  Data-driven sensor interpretation in autonomous driving can lead to highly
implausible predictions as can most of the time be verified with common-sense
knowledge. However, learning common knowledge only from data is hard and
approaches for knowledge integration are an active research area. We propose to
use a partly human-designed, partly learned set of rules to describe relations
between objects of a traffic scene on a high level of abstraction. In doing so,
we improve and robustify existing deep neural networks consuming low-level
sensor information. We present an initial study adapting the well-established
Probabilistic Soft Logic (PSL) framework to validate and improve on the problem
of semantic segmentation. We describe in detail how we integrate common
knowledge into the segmentation pipeline using PSL and verify our approach in a
set of experiments demonstrating the increase in robustness against several
severe image distortions applied to the A2D2 autonomous driving data set.
