At the Széchenyi István University we develop an autonomous racing car for the Shell Eco-marathon. One of the main tasks is to create a neural network which is segment the road surface, the protective barriers and other components of the race track. The difficulty of this task, that there is no a right dataset for this special issue. Only a limited size dataset available, therefore, we would like to expands this dataset with computer generated training images, which comes from a virtual city environment. In this work we want to examine the effect of computer generated images on the efficiency of different neural networks. In the training process real images and computer generated virtual images are mixed in several different ways. After that, three different neural network architecture for road surface and road barrier detection are trained. Experiences shows how to mixing datasets and how they can improve efficiency.