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

As the use of Artificial Intelligence (AI) components in cyber-physicalsystems is becoming more common, the need for reliable system architecturesarises. While data-driven models excel at perception tasks, model outcomes areusually not dependable enough for safety-critical applications. In this work,wepresent a timeseries-aware uncertainty wrapper for dependable uncertaintyestimates on timeseries data. The uncertainty wrapper is applied in combinationwith information fusion over successive model predictions in time. Theapplication of the uncertainty wrapper is demonstrated with a traffic signrecognition use case. We show that it is possible to increase model accuracythrough information fusion and additionally increase the quality of uncertaintyestimates through timeseries-aware input quality features.

Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning