A novel approach is presented for learning probabilistic transition matrices from temporal data series as switching models based on Generalized States (GS). An observed data sequence is analyzed by a reference filter whose errors are clustered. Each cluster is associated with a dynamic flow model, which described as a parametric linear attractor. The set of linear attractors define the Hierarchical Generalized Dynamic Bayesian Network (H-GDBN), which encodes a learned model of the obtained sequence. A Markov Jump Particle Filter (MJPF) uses H-GDBN’s probabilistic information to make inferences at a multilevel scale and facilitates the detection of abnormalities. This paper shows how transition matrices can be obtained as an integral part of the clustering step by employing the advantages of GSs, enabling a unique optimal criterion for learning flow models at discrete and continuous levels of H-GDBN. For evaluating the proposed method, odometry and proprioceptive control data from an autonomous vehicle are employed to learn H-GDBNs. Learned H-GDBN are used to detect abnormalities when vehicle encounter any abnormal situation. Performance evaluation based on ROC curves is provided to select the optimal transition matrix.

提出了一种新方法，用于从时间数据序列中学习概率转移矩阵作为基于广义状态 (GS) 的切换模型。观察到的数据序列由参考滤波器分析，其误差被聚类。每个集群都与一个动态流模型相关联，该模型被描述为参数线性吸引子。线性吸引子集定义了分层广义动态贝叶斯网络 (H-GDBN)，它对获得的序列的学习模型进行编码。马尔可夫跳跃粒子滤波器 (MJPF) 使用 H-GDBN 的概率信息在多级尺度上进行推断并促进异常检测。本文展示了如何通过利用 GS​​ 的优势将转移矩阵作为聚类步骤的一个组成部分获得，在 H-GDBN 的离散和连续级别上为学习流模型提供独特的最佳标准。为了评估所提出的方法，使用来自自动驾驶汽车的里程计和本体感受控制数据来学习 H-GDBN。学习到的 H-GDBN 用于在车辆遇到任何异常情况时检测异常。提供基于 ROC 曲线的性能评估以选择最佳转换矩阵。