Behavioral Cloning

Sanskar Arora

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The goals / steps of this project are the following:

• Use the simulator to collect data of good driving behavior
• Build, a convolution neural network in Keras that predicts steering angles from images
• Train and validate the model with a training and validation set
• Test that the model successfully drives around track one without leaving the road
• Summarize the results with a written report

Rubric Points

Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

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Files Submitted & Code Quality

1. Submission includes all required files and can be used to run the simulator in autonomous mode

My project includes the following files:

• model.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 or writeup_report.pdf summarizing the results

2. Submission includes functional code

Using the Udacity provided simulator and my drive.py file, the car can be driven autonomously around the track by executing

python drive.py model.h5

model.h5 is saved with keras and tensorlow Please make sure you're running with keras and tensorflow before testing out the model.

3. Submission code is usable and readable

The model.py file contains the code for training and saving the convolution neural network. This file shows the pipeline that I have used for training and validating the model (I have used a very old laptop so my library versions are a bit old).

Model Architecture and Training Strategy

1. An appropriate model architecture has been employed

The base of my model is inspireed from LeNet with additional normalization layer, cropping layer and dropout layer.

2. Attempts to reduce overfitting in the model

The model contains dropout layers in order to reduce overfitting.

3. Model parameter tuning

The model used an adam optimizer, so there was no need to set learning rate or any other parameters manually.

4. Appropriate training data

According to me the architecture of model and the data fed to it plays the most important role (for this project). I have employed all the data augmentations suggestions in the project resources, three cameras, image flipping, image cropping and image normalization.

Model Architecture and Training Strategy

1. Solution Design Approach

Initially, I thought the problem would require a complicated model so I ran a CNN with six convo2d layers, with depth 32, 64, 128, 128, 256, 256 respectively. The result is a model.h5 of size more than half a GB, which still shitted the bed. After about a dozen failed attempts at tuning I finally arrived at this model. Although reviewing the videos only I decided to go with LeNet and create a better training dataset.

Then I trained the model for 20 epoch and the vehicle is able to drive around the track without leaving the road.

2. Final Model Architecture

The final model architecture consisted of a convolution neural network with the following layers:

• A Cropping layer to crop the top of input images.
• A Lambda layer to normalize the pixal value.
• Two consecutive Convo2D layers with 6 x (5,5) layers with 'relu' activation and 2x2 maxpooling.
• A flatten layer
• Two Dense layers of 120, 80 neurons
• A dropout layer with prob=0.5
• A final layer of 1 neuron that outputs the steering angle.

3. Creation of the Training Set & Training Process

Initially, I thought I could create good training data on my own by following the recommendation: two middle of the road, and a few left/right recovery lap. I spent a few days driving the track but unable to generate a training set that is good enough to train a model.

Then I went back to the project website and learnt that it's almost impossible to generate a training data without a joystick. So at this point I decide that use the provided data.zip. Then I implemented all augmentation and preprocessing steps. In the end, it worked out.

The training data is cropped top and bottom, then flipped. Examples of training data:

Center camera image and its flip:

Left camera image and its flip:

Right camera image and its flip:

The final result of my model is like this.