  Amodal perception terms the ability of humans to imagine the entire shapes of
occluded objects. This gives humans an advantage to keep track of everything
that is going on, especially in crowded situations. Typical perception
functions, however, lack amodal perception abilities and are therefore at a
disadvantage in situations with occlusions. Complex urban driving scenarios
often experience many different types of occlusions and, therefore, amodal
perception for automated vehicles is an important task to investigate. In this
paper, we consider the task of amodal semantic segmentation and propose a
generic way to generate datasets to train amodal semantic segmentation methods.
We use this approach to generate an amodal Cityscapes dataset. Moreover, we
propose and evaluate a method as baseline on Amodal Cityscapes, showing its
applicability for amodal semantic segmentation in automotive environment
perception. We provide the means to re-generate this dataset on github.
