  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.
