  This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for
control of safety-critical systems. Decision making for safety-critical systems
is challenging due to performance requirements with significant consequences in
the event of failure. In practice, failure of such systems can be avoided by
introducing redundancies of control. Neural Networks (NNs) are generally not
used for safety-critical systems as they can behave in unexpected ways in
response to novel inputs. In addition, there may not be any indication as to
when they will fail. BNNs have been recognized for their ability to produce not
only viable outputs but also provide a measure of uncertainty in these outputs.
This work combines the knowledge of prediction uncertainty obtained from BNNs
and ensemble control for a redundant control methodology. Our technique is
applied to an agile autonomous driving task. Multiple BNNs are trained to
control a vehicle in an end-to-end fashion on different sensor inputs provided
by the system. We show that an individual network is successful in maneuvering
around the track but crashes in the presence of unforeseen input noise. Our
proposed ensemble of BNNs shows successful task performance even in the event
of multiple sensor failures.
