  In a cyber-physical system such as an autonomous vehicle (AV), machine
learning (ML) models can be used to navigate and identify objects that may
interfere with the vehicle's operation. However, ML models are unlikely to make
accurate decisions when presented with data outside their training
distribution. Out-of-distribution (OOD) detection can act as a safety monitor
for ML models by identifying such samples at run time. However, in safety
critical systems like AVs, OOD detection needs to satisfy real-time constraints
in addition to functional requirements. In this demonstration, we use a mobile
robot as a surrogate for an AV and use an OOD detector to identify potentially
hazardous samples. The robot navigates a miniature town using image data and a
YOLO object detection network. We show that our OOD detector is capable of
identifying OOD images in real-time on an embedded platform concurrently
performing object detection and lane following. We also show that it can be
used to successfully stop the vehicle in the presence of unknown, novel
samples.
