Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i.e. knowledge-based methods) may be prone to errors, such as false positive/negative classification of scenarios that lie on the border of two scenario classes, missing unknown scenario classes, or even failing to detect anomalies. On the other hand, verification of labels by annotators is not cost-efficient. For this purpose, active learning (AL) could potentially improve the annotation procedure by including an annotator/expert in an efficient way. In this study, we develop a generic active learning framework to annotate driving trajectory time series data. We first compute an embedding of the trajectories into a latent space in order to extract the temporal nature of the data. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any classification method and any query strategy, regardless of the structure of the original time series data. Furthermore, we utilize our active learning framework to discover unknown driving scenario trajectories. This will ensure that previously unknown trajectory types can be effectively detected and included in the labeled dataset. We evaluate our proposed framework in different settings on novel real-world datasets consisting of driving trajectories collected by Volvo Cars Corporation. We observe that active learning constitutes an effective tool for labelling driving trajectories as well as for detecting unknown classes. Expectedly, the quality of the embedding plays an important role in the success of the proposed framework.

网络邻接矩阵的光谱嵌入通常产生大约围绕低维子纤维结构的节点表示。特别地，当从潜在位置模型产生图表时，期望隐藏的子结构出现。此外，网络内的社区存在可能在嵌入中生成特定的特定社区的子多种结构，但是在网络的大多数统计模型中，这不明确地解释。在本文中，提出了一类称为潜在结构块模型（LSBM）的模型来解决这种情况，允许在存在社区特定的一维歧管结构时允许图形聚类。 LSBMS专注于特定的潜伏空间模型，随机点产品图（RDPG），并为每个社区的潜在位置分配潜在的子多种。讨论了来自LSBMS引起的嵌入式的贝叶斯模型，并显示在模拟和现实世界网络数据上具有良好的性能。该模型能够正确地恢复生活在一维歧管中的底层社区，即使当底层曲线的参数形式未知，也可以在各种实际数据上实现显着的结果。

提出了一种新的动态网络模型，称为相互刺激的点处理图（MEG）。 MEG是一种可扩展的网络范围统计模型，用于多达数码标记的点进程，可用于评估未来事件的重要事件时，包括以前未观察到的连接的异常检测。该模型组合了互励磁点过程来估计事件和潜在空间模型之间的依赖性，以推断节点之间的关系。每个网络边缘的强度函数专用于节点特定参数参数，允许跨网络共享信息。这种结构甚至可以估计强度，即使对于未被观察的边缘，这在现实世界中尤其重要，例如网络安全中产生的计算机网络。获得了日志似然的递归形式，用于通过现代梯度上升算法推导快速推理过程。也导出了EM算法。该模型在模拟图和现实世界数据集上进行测试，展示出色的性能。