  Road markings provide guidance to traffic participants and enforce safe
driving behaviour, understanding their semantic meaning is therefore paramount
in (automated) driving. However, producing the vast quantities of road marking
labels required for training state-of-the-art deep networks is costly,
time-consuming, and simply infeasible for every domain and condition. In
addition, training data retrieved from virtual worlds often lack the richness
and complexity of the real world and consequently cannot be used directly. In
this paper, we provide an alternative approach in which new road marking
training pairs are automatically generated. To this end, we apply principles of
domain randomization to the road layout and synthesize new images from altered
semantic labels. We demonstrate that training on these synthetic pairs improves
mIoU of the segmentation of rare road marking classes during real-world
deployment in complex urban environments by more than 12 percentage points,
while performance for other classes is retained. This framework can easily be
scaled to all domains and conditions to generate large-scale road marking
datasets, while avoiding manual labelling effort.
