Behaviorial Cloning Project
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:
The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.
The following resources can be found in this github repository:
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.
Details About Files In This Directory
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:
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.
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 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
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.
Why create a video
- It's been noted the simulator might perform differently based on the hardware. So if your model drives succesfully on your machine it might not on another machine (your reviewer). Saving a video is a solid backup in case this happens.
- You could slightly alter the code in
video.pyto create a video of what your model sees after the image is processed (may be helpful for debugging).