Poster Abstract: Learning Contextual Runtime Monitors for Safe AI-Based Autonomy

Paper in proceeding, 2026

We introduce a novel framework for learning context-aware runtime monitors for AI-based control ensembles. Machine-learning (ML)-based controllers are increasingly deployed in (autonomous) cyber-physical systems due to their ability to solve complex decision-making tasks. However, their accuracy can degrade in unfamiliar environments, creating significant safety concerns. Traditional ensemble methods aim to improve robustness by averaging or voting across multiple controllers, but this often dilutes the specialized strengths of individual controllers in different operating contexts. We argue that a monitoring framework should identify and exploit these contextual strengths. We reformulate the design of safe AIbased control ensembles as a contextual monitoring problem. A monitor continuously observes the system's context and selects the controller best suited to the current conditions. We cast monitor learning as a contextual learning task and draw on techniques from contextual multi-armed bandits. Our approach brings two key benefits: (1) theoretical safety guarantees during controller selection, and (2) improved utilization of controller diversity. We validate our framework in two simulated autonomous driving scenarios, showing significant improvements in safety and performance, specifically over non-contextual baselines.