  Trajectory datasets of road users have become more important in the last
years for safety validation of highly automated vehicles. Several naturalistic
trajectory datasets with each more than 10.000 tracks were released and others
will follow. Considering this amount of data, it is necessary to be able to
compare these datasets in-depth with ease to get an overview. By now, the
datasets' own provided information is mainly limited to meta-data and
qualitative descriptions which are mostly not consistent with other datasets.
This is insufficient for users to differentiate the emerging datasets for
application-specific selection. Therefore, an automated analysis framework is
proposed in this work. Starting with analyzing individual tracks, fourteen
elementary characteristics, so-called detection types, are derived and used as
the base of this framework. To describe each traffic scenario precisely, the
detections are subdivided into common metrics, clustering methods and anomaly
detection. Those are combined using a modular approach. The detections are
composed into new scores to describe three defined attributes of each track
data quantitatively: interaction, anomaly and relevance. These three scores are
calculated hierarchically for different abstract layers to provide an overview
not just between datasets but also for tracks, spatial regions and individual
situations. So, an objective comparison between datasets can be realized.
Furthermore, it can help to get a deeper understanding of the recorded
infrastructure and its effect on road user behavior. To test the validity of
the framework, a study is conducted to compare the scores with human
perception. Additionally, several datasets are compared.
