  Reliable tracking algorithms are essential for automated driving. However,
the existing consistency measures are not sufficient to meet the increasing
safety demands in the automotive sector. Therefore, this work presents a novel
method for self-assessment of single-object tracking in clutter based on Kalman
filtering and subjective logic. A key feature of the approach is that it
additionally provides a measure of the collected statistical evidence in its
online reliability scores. In this way, various aspects of reliability, such as
the correctness of the assumed measurement noise, detection probability, and
clutter rate, can be monitored in addition to the overall assessment based on
the available evidence. Here, we present a mathematical derivation of the
reference distribution used in our self-assessment module for our studied
problem. Moreover, we introduce a formula that describes how a threshold should
be chosen for the degree of conflict, the subjective logic comparison measure
used for the reliability decision making. Our approach is evaluated in a
challenging simulation scenario designed to model adverse weather conditions.
The simulations show that our method can significantly improve the reliability
checking of single-object tracking in clutter in several aspects.
