  The prediction of surrounding agents' motion is a key for safe autonomous
driving. In this paper, we explore navigation maps as an alternative to the
predominant High Definition (HD) maps for learning-based motion prediction.
Navigation maps provide topological and geometrical information on road-level,
HD maps additionally have centimeter-accurate lane-level information. As a
result, HD maps are costly and time-consuming to obtain, while navigation maps
with near-global coverage are freely available. We describe an approach to
integrate navigation maps into learning-based motion prediction models. To
exploit locally available HD maps during training, we additionally propose a
model-agnostic method for knowledge distillation. In experiments on the
publicly available Argoverse dataset with navigation maps obtained from
OpenStreetMap, our approach shows a significant improvement over not using a
map at all. Combined with our method for knowledge distillation, we achieve
results that are close to the original HD map-reliant models. Our publicly
available navigation map API for Argoverse enables researchers to develop and
evaluate their own approaches using navigation maps.
