Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three data sets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Further, the method is computationally efficient and allows real-time assurance monitoring of CPS.
翻译:在网络物理系统中广泛使用深神经网络等机器学习部件。然而,这些部件可能会带来新型危险,可能产生灾难性后果,需要为工程可信赖的系统加以解决。虽然深神经网络提供先进能力,但必须有工程方法和做法加以补充,以便能够有效地融入计算机辅助系统。在本文件中,我们提议了一种基于符合预测框架的对学习产生的计算机辅助系统进行保证监测的方法。为了进行实时保证监测,该方法利用远程学习将高维投入转换为较小尺寸的嵌入式演示。通过利用符合预测,该方法提供了良好校准的信心,并确保了有限的小误差率,同时限制了无法准确预测的投入数量。我们展示了在墙后使用三套移动机器人数据集、语音识别和交通信号识别的方法。实验结果显示,错误率是完全校准的,而警报数量则非常少。此外,该方法是计算高效的,允许实时监测计算机辅助系统。