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

arXiv: https://arxiv.org/abs/2605.22530 · HTML: https://mlsanity.com/paper/2605.22530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 5 of 5)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Perpetual Assurances for Self-Adaptive Systems](https://mlsanity.com/paper/1903.04771/) (2019)
- [Towards an Argument Pattern for the Use of Safety Performance Indicators](https://mlsanity.com/paper/2410.00578/) (2024)
- [Reconciling Safety Measurement and Dynamic Assurance](https://mlsanity.com/paper/2405.19641/) (2024)

## Abstract (arXiv)

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.