is 60.

• The fourth argument, run1, is the directory in which to save the images seen by the agent. If the directory already exists, it'll be overwritten.

6. License

This Project is released under the Apache licenes.