How to simulate a self driving car

This is for the Hawaii Machine Learning meetup group. The code was for Siraj Raval Youtube video. The original repo was by Naoki Shibuya.

Overview

We're going to use Udacity's self driving car simulator for training an autonomous car.

Dependencies

You can install all dependencies by running one of the following commands

You need a anaconda or miniconda to use the environment setting.

# Use TensorFlow without GPU
conda env create -f environments.yml 

# Use TensorFlow with GPU
conda env create -f environment-gpu.yml

Or you can manually install the required libraries (see the contents of the environemnt*.yml files) using pip.

Usage

Run the pretrained model

Start up the Udacity self-driving simulator, choose a scene and press the Autonomous Mode button. Then, run the model as follows:

python drive.py model.h5

To train the model

You'll need the data folder which contains the training images.

python model.py

This will generate a file model-<epoch>.h5 whenever the performance in the epoch is better than the previous best. For example, the first epoch will generate a file called model-000.h5.

Credits

The credits for this code go to naokishibuya, Siraj also created a wrapper to get people started. I fixed what I think is a bug to hopefully ensure it runs smoothly for everyone.