Simulation of Self Driving Car

We’re going to use Udacity’s self driving car simulator as a testbed for training an autonomous car.


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.


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 model.h5

To train the model

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


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.


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