论文标题
促进连续ML安全保证的变更实施
Facilitating Change Implementation for Continuous ML-Safety Assurance
论文作者
论文摘要
我们提出了一种将安全至关重要的机器学习组件部署到不断发展的环境中的方法，在该环境中，在工程过程中需要增加自动化程度。我们将语义标签与安全案例论证相关联，并将每个证据转换为定量度量或逻辑公式。在适当的工具支持下，影响的特征是在安全论证树上查询，以突出证据变成无效的证据。该概念是使用基于视觉的紧急制动系统的自动引导车辆进行工厂自动化的。
We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering process is desired. We associate semantic tags with the safety case argumentation and turn each piece of evidence into a quantitative metric or a logic formula. With proper tool support, the impact can be characterized by a query over the safety argumentation tree to highlight evidence turning invalid. The concept is exemplified using a vision-based emergency braking system of an autonomous guided vehicle for factory automation.
