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
# 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 drive.py 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