@inproceedings {PUBA237,
	title = {Accurate Behavior Prediction on Highways Based on a Systematic Combination of Classifiers},
	author = {Sarah Bonnin AND Thomas H Weisswange AND Franz Kummert AND Jens Schm{\"u}dderich},
	year = {2013},
	month = {June},
	abstract = {To drive safe, a good driver will observe his surroundings, anticipate the actions of other traffic participants and then decide for a maneuver. But if a driver is inattentive or overloaded he may fail to include some relevant information. This can than lead to wrong decisions and potentially result in an accident. In order to assist a driver in his decision making, Advanced Driver Assistance Systems (ADAS) are becoming more and more popular in commercial cars. The quality of these existing systems is weak compared to the experienced driver, because they rely purely on physical observation and thus react shortly before an accident. One idea for an earlier warning of the driver is to use behavior prediction. We classify existing research in this area with respect to two aspects, quality and scope. Quality means the ability to warn early a driver before a dangerous situation and scope is the variety of scenes in which the approach can work. In general we see two tendencies, those methods targeting for large scope but low quality and those targeting for low scope but high quality. Our goal is to have a system with high quality and high scope. To achieve this, we propose a system that combines classifiers to predict behaviors for many scenarios. To show that a combination between generic and specific classifiers is a solution to robustly improve accuracy, this paper will introduce the generic concept of our system followed by a concrete implementation for a lane
change prediction for highway scenarios.},
	publisher = {IEEE},
	booktitle = {Intelligent Vehicles Symposium (IV)},
	pages = {242--249}
}