  Data-driven models (DDM) based on machine learning and other AI techniques
play an important role in the perception of increasingly autonomous systems.
Due to the merely implicit definition of their behavior mainly based on the
data used for training, DDM outputs are subject to uncertainty. This poses a
challenge with respect to the realization of safety-critical perception tasks
by means of DDMs. A promising approach to tackling this challenge is to
estimate the uncertainty in the current situation during operation and adapt
the system behavior accordingly. In previous work, we focused on runtime
estimation of uncertainty and discussed approaches for handling uncertainty
estimations. In this paper, we present additional architectural patterns for
handling uncertainty. Furthermore, we evaluate the four patterns qualitatively
and quantitatively with respect to safety and performance gains. For the
quantitative evaluation, we consider a distance controller for vehicle
platooning where performance gains are measured by considering how much the
distance can be reduced in different operational situations. We conclude that
the consideration of context information of the driving situation makes it
possible to accept more or less uncertainty depending on the inherent risk of
the situation, which results in performance gains.
