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

Abstract:

As an initial assessment, over 480,000 labeled virtual images of normal highway driv- ing were readily generated in Grand Theft Auto V’s virtual environment. Using these images, a CNN (Convolutional Neural Network) and a RNN (Recurrent Neural Net- work) were 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 and Recurrent Neural Networks for safe driving.