In the autonomous driving context it has always been crucial to be able to teach a vehicle to precisely identify each entity in an image, in order to take appropriate decisions in various scenarios. Semantic segmentation aims at doing so by classifying each individual pixel, but, as well as all approaches based on Deep Learning, it requires lots of data to effectively train a network. Collecting big amounts of labeled data is far from trivial, so synthetic and real datasets were created to overcome such scarcity. This is not enough though, as real datasets are still too small and not various enough to satisfy this need, whereas readily available synthetic datasets do not offer a wide variety of scenarios to choose from.