  Using HD maps directly as training data for machine learning tasks has seen a
massive surge in popularity and shown promising results, e.g. in the field of
map perception. Despite that, a standardized HD map framework supporting all
parts of map-based automated driving and training label generation from map
data does not exist. Furthermore, feeding map perception models with map data
as part of the input during real-time inference is not addressed by the
research community. In order to fill this gap, we presentlanelet2_ml_converter,
an integrated extension to the HD map framework Lanelet2, widely used in
automated driving systems by academia and industry. With this addition Lanelet2
unifies map based automated driving, machine learning inference and training,
all from a single source of map data and format. Requirements for a unified
framework are analyzed and the implementation of these requirements is
described. The usability of labels in state of the art machine learning is
demonstrated with application examples from the field of map perception. The
source code is available embedded in the Lanelet2 framework under
https://github.com/fzi-forschungszentrum-informatik/Lanelet2/tree/feature_ml_converter
