  As an initial assessment, over 480,000 labeled virtual images of normal
highway driving were readily generated in Grand Theft Auto V's virtual
environment. Using these images, a CNN was trained to detect following distance
to cars/objects ahead, lane markings, and driving angle (angular heading
relative to lane centerline): all variables necessary for basic autonomous
driving. Encouraging results were obtained when tested on over 50,000 labeled
virtual images from substantially different GTA-V driving environments. This
initial assessment begins to define both the range and scope of the labeled
images needed for training as well as the range and scope of labeled images
needed for testing the definition of boundaries and limitations of trained
networks. It is the efficacy and flexibility of a "GTA-V"-like virtual
environment that is expected to provide an efficient well-defined foundation
for the training and testing of Convolutional Neural Networks for safe driving.
Additionally, described is the Princeton Virtual Environment (PVE) for the
training, testing and enhancement of safe driving AI, which is being developed
using the video-game engine Unity. PVE is being developed to recreate rare but
critical corner cases that can be used in re-training and enhancing machine
learning models and understanding the limitations of current self driving
models. The Florida Tesla crash is being used as an initial reference.
