Runtime Monitoring DNN-Based Perception
This is an incremental tutorial that surveys existing techniques for improving safety in perception systems, relevant for engineers and researchers in safety-critical AI applications.
The paper addresses the need for runtime verification techniques to detect and diagnose critical events in safety-critical DNN-based perception systems, highlighting methods from machine learning and formal methods communities.
Deep neural networks (DNNs) are instrumental in realizing complex perception systems. As many of these applications are safety-critical by design, engineering rigor is required to ensure that the functional insufficiency of the DNN-based perception is not the source of harm. In addition to conventional static verification and testing techniques employed during the design phase, there is a need for runtime verification techniques that can detect critical events, diagnose issues, and even enforce requirements. This tutorial aims to provide readers with a glimpse of techniques proposed in the literature. We start with classical methods proposed in the machine learning community, then highlight a few techniques proposed by the formal methods community. While we surely can observe similarities in the design of monitors, how the decision boundaries are created vary between the two communities. We conclude by highlighting the need to rigorously design monitors, where data availability outside the operational domain plays an important role.