Simulation to Real Domain Adaptation for Lane Segmentation

As the cost of labelling and collecting real world data remains an issue for companies, simulator training and transfer learning slowly evolved to be the foundation of many state-of the-art projects. In this paper these methods are applied in the Duckietown setup where self-driving agents can be developed and tested.Our aim was to train a selected artificial neural network for right lane segmentation on simulator generated stream of images as a comparison baseline, then use domain adaptation to be more precise and stable in the real environment. We have tested and compared four knowledge transfer methods that included domain transformation using CycleGAN and semi-supervised domain adaptation via Minimax Entropy. As the latter was previously untested in semantic segmentation according to our best knowledge, we have contributed to showing it is indeed possible and produces promising results. Finally we have shown that it could also create a model that fulfills our performance requirements of stability and accuracy.We show that the selected methods are equally eligible for the simulation to real transfer learning problem, and that the simplest method delivers the best performance.