The output video can be seen here.
This project requires:
The project enviroment can be created with CarND Term1 Starter Kit. Click here for the details.
The simulator can be downloaded from here.
This discusses my approach to this problem and the necessary steps that I had to take in order to make it work.
This file contains the model I used for training the agent to learn how to stay on track and drive autonomously.
You can train the model using this command:
python model.py
Make sure to adjust the path to the location of the training data in your local directory.
This script will save the trained model as a .h5 file.
Usage of drive.py
requires you have saved the trained model as an h5 file, i.e. model.h5
.
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 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.
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.