  Cyber-physical systems (CPS) can benefit by the use of learning enabled
components (LECs) such as deep neural networks (DNNs) for perception and
decision making tasks. However, DNNs are typically non-transparent making
reasoning about their predictions very difficult, and hence their application
to safety-critical systems is very challenging. LECs could be integrated easier
into CPS if their predictions could be complemented with a confidence measure
that quantifies how much we trust their output. The paper presents an approach
for computing confidence bounds based on Inductive Conformal Prediction (ICP).
We train a Triplet Network architecture to learn representations of the input
data that can be used to estimate the similarity between test examples and
examples in the training data set. Then, these representations are used to
estimate the confidence of set predictions from a classifier that is based on
the neural network architecture used in the triplet. The approach is evaluated
using a robotic navigation benchmark and the results show that we can computed
trusted confidence bounds efficiently in real-time.
