Model-based multi-sensor multi-object tracking approaches crucially depend on their prediction and measurement models. Typically, the approaches work well if the models and their parameters fit the current situation. For a safe operation, it is, therefore, crucial to continuously monitor them. In general, no performance guarantees for the filter can be made in the case of non-fitting models. On the other hand, if the models match, the mathematical properties of the tracking approach, such as Bayes optimality, apply. Distinguishing the two cases leads to a more interpretable and trustable tracking result and provides useful insights for modules later in the processing chain. This paper proposes two methods for monitoring two different parameters of the multi-object measurement model. The evaluation based on simulated data shows that both methods can detect wrong filter parameters, which enhances the reliability of the tracking approach.

Decision making in automated vehicles is based on the environment model, which is typically computed by a tracking module from information gathered by sensors. Thus, for safe and robust operation of the vehicle, the assessment of the current quality of the tracking module is crucial. This work makes a step towards this goal by providing a clutter rate estimation method with a self-monitored quality assessment for the labeled multi-Bernoulli filter. The significance of the proposed quality index is demonstrated by comparing it with the actual estimation error calculated with ground truth data. The simulation results show that the developed quality index is a meaningful value that can be computed online without the need for ground truth data. Moreover, it is competitive and closely related to the estimation error.

With this work, we contribute novel operators and perspectives to the field of subjective logic. We propose a novel multi-source trust revision approach enabling multisource fusion, which considers majority tendencies to mitigate occurring conflicts. For this, the degree of conflict is extended for a multi-source use, which allows our definition of so-called conflict shares. Subsequently, combining our and existing trust revision methods, we propose a generalized trust revision approach. Extending trust revision to subjective networks describing time-dependent processes, we propose the use of sub subjective networks and further the transition to recursive subjective networks. Finally, our trust revision approach and the sub subjective network proposal are evaluated and demonstrated based on experiments, which show conflict handling favoring majorities and an efficient evaluation of time-dependent decision processes.