Towards an Advanced Self-Monitoring Tracking Module: Leveraging Statistical Hypothesis Tests and Subjective Logic Reasoning
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Date
2025-03-20
Authors
Griebel, Thomas
Scheible, Alexander
Buchholz, Michael
Dietmayer, Klaus
Journal Title
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Volume Title
Publication Type
Beitrag zu einer Konferenz
Published in
2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024
Abstract
In automated driving systems, monitoring and self-assessment of tracking algorithms is essential. This is especially necessary to meet today’s safety and robustness challenges in an automated system. We propose a hybrid approach to develop a self-monitoring module for tracking algorithms. It makes use of well-known statistical hypothesis testing techniques. The results of which are fed into a subjective logic-based reasoning framework to produce robust and reliable self-assessment scores. Hence, we investigate the potential of combining these two approaches for monitoring and self-assessment systems and show the significance of this approach in experimental results.
Description
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Institutions
Institut für Mess-, Regel- und Mikrotechnik
Citation
DFG Project uulm
EU Project THU
EVENTS / ReliablE in-Vehicle pErception and decisioN-making in complex environmenTal conditionS / EC / HE / 101069614
PoDIUM / PDI connectivity and cooperation enablers building trust and sustainability for CCAM / EC / HE / 101069547
PoDIUM / PDI connectivity and cooperation enablers building trust and sustainability for CCAM / EC / HE / 101069547
Other projects THU
License
CC BY 4.0 International
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DOI external
DOI external
10.1109/ITSC58415.2024.10920240
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DFG Project THU
item.page.thu.projectEU
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Series
Keywords
Self-assessment, Multi-sensor tracking, Subjective logic, Hypothesis testing, Monitoring, Statistical hypothesis testing, DDC 620 / Engineering & allied operations