rzuccolo/rz-behaviral-cloning

Convolutional neural networks applied to clone driving behavior. Keras/TensorFlow. The model outputs a steering angle to an autonomous vehicle simulator.

Behavioral Cloning Project

Overview

Deep neural networks and convolutional neural networks applied to clone driving behavior. Train, validate and test a model using Keras/TensorFlow. The model outputs a steering angle to an autonomous vehicle.

Udacity provided a simulator where we can steer a car around a track for data collection. Then we use image data and steering angles to train a neural network and then use the model to drive the car autonomously around the track.

Track1 Video Record, 'Fastest' graphical mode Click on this link to watch the video recording in 'fastest' mode for track 1 Track2 Video Record, 'Fastest' graphical mode Click on this link to watch the video recording in 'fastest' mode for track 2

The Project

The goals/steps of this project are the following:

• Use the simulator to collect data on 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
• Track 2 is a challenge!

Details About Files In This Directory

drive.py

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 basic file.

Saving a video of the autonomous agent

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 was seen. This information is used by video.py to create a chronological video of the agent driving.

video.py

python video.py run1

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

Acknowledgments / References

• Udacity Self-Driving Car Nanodegree
• NVIDIA's End to End Learning for Self-Driving Cars
• Mez Gebre
• Vivek Yadav
• Andrew Hogan