  Many autonomous systems, such as driverless taxis, perform safety critical
functions. Autonomous systems employ artificial intelligence (AI) techniques,
specifically for the environment perception. Engineers cannot completely test
or formally verify AI-based autonomous systems. The accuracy of AI-based
systems depends on the quality of training data. Thus, novelty detection -
identifying data that differ in some respect from the data used for training -
becomes a safety measure for system development and operation. In this paper,
we propose a new architecture for autoencoder-based semantic novelty detection
with two innovations: architectural guidelines for a semantic autoencoder
topology and a semantic error calculation as novelty criteria. We demonstrate
that such a semantic novelty detection outperforms autoencoder-based novelty
detection approaches known from literature by minimizing false negatives.
