Architecture as it controls a simulated autonomous vehicle

Using a neural network as an abstract black box makes it hard to grasp its inner workings. Visualizing a dynamic functioning neural network along with its related model simulation may lead to a deeper comprehension of both. We propose using a virtual environment as a tool to investigate the complex space of a neural network. As an example, we train a simulated remote autonomous vehicle to navigate a pre-planned path through a set of obstacles using a LAPART neural network. Within the virtual environment, we can easily and naturally position ourselves to best observe the activity in which we are most interested and discover the evolving space of an operating neural network.