Behaviorial Cloning Project

Overview

This project uses deep neural networks and convolutional neural networks to clone driving behavior. A model was developed to output steering angle to an autonomous vehicle. It was developed, trained and tested using Keras.

The Project

The goals / steps of this project are the following:

• Use the simulator to collect data of good driving behavior
• Design, train and validate a model that predicts a steering angle from image data
• Use the model to drive the vehicle autonomously around the first track in the simulator. The vehicle should remain on the road for an entire loop around the track.
• Summarize the results with a written report

Files and Dependancies

My project includes the following files:

• model.py contains the script to create and train the model
• utils.py contains the preprocessing steps for the dataset
• drive.py used for driving the car in autonomous mode
• model.h5 contains a trained convolution neural network
• writeup.md explains the results
• video.py takes in the recording images from drive.py and combines them into a video
• run1.mp4 is the video output of video.py, showing the simulator running in autonomous mode from the trained model
• histogram.py outputs a histogram of the training / validation set to better understand data representation

Dependancies:

• CarND Term1 Starter Kit

• CarND Simulator

Details About Files In This Directory

drive.py

Usage of drive.py requires you have saved the trained model as an h5 file, i.e. model.h5. See the Keras documentation for how to create this file using the following command:

model.save(filepath)

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

python drive.py model.h5

The above command 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.

Saving a video of the autonomous agent

python drive.py model.h5 run1

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

ls run1

[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_424.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_451.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_477.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_528.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_573.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_618.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_697.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_723.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_749.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_817.jpg
...

The image file name is a timestamp when the image image was seen. This information is used by video.py to create a chronological video of the agent driving.

video.py

python video.py run1

Create a video based on images found in the run1 directory. The name of the video will be name of the directory following by '.mp4', so, in this case the video will be run1.mp4.

Optionally one can specify the FPS (frames per second) of the video:

python video.py run1 --fps 48

The video will run at 48 FPS. The default FPS is 60.