A Subjective Logic-based method for runtime confidence updates in safety arguments

TL;DR

This paper introduces a Subjective Logic-based approach for real-time confidence updates in safety cases, integrating design-time evidence with runtime Safety Performance Indicators to enhance assurance during operation.

Contribution

It presents a novel method combining static safety cases with dynamic runtime confidence updates using Subjective Logic, emphasizing safety responsiveness over classical Bayesian methods.

Findings

Confidence in safety claims increases with positive SPI evidence

The method effectively updates confidence during operation in a simulated construction zone

Penalties are applied promptly when safety violations are detected

Abstract

We present a method for dynamic quantitative assurance that enhances static safety cases with continuous, runtime-driven confidence updates. The method quantifies and propagates confidence across the development lifecycle by integrating design-time evidence and windowed runtime Safety Performance Indicators (SPIs) within a single Subjective Logic (SL)-based assurance case. At runtime, SPI evidence is continuously evaluated, and targeted claims are updated using a rule that increases confidence in the absence of violations and imposes prompt penalties when violations occur. This design prioritizes safety-relevant responsiveness over exact classical Bayesian posterior updates. We demonstrate the method using a simulation-based construction zone assist function, focusing on an ML-based construction cone detection component, and show how confidence evolves as SPI evidence is observed in operation.