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

2026-05-21

AI Summary

Traditional static safety cases struggle to dynamically respond to runtime evidence and cannot continuously quantify confidence in system safety. This work proposes a dynamic safety argumentation framework grounded in subjective logic, which integrates design-time evidence with runtime Safety Performance Indicators (SPIs). By employing a sliding window mechanism to process SPI data in real time, the framework introduces a confidence-updating rule prioritizing safety responsiveness—gradually increasing confidence in the absence of violations while imposing swift penalties upon detection of anomalies—thereby overcoming limitations inherent in conventional Bayesian posterior updating. The approach is validated through simulations of an assistive function in construction zones, effectively demonstrating the dynamic evolution of confidence in a machine learning–driven traffic cone detection component as informed by runtime evidence.

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.

Problem

• runtime confidence updates
• safety arguments
• Safety Performance Indicators
• Subjective Logic
• dynamic quantitative assurance

Innovation

• Subjective Logic
• runtime confidence update
• Safety Performance Indicators
• dynamic assurance case
• safety argument