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Generating traffic scene with deep convolutional generative adversarial networks

Training and testing unmanned vehicles need various real data. However, data of some special or dangerous testing environment may not be accessible, or may only be accessible at certain times. So, using generative adversarial networks to learn the real traffic scene and generate a new scene is an effective way of solving the problem. In this paper, a framework of deep convolutional generative adversarial networks (DCGAN) was used to generate new traffic scene images and videos. Firstly, 300 sets of videos and images of overtaking scenes were selected as training data. A generator with convolutional neural network was used to generate samples. Then the training data and the generated samples were trained in a two-class discriminator that provides the probabilities of the samples that come from the generated sample and the training data respectively. Then the generator was updated by back propagation algorithm. Afterward, a new sample was generated and trained in the discriminator again. Repeated several times, a serial of generated samples were generated, the probability distribution of which is basically the same as the training data. The experiments show that this method can effectively generate realistic traffic scene images and videos.