Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic

TL;DR

This paper introduces a novel online self-assessment method for Kalman filters in automated driving, using subjective logic to explicitly model statistical uncertainty and improve safety and robustness.

Contribution

It combines Kalman filtering with subjective logic to provide explicit statistical uncertainty measures in self-assessment, enhancing safety in automated driving systems.

Findings

Provides explicit statistical uncertainty measurement in Kalman filter self-assessment

Improves robustness and safety in automated driving modules

Extends classical probabilistic approaches with subjective logic

Abstract

Self-assessment is a key to safety and robustness in automated driving. In order to design safer and more robust automated driving functions, the goal is to self-assess the performance of each module in a whole automated driving system. One crucial component in automated driving systems is the tracking of surrounding objects, where the Kalman filter is the most fundamental tracking algorithm. For Kalman filters, some classical online consistency measures exist for self-assessment, which are based on classical probability theory. However, these classical approaches lack the ability to measure the explicit statistical uncertainty within the self-assessment, which is an important quality measure, particularly, if only a small number of samples is available for the self-assessment. In this work, we propose a novel online self-assessment method using subjective logic, which is a modern extension of probabilistic logic that explicitly models the statistical uncertainty. Thus, by embedding classical Kalman filtering into subjective logic, our method additionally features an explicit measure for statistical uncertainty in the self-assessment.