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.

Monitoring and self-assessment of tracking algorithms are essential in modern automated driving systems. However, the further use of this self-assessment information is another growing and not thoroughly studied area of research. One option is to adapt the parameters configured in the tracking algorithm online to obtain better and more robust tracking results directly. The paper proposes a novel overall concept and framework for adaptive Kalman filtering using subjective logic. Based on a self-assessment method, we present multiple variants of adaptive strategies to adapt the noise assumptions online for Kalman filtering. This paper focuses mainly on adaptation procedures for multi-sensor Kalman filters. The proposed method is evaluated in various experiments and compared with state-of-the-art adaptive Kalman filters.

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.

Operation monitoring for automation systems requires self-assessment of all data processing modules. In this work, we extend our new self-assessment method for linear Kalman filters based on subjective logic to nonlinear Kalman filtering. Furthermore, we propose novel approaches within this subjective logic-based framework to assess the overall filter performance in multi-sensor systems online, i.e., in real-time without ground truth data. The results of the proposed self-assessment method for nonlinear Kalman filtering are demonstrated through simulation studies, showing advantages compared to classical consistency measures, like the normalized innovation squared. In addition, the results of the proposed online overall filtering assessment for multi-sensor systems can even compete with consistency measures based on ground truth data, which cannot be applied in online applications.

The multi-sensor Labeled Multi-Bernoulli filter has the challenge of relying on the NP-hard multi-sensor update of the Generalized Labeled Multi-Bernoulli filter. This paper proposes the Fast Product Multi-Sensor Labeled Multi-Bernoulli filter, which is a filter for multi-sensor systems that solves this task by performing computationally simpler single-sensor Labeled Multi-Bernoulli filter updates based on a common prediction for each sensor. These single-sensor updates are then fused using a novel and efficient fusion strategy. Furthermore, the proposed filter is based on the Bayes parallel combination rule and can be seen as an efficient approximation of the multi-sensor Labeled Multi-Bernoulli filter. It enables full parallelization of the update step and benefits from sensor order independence compared to Iterated Corrector implementations. As a result, the robustness is increased, which is important for safety reasons, e.g., in autonomous driving. Our approach is evaluated on simulations, and the results are compared to an Iterated Corrector implementation of the Labeled Multi-Bernoulli filter.

The main challenge in random finite set-based multi-sensor multi-object tracking is the NP-hard association of the sensor measurements with the tracks. Using the Bayes parallel combination rule, we have recently proposed the Product Multi-sensor Generalized Labeled Multi-Bernoulli (PM-GLMB) filter, decomposing the multi-sensor update into simpler single-sensor problems with subsequent Bayes optimal fusion. This paper extends the filter to prior densities with Gaussian mixture spatial distributions, which is an inevitable prerequisite for the Product Multi-sensor Labeled Multi-Bernoulli (PM-LMB) filter we propose afterward. Finally, we evaluate the performance of both in a simulation study. In this context, we address the known performance problems of the PM-GLMB filter in crowded situations and show how the PM-LMB filter overcomes these.

A main task for automated vehicles is an accurate and robust environment perception. Especially, an error-free detection and modeling of other traffic participants is of great importance to drive safely in any situation. For this purpose, multi-object tracking algorithms, based on object detections from raw sensor measurements, are commonly used. However, false object hypotheses can occur due to a high density of different traffic participants in complex, arbitrary scenarios. For this reason, the presented approach introduces a probabilistic model to verify the existence of a tracked object. Therefore, an object verification module is introduced, where the influences of multiple digital map elements on a track's existence are evaluated. Finally, a probabilistic model fuses the various influences and estimates an extended existence probability for every track. In addition, a Bayes Net is implemented as directed graphical model to highlight this work's expandability. The presented approach, reduces the number of false positives, while retaining true positives. Real world data is used to evaluate and to highlight the benefits of the presented approach, especially in urban scenarios.

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