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Self-assessment is a key to safety and robustness in automated driving. Inorder to design safer and more robust automated driving functions, the goal isto self-assess the performance of each module in a whole automated drivingsystem. One crucial component in automated driving systems is the tracking ofsurrounding objects, where the Kalman filter is the most fundamental trackingalgorithm. For Kalman filters, some classical online consistency measures existfor self-assessment, which are based on classical probability theory. However,these classical approaches lack the ability to measure the explicit statisticaluncertainty within the self-assessment, which is an important quality measure,particularly, if only a small number of samples is available for theself-assessment. In this work, we propose a novel online self-assessment methodusing subjective logic, which is a modern extension of probabilistic logic thatexplicitly models the statistical uncertainty. Thus, by embedding classicalKalman filtering into subjective logic, our method additionally features anexplicit measure for statistical uncertainty in the self-assessment.

Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic