  Data-driven perception approaches are well-established in automated driving
systems. In many fields even super-human performance is reached. Unlike
prediction and planning approaches, mainly supervised learning algorithms are
used for the perception domain. Therefore, a major remaining challenge is the
efficient generation of ground truth data. As perception modules are positioned
close to the sensor, they typically run on raw sensor data of high bandwidth.
Due to that, the generation of ground truth labels typically causes a
significant manual effort, which leads to high costs for the labelling itself
and the necessary quality control. In this contribution, we propose an
automatic labeling approach for semantic segmentation of the drivable ego
corridor that reduces the manual effort by a factor of 150 and more. The
proposed holistic approach could be used in an automated data loop, allowing a
continuous improvement of the depending perception modules.
