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Use Deep Learning to Clone Driving Behavior

The goal of this project is to create a neural network that can learn how to drive a car by watching how you drive.

https://github.com/andriikushch/CarND-Behavioral-Cloning-P3/blob/master/output.gif?raw=true

Here is a list of my steps to achieve this:

  • I will use the simulator to create data to train the network.
  • Preprocess data by cropping and normalizing, that it will contain relevant data and proper shape.
  • Design and teach the network.
  • Test, visualize and write a report.

Required files

The submission includes:

  • model.py
  • drive.py
  • model.h5 stored by parts in folder model:
    • please use cat model/model.h* > model.h5 to get the original model.h5 file (because github for free does not support files larger then 100 mb)
  • writeup report (README.md or writeup_report, they are the same)
  • center.jpg, left.jpg, right.jpg
    • samples from the training set
  • video.mp4
    • video result
  • output.gif

The Code

This implementation was able to successfully train network which can clone the behavior of driving.

Data preprocessing and model are in the model.py. For memory efficiency, there is a generator used.

Model Architecture and Training Strategy

The selected architecture was inspired by https://devblogs.nvidia.com/deep-learning-self-driving-cars/

https://devblogs.nvidia.com/parallelforall/wp-content/uploads/2016/08/cnn-architecture-624x890.png

Because of computation limitation in a given environment, I had to simplify it a little bit. To prevent overfitting and improve learning I've added BatchNormalization and Dropout:

https://github.com/andriikushch/CarND-Behavioral-Cloning-P3/blob/master/model.png?raw=true

Code in model.py :

# add Convolution2D layers
model.add(Convolution2D(filters=24, kernel_size=(5, 5), padding='valid', activation='relu'))
model.add(Convolution2D(filters=36, kernel_size=(5, 5), padding='valid', activation='relu'))
model.add(Convolution2D(filters=48, kernel_size=(5, 5), padding='valid', activation='relu'))
model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='valid', activation='relu'))
model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='valid', activation='relu'))

# add fully connected layers
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Dense(50, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(50, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(1))

In order to prepare training and validation sets I used :

from sklearn.model_selection import train_test_split

train_samples, validation_samples = train_test_split(samples, test_size=0.2)

And shuffle samples in generator code (see model.py -> generator() for details).

Examples of training images

Left Center Right
https://github.com/andriikushch/CarND-Behavioral-Cloning-P3/blob/master/left.jpg?raw=true https://github.com/andriikushch/CarND-Behavioral-Cloning-P3/blob/master/center.jpg?raw=true https://github.com/andriikushch/CarND-Behavioral-Cloning-P3/blob/master/right.jpg?raw=true

For every image, I also generated an augmented image by flipping the original one (see the generator in model.py).

Result

Here is original resulting video file https://github.com/andriikushch/CarND-Behavioral-Cloning-P3/blob/master/video.mp4

As we see from the video the car can drive a lap without any significant issue.