Self-Assessment for Single-Object Tracking in Clutter Using Subjective Logic

TL;DR

This paper introduces a novel self-assessment method for single-object tracking in cluttered environments using subjective logic, enhancing reliability measures with statistical evidence in automated driving scenarios.

Contribution

It presents a new approach combining Kalman filtering and subjective logic for online reliability assessment, including evidence-based confidence measures and conflict thresholding.

Findings

Improves reliability assessment accuracy in cluttered scenarios

Provides a statistical measure of evidence for tracking confidence

Enhances safety in automated driving through better self-assessment

Abstract

Reliable tracking algorithms are essential for automated driving. However, the existing consistency measures are not sufficient to meet the increasing safety demands in the automotive sector. Therefore, this work presents a novel method for self-assessment of single-object tracking in clutter based on Kalman filtering and subjective logic. A key feature of the approach is that it additionally provides a measure of the collected statistical evidence in its online reliability scores. In this way, various aspects of reliability, such as the correctness of the assumed measurement noise, detection probability, and clutter rate, can be monitored in addition to the overall assessment based on the available evidence. Here, we present a mathematical derivation of the reference distribution used in our self-assessment module for our studied problem. Moreover, we introduce a formula that describes how a threshold should be chosen for the degree of conflict, the subjective logic comparison measure used for the reliability decision making. Our approach is evaluated in a challenging simulation scenario designed to model adverse weather conditions. The simulations show that our method can significantly improve the reliability checking of single-object tracking in clutter in several aspects.