  Machine learning (ML) is actively finding its way into modern cyber-physical
systems (CPS), many of which are safety-critical real-time systems. It is well
known that ML outputs are not reliable when testing data are novel with regards
to model training and validation data, i.e., out-of-distribution (OOD) test
data. We implement an unsupervised deep neural network-based OOD detector on a
real-time embedded autonomous Duckiebot and evaluate detection performance. Our
OOD detector produces a success rate of 87.5% for emergency stopping a
Duckiebot on a braking test bed we designed. We also provide case analysis on
computing resource challenges specific to the Robot Operating System (ROS)
middleware on the Duckiebot.
