/Self-Driving-Car-Behavior-Cloning

Keras and Behavior Clonning

Primary LanguageJupyter NotebookMIT LicenseMIT

Behavioral Cloning

Writeup Template

Behavioral Cloning Project

The steps of this project are the following:

  • Use the simulator provided by Udacity to collect data of good driving behavior. The simulator can be found in the following link: https://github.com/udacity/self-driving-car-sim
  • 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

Rubric Points

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

3. Submission code is usable and readable

The model.py file contains the code for training and saving the convolution neural network. The file shows the pipeline I used for training and validating the model, and it contains comments to explain how the code works.

Model Architecture and Training Strategy

1. An appropriate model architecture has been employed

A model summary is as follows:

Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
lambda_1 (Lambda)                (None, 160, 320, 3)   0           lambda_input_2[0][0]             
____________________________________________________________________________________________________
cropping2d_1 (Cropping2D)        (None, 90, 320, 3)    0           lambda_1[0][0]                   
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D)  (None, 43, 158, 24)   1824        cropping2d_1[0][0]               
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D)  (None, 20, 77, 36)    21636       convolution2d_1[0][0]            
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D)  (None, 8, 37, 48)     43248       convolution2d_2[0][0]            
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D)  (None, 6, 35, 64)     27712       convolution2d_3[0][0]            
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D)  (None, 4, 33, 64)     36928       convolution2d_4[0][0]            
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 8448)          0           convolution2d_5[0][0]            
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 100)           844900      flatten_1[0][0]                  
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 50)            5050        dense_1[0][0]                    
____________________________________________________________________________________________________
dense_3 (Dense)                  (None, 10)            510         dense_2[0][0]                    
____________________________________________________________________________________________________
dense_4 (Dense)                  (None, 1)             11          dense_3[0][0]                    
====================================================================================================
Total params: 981,819
Trainable params: 981,819
Non-trainable params: 0

2. Attempts to reduce overfitting in the model

I decided to keep the training epochs low: only five epochs. In addition to that, I split my sample data into training and validation data. Using 80% as training and 20% as validation.

3. Model parameter tuning

The model used an adam optimizer, so the learning rate was not tuned manually.

4. Appropriate training data

Training data was chosen to keep the vehicle driving on the road. I used a combination of center lane driving, recovering from the left and right sides of the road ...

For details about how I created the training data, see the next section.

Model Architecture and Training Strategy

1. Solution Design Approach

The overall strategy for deriving a model architecture was to ...

My first step was to use a convolution neural network model similar to the ... I thought this model might be appropriate because ...

In order to gauge how well the model was working, I split my image and steering angle data into a training and validation set. I found that my first model had a low mean squared error on the training set but a high mean squared error on the validation set. This implied that the model was overfitting.

To combat the overfitting, I modified the model so that ...

Then I ...

The final step was to run the simulator to see how well the car was driving around track one. There were a few spots where the vehicle fell off the track... to improve the driving behavior in these cases, I ....

At the end of the process, the vehicle is able to drive autonomously around the track without leaving the road.

2. Final Model Architecture

image8