系统论视野中的不可分割性
System Theoretic View on Uncertainties
摘要：
操作环境的复杂性和高度自动化驾驶所需的技术是前所未有的。除了Laprie等人的故障-错误-故障模型之外，还有一种对安全运行的不同类型的威胁，即性能限制。我们提出了一个系统理论的方法来处理这些问题，并得出一个分类的基础上的不确定性，即缺乏知识，作为一个根本原因。不确定性是对系统可靠性的威胁，因为它限制了我们评估其可靠性属性的能力。我们区分偶然性（固有的概率模型），认识（缺乏模型参数知识）和本体论（模型的不完整性）的不确定性，以确定应对它们的策略和方法。类似于Laprie等人的分类法，我们将方法归类为不确定性预防（使用具有已知行为的元素，避免易于出现紧急行为的架构，限制操作设计域等），消除不确定性（在设计期间通过实验设计等，在发布后通过现场观察、持续更新等），不确定性容限（使用具有不同不确定性的冗余架构，不确定性感知深度学习等）以及不确定性预测（剩余不确定性的估计等）。
英文摘要：
The complexity of the operating environment and required technologies for highly automated driving is unprecedented. A different type of threat to safe operation besides the fault-error-failure model by Laprie et al. arises in the form of performance limitations. We propose a system theoretic approach to handle these and derive a taxonomy based on uncertainty, i.e. lack of knowledge, as a root cause. Uncertainty is a threat to the dependability of a system, as it limits our ability to assess its dependability properties. We distinguish uncertainties by aleatory (inherent to probabilistic models), epistemic (lack of model parameter knowledge) and ontological (incompleteness of models) in order to determine strategies and methods to cope with them. Analogous to the taxonomy of Laprie et al. we cluster methods into uncertainty prevention (use of elements with well-known behavior, avoiding architectures prone to emergent behavior, restriction of operational design domain, etc.), uncertainty removal (during design time by design of experiment, etc. and after release by field observation, continuous updates, etc.), uncertainty tolerance (use of redundant architectures with diverse uncertainties, uncertainty aware deep learning, etc.) and uncertainty forecasting (estimation of residual uncertainty, etc.).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
