An Event-Based Approach for the Conservative Compression of Covariance Matrices

Abstract

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 triggering condition is satisfied for the element. Here, triggering conditions can be parameterized 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. The method is simple to implement, computationally efficient, and thus, suitable for resource-constrained systems. In addition, 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 with minimal overconservativeness. The feasibility of learning triggering condition parameters is demonstrated.