Self Driving Car Engineer Project 3 - Behavioural Cloning of Camera-Based Steering Control with Neural Networks

Benjamin Söllner, 30 May 2017

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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

Dependencies

This project requires:

• CarND Term1 Starter Kit: The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.
• Self Driving Car Simulator from the Udacity Self Driving Car Nanodegree
• Training data, e.g. the official Udacity training data

The following resources can be found in this github repository:

• clone.py containing the script to create and train the model
• drive.py for driving the car in autonomous mode
• model.h5 containing a trained convolution neural network
• writeup_report.md and writeup_report.html summarizing the results
• video.mp4 the video that shows the car driving around the track

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.

clone.py

This file does the actual training of the model You can run the training pipeline like so:

python clone.py

The script will assume the following:

• It will assume training data (csv and images from the self driving car simulator) to be stored in the data subdirectory
• It will write a model_*.h5 file to the main directory (after training)