  As the use of Artificial Intelligence (AI) components in cyber-physical
systems is becoming more common, the need for reliable system architectures
arises. While data-driven models excel at perception tasks, model outcomes are
usually not dependable enough for safety-critical applications. In this work,we
present a timeseries-aware uncertainty wrapper for dependable uncertainty
estimates on timeseries data. The uncertainty wrapper is applied in combination
with information fusion over successive model predictions in time. The
application of the uncertainty wrapper is demonstrated with a traffic sign
recognition use case. We show that it is possible to increase model accuracy
through information fusion and additionally increase the quality of uncertainty
estimates through timeseries-aware input quality features.

Machine Learning;Software Engineering