@inproceedings {pub2893,
	title = {Managing the Complexity of Inner-City Scenes: An Efficient Situation Hypotheses Selection Scheme},
	author = {Stefan Klingelschmitt AND Florian Damerow AND Julian Eggert},
	year = {2015},
	month = {June},
	abstract = {Due to the large number and the high variability
of possible traffic situations, intersections are among the most
accident-prone spots in inner-city traffic. To reliably assist the
driving tasks elaborated risk assessment systems are needed.
Current approaches are mainly based on the prediction of
possible future trajectories of the involved traffic participants.
However, considering the variability and combinatorics of
intersection-related traffic situations, this becomes unfeasible
for limited computational resources. Here, we present a general
framework for an efficient situation hypotheses selection system.
The selection process is based on reasoning about whether a
particular situation results in a threat for the ego vehicle{\textquoteright}s
behavior. Our approach combines the results of a probabilistic
situation recognition and a fast risk assessment using stateof-
the-art regression methods. We show that the proposed
system is able to effectively reduce the number of unnecessarily
considered situation hypotheses on average by over 80\%.},
	publisher = {IEEE},
	booktitle = {Intelligent Vehicles Symposium (IV) 2015},
	city = {Seoul, Korea}
}