Self-Assessment for Multi-Object Tracking Based on Subjective Logic

Thomas Griebel, Nikolas Dehler, Alexander Scheible, Michael Buchholz, Klaus Dietmayer

Abstract: In automated driving, the safety and robustness of the overall system are among the most important key challenges today. To tackle these safety and robustness challenges, the monitoring and self-assessment of all modules in the automated system is necessary. Tracking surrounding objects as part of the environmental perception is a key module in automated systems. Thus, this work presents a novel overall concept and framework for self-assessment in multi-object tracking based on the subjective logic theory. The self-assessment concept is comprehensively discussed and evaluated by simulations and real-world data of the KITTI dataset, showing the relevance of this proposed method.