Traditional safety-critical systems are engineered in a way to be predictable in all operating conditions. They are common in industrial automation and transport applications where uncertainties (e.g., fault occurrence rates) can be modeled and precisely evaluated. Furthermore, they use high-cost hardware components to increase system reliability. On the contrary, future systems are increasingly required to be “smart” (or “intelligent”) that is to adapt to new scenarios, learn and react to unknown situations, possibly using low-cost hardware components. In order to move a step forward to fulfilling those new expectations, in this paper we address run-time stochastic evaluation of quantitative safety targets, like hazard rate, in self-adaptive event detection systems by using Bayesian Networks and their extensions. Self-adaptation allows changing correlation schemes on diverse detectors based on their reputation, which is continuously updated to account for performance degradation as well as modifications in environmental conditions. To that aim, we introduce a specific methodology and show its application to a case-study of vehicle detection with multiple sensors for which a real-world data-set is available from a previous study. Besides providing a proof-of-concept of our approach, the results of this paper pave the way to the introduction of new paradigms in the dynamic safety assessment of smart systems.

传统的安全关键系统经过精心设计，可以在所有操作条件下预测。它们在可以对不确定性（例如故障发生率）进行建模和精确评估的工业自动化和运输应用中很常见。此外，他们使用高成本的硬件组件来提高系统可靠性。相反，未来的系统越来越需要“智能”（或“智能”），以适应新情况，学习未知情况并做出反应，并可能使用低成本硬件组件。为了向满足这些新期望的方向迈出一步，在本文中，我们使用贝叶斯网络及其扩展解决了自适应事件检测系统中定量安全目标（如危险率）的运行时随机评估。自适应允许根据其信誉来更改各种检测器上的相关方案，并不断对其进行更新以解决性能下降以及环境条件的变化。为此，我们介绍了一种特定的方法，并展示了其在具有多个传感器的车辆检测案例研究中的应用，该案例可从先前的研究中获得真实的数据集。除了提供我们方法的概念验证之外，本文的结果还为在智能系统的动态安全评估中引入新的范式铺平了道路。我们介绍了一种特定的方法，并展示了其在具有多个传感器的车辆检测案例研究中的应用，该案例可从先前的研究中获得真实的数据集。除了提供我们方法的概念验证之外，本文的结果还为在智能系统的动态安全评估中引入新的范式铺平了道路。我们介绍了一种特定的方法，并展示了其在具有多个传感器的车辆检测案例研究中的应用，该案例可从先前的研究中获得真实的数据集。除了提供我们方法的概念验证之外，本文的结果还为在智能系统的动态安全评估中引入新的范式铺平了道路。