  Transfer learning is an important field of machine learning in general, and
particularly in the context of fully autonomous driving, which needs to be
solved simultaneously for many different domains, such as changing weather
conditions and country-specific driving behaviors. Traditional transfer
learning methods often focus on image data and are black-box models. In this
work we propose a transfer learning framework, core of which is learning an
explicit mapping between domains. Due to its interpretability, this is
beneficial for safety-critical applications, like autonomous driving. We show
its general applicability by considering image classification problems and then
move on to time-series data, particularly predicting lane changes. In our
evaluation we adapt a pre-trained model to a dataset exhibiting different
driving and sensory characteristics.
