Video Demonstrations

Easy Track

Hard Track

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

The simulator has been provided by Udacity, where you can steer a car around a track for data collection. The image data and steering angles are used to train a neural network and then use this model to drive the car autonomously around the track.

See my detailed writeup of the project here

To meet specifications, the project required submitting 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)

I additionally included the following files:

• model_w_generator.py (script to create and train model using a python generator)
• canny_augment.py (script to add canny edge detection to images)

The model_w_generator.py script reads the training images into memory batch by batch instead of all at once. This allows for training on greater datasets because it uses less memory.

The canny_augment.py function applies canny edge detection to the images which improved the classifier's performance.

This README file describes how to output the video in the "Details About Files In This Directory" section.

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.
• Summarize the results with a written report

This lab requires:

• CarND Term1 Starter Kit

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

The simulator can be downloaded from the classroom. In the classroom, we have also provided sample data that you can optionally use to help train your model.

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 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.

Note: There is known local system's setting issue with replacing "," with "." when using drive.py. When this happens it can make predicted steering values clipped to max/min values. If this occurs, a known fix for this is to add "export LANG=en_US.utf8" to the bashrc file.

python drive.py model.h5 run1

The fourth argument run1 is the directory to save the images seen by the agent to. 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 when the image image was seen. This information is used by video.py to create a chronological video of the agent driving.

python video.py run1

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

The video will run at 48 FPS. The default FPS is 60.