论文标题
实时启用学习的网络物理系统中的实时分布检测
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems
论文作者
论文摘要
通过使用可以处理现实世界中不确定性和可变性的机器学习组件，网络物理系统（CPS）极大地受益。但是，诸如深神经网络之类的典型组件会引入可能影响系统安全性的新型危害。系统行为取决于仅在运行时可用的数据，并且可能与用于培训的数据不同。分发数据可能会导致较大的错误并损害安全性。本文考虑了在CPS控制系统中有效检测到分布数据的问题。检测必须是强大的，并限制错误警报的数量，同时计算为实时监视的计算效率。提出的方法利用了诱导的保形预测和异常检测来开发具有良好校准的错误警报率的方法。我们使用各种自动编码器和深层支持矢量数据描述来学习可以有效使用的模型，以相对于训练集，可以计算新输入的不符合性，并启用实时检测分布不足的高维输入。我们使用高级紧急制动系统和在开源模拟器中实现的自动驾驶汽车中实现的自动驱动器端到端控制器演示了该方法。仿真结果显示，在执行时间与原始机器学习组件的执行时间相当的同时，误报数量和检测延迟很少。
Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep neural networks, however, introduce new types of hazards that may impact system safety. The system behavior depends on data that are available only during runtime and may be different than the data used for training. Out-of-distribution data may lead to a large error and compromise safety. The paper considers the problem of efficiently detecting out-of-distribution data in CPS control systems. Detection must be robust and limit the number of false alarms while being computational efficient for real-time monitoring. The proposed approach leverages inductive conformal prediction and anomaly detection for developing a method that has a well-calibrated false alarm rate. We use variational autoencoders and deep support vector data description to learn models that can be used efficiently compute the nonconformity of new inputs relative to the training set and enable real-time detection of out-of-distribution high-dimensional inputs. We demonstrate the method using an advanced emergency braking system and a self-driving end-to-end controller implemented in an open source simulator for self-driving cars. The simulation results show very small number of false positives and detection delay while the execution time is comparable to the execution time of the original machine learning components.
