Formal Verification Methods for Runtime Assurance in Autonomous Control Software

Abstract

The rapid deployment of autonomous control software in safety critical domains necessitates rigorous validation and verification processes. Traditional formal verification methods offer mathematical guarantees regarding system behavior under specified conditions. However, the translation of these static guarantees into dynamic, unpredictable operational environments often reveals significant assurance gaps. This paper investigates the predictive relationship between the outcomes of formal verification methods and the actual runtime assurance observed in autonomous control systems. By developing and deploying a comprehensive benchmark evaluation suite, this study systematically analyzes various control software architectures subjected to both static formal verification and dynamic runtime testing. The research evaluates how metrics derived from model checking and theorem proving correlate with runtime safety interventions, system stability, and fault tolerance capabilities. Through extensive simulation and empirical data collection across varied operational scenarios, the findings demonstrate that while formal verification provides a foundational layer of safety, specific static metrics can reliably predict the frequency and severity of required runtime assurance interventions. The analysis reveals distinct patterns where high state space coverage in verification correlates strongly with reduced runtime fault triggers, whereas abstract theorem proving outcomes show a more variable predictive capacity. These insights contribute to a more holistic framework for certifying autonomous systems, ensuring that theoretical safety guarantees are effectively translated into robust physical performance.