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

TL;DR

This paper presents a flexible event-based method for conservatively compressing covariance matrices, enabling efficient data transmission with minimal loss by selectively transmitting matrix elements based on learned triggering conditions.

Contribution

It introduces a novel event-based approach with parametrizable triggering conditions for covariance matrix compression, including a method to learn these parameters from data.

Findings

Achieves substantial data reduction with minimal over-conservativeness

Demonstrates effective learning of triggering parameters from real-world data

Validates approach on vehicle trajectory datasets

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 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.