This work introduces a flexible and versatile method for the data-efficient yet conservative transmission of covariance matrices, where a matrix element is only transmitted if a so-called triggering condition is satisfied for the element. Here, triggering conditions can be parametrized on a per-element basis, applied simultaneously to yield combined triggering conditions or applied only to certain subsets of elements. This allows, e.g., to specify transmission accuracies for individual elements or to constrain the bandwidth available for the transmission of subsets of elements. Additionally, a methodology for learning triggering condition parameters from an application-specific dataset is presented. The performance of the proposed approach is quantitatively assessed in terms of data reduction and conservativeness using estimate data derived from real-world vehicle trajectories from the InD-dataset, demonstrating substantial data reduction ratios with minimal over-conservativeness. The feasibility of learning triggering condition parameters is demonstrated.

翻译：本文提出了一种灵活且通用的方法，用于在数据高效的同时保守地传输协方差矩阵。其中，仅当某个矩阵元素满足所谓的触发条件时，才进行传输。触发条件可基于每个元素进行参数化，也可同时应用以生成组合触发条件，或仅应用于特定元素子集。这使得用户能够例如指定单个元素的传输精度，或限制用于传输特定元素子集的带宽。此外，本文还提出了一种从特定应用数据集中学习触发条件参数的方法。通过使用源自InD数据集的真实车辆轨迹估计数据，从数据压缩率与保守性两个维度对所提方法的性能进行了定量评估，结果表明该方法在实现显著数据压缩比的同时，仅产生极小的过度保守性。同时，本文验证了学习触发条件参数的可行性。