@inproceedings {pub3201,
	title = {Probabilistic, Discriminative Maneuver Estimation in Generic Traffic Scenes using Pairwise Probability Coupling},
	author = {Stefan Klingelschmitt AND Volker Willert AND Julian Eggert},
	year = {2016},
	month = {November},
	abstract = {Future advanced driver assistance system as well as autonomous vehicles are expected to further increase their areas of applicability. Reliable maneuver estimations are a prerequisite for many of the provided functionality. Accordingly, maneuver estimation systems need to cover a wide range of scenarios. The majority of recently presented approaches are targeted at fixed scenarios. However, having specialized maneuver estimation systems covering each possible scenario is unrealistic. Therefore, we present an approach for making discriminative maneuver estimations in generic traffic scenes. It is based on reusable, partial classifiers that are combined online using a technique called pairwise probability coupling. As a result we are able to make discriminative maneuver estimations in generic traffic scenes. The benefits and applicability are presented on an inner-city real-world data set. The evaluation indicates that the assembled probabilistic maneuver estimation is not only able to outperform generative models; it surpasses the performance of specially designed models due to the reduced complexities of the partial classifiers.},
	publisher = {IEEE},
	booktitle = {Intelligent Transportation Systems Conference (ITSC) 2016},
	pages = {1269-1276}
}