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

Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars