用主观逻辑动态更新安全论证中的置信度，实时反映系统运行状态。
A Subjective Logic-based method for runtime confidence updates in safety arguments
- 基于主观逻辑融合设计证据与运行时指标，实现置信度动态更新。
- 无违规时提升置信度，发生违规时立即降权，响应更及时。
- 适合高安全要求的自动驾驶、工业控制等场景的持续验证。
我们提出一种动态量化保障方法，将静态安全论证扩展为可连续更新的运行时置信度体系。该方法通过单一主观逻辑（Subjective Logic）框架，整合设计阶段证据与窗口化运行时安全性能指标（SPIs），实现置信度的量化传播。运行时持续评估SPI证据，当未检测到违规时提升相关主张置信度，一旦出现违规则立即施加惩罚性修正。该设计强调安全相关响应速度，而非严格遵循经典贝叶斯后验更新。我们在模拟的施工区域辅助功能中进行了验证，聚焦基于机器学习的施工锥桶检测组件，展示了在实际运行中随SPI证据积累置信度演化的全过程。
原文摘要 · Abstract (English)
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.
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