Safety filters as a means for ensuring functional safety in data-driven control: an overview

Abstract This paper highlights the necessity of provable runtime safety mechanisms for integrating data-driven control methods into safety-critical systems operating in open environments. Since data-driven methods are often limited in providing formal guarantees, we argue for the use of formal methods, such as safety filters, to provide safety assurances. Safety filters denote a class of runtime mechanisms that ensure safety even if the nominal control method does not ensure safety, by constraining the system state to provably safe sets. The focus of this work is on analyzing safety filters from the perspective of functional safety as defined in industrial standards. We demonstrate how safety filters can provably reduce risks associated with hazardous behavior and how they operate as a monitoring and intervention mechanism for data-driven methods.