English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Reliable tracking algorithms are essential for automated driving. However,the existing consistency measures are not sufficient to meet the increasingsafety demands in the automotive sector. Therefore, this work presents a novelmethod for self-assessment of single-object tracking in clutter based on Kalmanfiltering and subjective logic. A key feature of the approach is that itadditionally provides a measure of the collected statistical evidence in itsonline reliability scores. In this way, various aspects of reliability, such asthe correctness of the assumed measurement noise, detection probability, andclutter rate, can be monitored in addition to the overall assessment based onthe available evidence. Here, we present a mathematical derivation of thereference distribution used in our self-assessment module for our studiedproblem. Moreover, we introduce a formula that describes how a threshold shouldbe chosen for the degree of conflict, the subjective logic comparison measureused for the reliability decision making. Our approach is evaluated in achallenging simulation scenario designed to model adverse weather conditions.The simulations show that our method can significantly improve the reliabilitychecking of single-object tracking in clutter in several aspects.

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