Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional state-space and learn compact surrogate representations of the operational state spaces. However, the problem is that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution. These conditions that do not belong to training distribution are referred to as Out-of-Distribution (OOD). Detecting OOD conditions at runtime is critical for the safety of CPS. In addition, it is also desirable to identify the context or the feature(s) that are the source of OOD to select an appropriate control action to mitigate the consequences that may arise because of the OOD condition. In this paper, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution. A common approach to solving this problem is the use of multi-chained one-class classifiers. However, this approach is expensive for CPSs that have limited computational resources and require short inference times. Our contribution is an approach to design and train a single $\beta$-Variational Autoencoder detector with a partially disentangled latent space sensitive to variations in image features. We use the feature sensitive latent variables in the latent space to detect OOD images and identify the most likely feature(s) responsible for the OOD. We demonstrate our approach using an Autonomous Vehicle in the CARLA simulator and a real-world automotive dataset called nuImages.
翻译:深神经网络正在被积极用于设计自主网络物理系统。这些模型的优势在于能够处理高维状态空间和学习运行状态空间的缩略替代表示。然而,问题在于用于培训模型的抽样观测可能永远无法覆盖整个物理环境的状态空间,因此,该系统很可能在不属于培训分布的条件中运行。不属于培训分布的条件被称为“ODD ” (OOOD)。运行时检测 OOD 条件对于CPS的安全至关重要。此外,还有必要确定作为 OD 来源的背景或特征,以选择适当的控制动作,减轻可能因 OOD 条件而产生的后果。因此,在本文中,我们研究这一问题时标多时间序列 OOD 检测图像时发现问题,OD(OD) 是按时间窗口(更替点)和整个培训数据分布中部分顺序定义的ODD(OD) 。在一次测试中,ODOD 一种常见的方法是用最昂贵的ODOD, 也就是用最昂贵的OD(OD) 时间里, 一种常见的方法来解决我们货币的OD 。