Griebel, Thomas (Ulm University), Scheible, Alexander (Ulm University), Buchholz, Michael (Universität Ulm), Dietmayer, Klaus (University of Ulm)

Towards an Advanced Self-Monitoring Tracking Module: Leveraging Statistical Hypothesis Tests and Subjective Logic Reasoning

Scheduled for presentation during the Invited Session "Self-Assessment of Perception Systems" (WeAT5), Wednesday, September 25, 2024, 11:30−11:50, Salon 13

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 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.