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Behavioral Cloning Project Udacity - Self-Driving Car NanoDegree

Overview

A convolutional neural network to clone driving behavior. The model outputs a steering angle to an autonomous vehicle.

The model uses the image data and steering angles to train a neural network and then use this model to drive the car autonomously around the track.

Required files for the project:

  • model.py (script used to create and train the model)
  • drive.py (script to drive the car)
  • model.h5 (a trained Keras model)
  • video.py (script for saving the video of the car)

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.

Details About Files

model.py

This is the script used to create and train the model.

Pipeline:

  • Create dataset by extracting image data and steering angles from csv file.
  • Use generators for memory efficiency.
  • Split the dataset into training and validation.
  • Built a convolutional neural network.
  • Save the model (any method can be used: either model.save(model.h5) or using checkpoints).

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.

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.

Saving a video of the autonomous agent

python drive.py model.h5 run1

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.

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 of when the 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

Creates a video based on images found in the run1 directory. The name of the video will be the name of the directory followed 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

Will run the video at 48 FPS. The default FPS is 60.

Why create a video

  1. It's been noted the simulator might perform differently based on the hardware. So if your model drives succesfully on your machine it might not on another machine. Saving a video is a solid backup in case this happens.
  2. You could slightly alter the code in drive.py and/or video.py to create a video of what your model sees after the image is processed (may be helpful for debugging).