  Artificial intelligence for autonomous driving must meet strict requirements
on safety and robustness. We propose to validate machine learning models for
self-driving vehicles not only with given ground truth labels, but also with
additional a-priori knowledge. In particular, we suggest to validate the
drivable area in semantic segmentation masks using given street map data. We
present first results, which indicate that prediction errors can be uncovered
by map-based validation.
