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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.22530 |
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| _version_ | 1866913152848887808 |
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| author | Herd, Benjamin Kelly, Jessica Heinemann, Clarissa Zacchi, João-Vitor |
| author_facet | Herd, Benjamin Kelly, Jessica Heinemann, Clarissa Zacchi, João-Vitor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22530 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Subjective Logic-based method for runtime confidence updates in safety arguments Herd, Benjamin Kelly, Jessica Heinemann, Clarissa Zacchi, João-Vitor Artificial Intelligence 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. |
| title | A Subjective Logic-based method for runtime confidence updates in safety arguments |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.22530 |