  Autonomous driving requires self awareness of its perception functions.
Technically spoken, this can be realized by observers, which monitor the
performance indicators of various perception modules. In this work we choose,
exemplarily, a semantic segmentation to be monitored, and propose an
autoencoder, trained in a self-supervised fashion on the very same training
data as the semantic segmentation to be monitored. While the autoencoder's
image reconstruction performance (PSNR) during online inference shows already a
good predictive power w.r.t. semantic segmentation performance, we propose a
novel domain mismatch metric DM as the earth mover's distance between a
pre-stored PSNR distribution on training (source) data, and an online-acquired
PSNR distribution on any inference (target) data. We are able to show by
experiments that the DM metric has a strong rank order correlation with the
semantic segmentation within its functional scope. We also propose a training
domain-dependent threshold for the DM metric to define this functional scope.
