@inproceedings {PUBA266,
	title = {Learning Situation Models from Velocity Profiles},
	author = {Matthias Platho AND Horst-Michael Gro{\ss} AND Julian Eggert},
	year = {2013},
	month = {October},
	abstract = {For an Advanced Driver Assistance System recognizing
the driving situation of other vehicles is a crucial prerequisite
to anticipate their behavior and plan own maneuvers
accordingly. Current methods for situation recognition usually
rely on an expert for defining the considered driving situations
manually while solely the parameters of the corresponding
behavior models are learned from observations. Unfortunately,
the performance of this approach is highly dependent on the
skills of the expert. Furthermore, the data for training needs to
be manually labeled to define when a certain type of situation is
present, which can be very time-consuming and may introduce
unwanted bias. In order to circumvent these problems, we
propose to learn types of situations and behavior models from
data simultaneously. The goal is to identify the set of driving
situations for which the corresponding behavior models achieve
the best fit to given observations. As both the assignment of
observations to driving situations and the model parameters
are unknown, an alternating, iterative algorithm minimizing
the model error is employed. We show that the algorithm
accomplishes to identify reasonable driving situations and that
it can be successfully applied for behavior prediction when
situation labels are missing.},
	publisher = {ITSC},
	booktitle = {IEEE Intelligent Transportation Systems Conference (ITSC)},
	city = {The Hague, The Netherlands},
	pages = {276-281}
}