  Artificial intelligence for autonomous driving must meet strict requirements
on safety and robustness, which motivates the thorough validation of learned
models. However, current validation approaches mostly require ground truth data
and are thus both cost-intensive and limited in their applicability. We propose
to overcome these limitations by a model agnostic validation using a-priori
knowledge from street maps. In particular, we show how to validate semantic
segmentation masks and demonstrate the potential of our approach using
OpenStreetMap. We introduce validation metrics that indicate false positive or
negative road segments. Besides the validation approach, we present a method to
correct the vehicle's GPS position so that a more accurate localization can be
used for the street-map based validation. Lastly, we present quantitative
results on the Cityscapes dataset indicating that our validation approach can
indeed uncover errors in semantic segmentation masks.
