@inproceedings {pub3106,
	title = {Probabilistic Situation Assessment Framework for Multiple Interacting Traffic Participants in Generic Traffic Scenes},
	author = {Stefan Klingelschmitt AND Florian Damerow AND Volker Willert AND Julian Eggert},
	year = {2016},
	month = {June},
	abstract = {Situation recognition is a prerequisite for many advanced drivers assistance systems as well as partially and fully automated vehicles. Various approaches have been targeted at estimating maneuvers of single scene entities. However, assessing multiple, possibly interacting, traffic participants simultaneously is crucial in complex traffic scenes and has hardly been investigated. Considering the variability and combinatorics of such scenarios, having specialized situation recognition systems covering each case directly is unrealistic. In this paper, we present a flexible framework for assessing generic traffic scenes with multiple interacting traffic participants. It is able to construct a fully interaction-respecting probabilistic situation assessment, while relying on reusable state-of-the art single-entity-based maneuver predictions. The benefits and applicability are presented on a large real-world data set. The evaluation indicates that the approach is not only able to reconstruct underlying interdependent probability distributions; it outperforms specially designed models, due to the reduced model complexities.},
	publisher = {IEEE},
	booktitle = {Intelligent Vehicles Symposium (IV)},
	pages = {1141-1148}
}