This repository contains starting files for the Behavioral Cloning Project.
In this project, you will use what you've learned about deep neural networks and convolutional neural networks to clone driving behavior. You will train, validate and test a model using Keras. The model will output a steering angle to an autonomous vehicle.
We have provided a simulator where you can steer a car around a track for data collection. You'll use image data and steering angles to train a neural network and then use this model to drive the car autonomously around the track.
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
Detail description: writeup
- An augmentation based deep neural network approach to learn human driving behavior
- Data augmentation skills
- Behavioral Cloning — make a car drive like yourself
- Avoid max pool layer
- Github
- How large should the batch size be for stochastic gradient descent?
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:
- drive.py
- video.py
- writeup.md
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 in which to save the images seen by the agent. If the directory already exists, it'll be overwritten.
ls run1
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...
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.
- 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
drive.py
and/orvideo.py
to create a video of what your model sees after the image is processed (may be helpful for debugging).
- Please keep in mind that training images are loaded in BGR colorspace using cv2 while drive.py load images in RGB to predict the steering angles.