  Accurate lane detection, a crucial enabler for autonomous driving, currently
relies on obtaining a large and diverse labeled training dataset. In this work,
we explore learning from abundant, randomly generated synthetic data, together
with unlabeled or partially labeled target domain data, instead. Randomly
generated synthetic data has the advantage of controlled variability in the
lane geometry and lighting, but it is limited in terms of photo-realism. This
poses the challenge of adapting models learned on the unrealistic synthetic
domain to real images. To this end we develop a novel autoencoder-based
approach that uses synthetic labels unaligned with particular images for
adapting to target domain data. In addition, we explore existing domain
adaptation approaches, such as image translation and self-supervision, and
adjust them to the lane detection task. We test all approaches in the
unsupervised domain adaptation setting in which no target domain labels are
available and in the semi-supervised setting in which a small portion of the
target images are labeled. In extensive experiments using three different
datasets, we demonstrate the possibility to save costly target domain labeling
efforts. For example, using our proposed autoencoder approach on the llamas and
tuSimple lane datasets, we can almost recover the fully supervised accuracy
with only 10% of the labeled data. In addition, our autoencoder approach
outperforms all other methods in the semi-supervised domain adaptation
scenario.
