Simulators

Sdsandbox

SdSandbox

Self Driving Car Sandbox

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Summary

Use Unity 3d game engine to simulate car physics in a 3d world. Generate image steering pairs to train a neural network. Uses NVidia PilotNet NN topology. Then validate the steering control by sending images to your neural network and feed steering back into the simulator to drive.

Some videos to help you get started

Training your first network

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World complexity

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Creating a robust training set

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Setup

You need to have Unity installed, and all python modules listed in the Requirements section below.

Linix Unity install here. Check last post in this thread.

You need python 3.4 or higher, 64 bit. You can create a virtual env if you like:

virtualenv -p python3 env
source env/bin/activate

And then you can install the dependancies. This installs a specific version of keras only because it will allow you to load the pre-trained model with fewer problems. If not an issue for you, you can install the latest keras.

pip install -r requirements.txt

If you have an cuda supported GPU - probably NVidia

pip install tensorflow-gpu

Or without a supported gpu

pip install tensorflow

Demo

  1. Start the prediction server with the pre-trained model.
cd sdsandbox/src
python predict_server.py ../outputs/highway.h5

If you get a crash loading this model, you will not be able to run the demo. But you can still generate your own model. This is a problem between tensorflow/keras versions.

  1. Load the Unity project sdsandbox/sdsim in Unity. Double click on Assets/Scenes/main to open that scene.

  2. Hit the start button to launch. Then the “Use NN Steering”.

#To create your own data and train

Generate training data

  1. Load the Unity project sdsandbox/sdsim in Unity.

  2. Create a dir sdsandbox/sdsim/log.

  3. Hit the start arrow in Unity to launch project.

  4. Hit button “Generate Training Data” to generate image and steering training data. See sdsim/log for output files.

  5. Stop Unity sim by clicking run arrow again.

  6. Run this python script to prepare raw data for training:

cd sdsandbox/src
python prepare_data.py
  1. Repeat 4, 5, 6 until you have lots of training data.

Train Neural network

python train.py ../outputs/mymodel.h5

Let this run. It may take a few hours if running on CPU. Usually far less on a GPU.

Run car with NN

  1. Start the prediction server. This listens for images and returns a steering result.
python predict_server.py ../outputs/mymodel.h5
  1. Start Unity project sdsim

  2. Push button “Use NN Steering”

Requirements

*Note: May work with Python 2.7+. But you will need to train your own models. The stock models will not load.

**Note: pygame only needed if using mon_and_predict_server.py which gives a live camera feed during inferencing.

Credits

Tawn Kramer