  Today's software stacks for autonomous vehicles rely on HD maps to enable
sufficient localization, accurate path planning, and reliable motion
prediction. Recent developments have resulted in pipelines for the automated
generation of HD maps to reduce manual efforts for creating and updating these
HD maps. We present FlexMap Fusion, a methodology to automatically update and
enhance existing HD vector maps using OpenStreetMap. Our approach is designed
to enable the use of HD maps created from LiDAR and camera data within
Autoware. The pipeline provides different functionalities: It provides the
possibility to georeference both the point cloud map and the vector map using
an RTK-corrected GNSS signal. Moreover, missing semantic attributes can be
conflated from OpenStreetMap into the vector map. Differences between the HD
map and OpenStreetMap are visualized for manual refinement by the user. In
general, our findings indicate that our approach leads to reduced human labor
during HD map generation, increases the scalability of the mapping pipeline,
and improves the completeness and usability of the maps. The methodological
choices may have resulted in limitations that arise especially at complex
street structures, e.g., traffic islands. Therefore, more research is necessary
to create efficient preprocessing algorithms and advancements in the dynamic
adjustment of matching parameters. In order to build upon our work, our source
code is available at https://github.com/TUMFTM/FlexMap_Fusion.
