@article {PUBA287,
	title = {General Behavior Prediction By A Combination Of Situation Specific Models},
	author = {Sarah Bonnin AND Thomas H Weisswange AND Franz Kummert AND Jens Schm{\"u}dderich},
	year = {2014},
	month = {February},
	abstract = {Before taking a decision, a driver anticipates the
future behavior of other traffic participants. However, if a driver
is inattentive or overloaded he may fail to consider relevant
information. This can lead to bad decisions and potentially result
in an accident. A computational system designed to anticipate
other traffic participants{\textquoteright} behaviors could assist the driver in his
decision making by sending him an early warning when a risk
of collision is predicted. Existing research in this area usually
focuses on only one of two aspects: quality or scope. Quality
refers to the ability to warn a driver early before a dangerous
situation happens. Scope is the diversity of scenarios in which the
approach can work. In general we see methods targeting broad
scope but showing low quality and others having narrow scope
but high quality. Our goal is to create a system with high quality
and high scope. To achieve this, we propose an architecture that
combines classifiers to predict behaviors for many scenarios. In
this paper we will first introduce the generic concept of such
a system applicable to highway scenarios as well as inner-city
scenarios. We will show that a combination of general and specific
classifiers is a solution to improve quality and scope based on a
concrete implementation for lane change prediction in highway
scenarios.},
	publisher = {IEEE Press},
	journal = {IEEE Transactions on Intelligent Transportation Systems},
	volume = {15},
	number = {4},
	pages = {1478-1488}
}