Autonomous driving is without a shadow of doubt the future of automotive and it currently needs efficient solutions. Deep learning methods have shown excellent results for object classification and detection, but the specific task needed for driving is the more challenging semantic segmentation: each pixel of a depicted scene should be recognized as belonging to a specific object. For this setting, it is crucial to collect a large amount of per-pixel labeled data, which need an expensive manual classification process. Synthetic datasets from simulators can be used in order to reduce the amount of data required and they come with free annotation by design.