In Europe, the major share of traffic accidents takes place at urban
intersections. In most of the cases, these accidents are the result of a
preceding driver error. One possibility for avoiding these accidents would
be to employ a vehicle-mounted Advanced Driver Assistance System (ADAS)
that supports the driver during negotiating an intersection and warns of
possible hazards. For this purpose, the ADAS would be required to assess
complex traffic situations comprehensively in order to anticipate the
future evolution of the current situation. At the time being, there is no
system available offering this functionality, and also in academia there
are no suitable methods for performing a comprehensive situation
assessment.
The work at hand presents a novel method for assessing urban intersection
situations. It is based on the insight that these traffic situations are
too complex and too variable to assess them as a whole. Therefore, a
decomposition of the traffic situation into smaller, more manageable parts
is proposed. Each part consists of a pair of interrelated entities, where
one entity affects the behavior of the other, for example a vehicle forcing
a trailing vehicle to slow down. Possible constellations of interrelated
entities are defined by a human expert in models which are tagged
'configurations'. 
In the course of this work, a set of methods was developed that employ the
concept of configurations. Besides  fundamental methods aiming at the
recognition of a road user's configuration also novel approaches for
predicting the behavior or the upcoming maneuver of a vehicle are
presented, which take the configuration of the vehicle explicitly into
account. In an extensive evaluation, it is shown that configurations can be
robustly recognized and are suited for a comprehensive situation
assessment. In addition, the proposed prediction methods excel the
state-of-the-art methods used for reference which can be traced back to the
consideration of configuration-information.