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Training, testing and enhancement of safe driving AI, which is being developed It is the efficacy and flexibility of a "GTA-V"-like virtualĮnvironment that is expected to provide an efficient well-defined foundationįor the training and testing of Convolutional Neural Networks for safe driving.Īdditionally, described is the Princeton Virtual Environment (PVE) for the Needed for testing the definition of boundaries and limitations of trained Images needed for training as well as the range and scope of labeled images Initial assessment begins to define both the range and scope of the labeled Virtual images from substantially different GTA-V driving environments. Encouraging results were obtained when tested on over 50,000 labeled Relative to lane centerline): all variables necessary for basic autonomousĭriving. To cars/objects ahead, lane markings, and driving angle (angular heading
Using these images, a CNN was trained to detect following distance Highway driving were readily generated in Grand Theft Auto V's virtualĮnvironment. Authors: Mark Martinez, Chawin Sitawarin, Kevin Finch, Lennart Meincke, Alex Yablonski, Alain Kornhauser Download PDF Abstract: As an initial assessment, over 480,000 labeled virtual images of normal
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