Behavioral Cloning Project

Overview

This repository contains the files for the Behavioral Cloning Project.

In this project, I used what I've learned about deep neural networks and convolutional neural networks to clone driving behavior. I trained, validated and tested a model using Keras. The model outputs a steering angle to an autonomous vehicle.

I used image data and steering angles obtained from Udacity's provided simulator to train a neural network and then use this model to drive the car autonomously around the simulator's track.

I also created a detailed writeup of the project.

The project's core consists of the following five files:

model.py (script used to create and train the model)
drive.py (script to drive the car - feel free to modify this file)
model.h5 (a trained Keras model)
a report writeup file (either markdown or pdf)
video.mp4 (a video recording of your vehicle driving autonomously around the track for at least one full lap)

The Project

The achieved goals / steps of this project are the following:

Collection of data of good driving behavior from Udacity's provided simulator
Designing, training and validating a model that predicts a steering angle from image data
Using the model to drive the vehicle autonomously around the first track in the simulator. The vehicle remains on the road for (at least) an entire loop around the track.
Summarizing the results with a written report

Dependencies

This lab requires:

CarND Term1 Starter Kit The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.

Details About Files In This Directory

`drive.py`

Usage of drive.py required the existence of the trained model as an h5 file 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.

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.

MK2112/CarND-Behavioral_Cloning