@inproceedings {PUBA37,
	title = {Situation specific learning for an ego-vehicle behavior prediction scenario},
	author = {Michael Garcia Ortiz AND Jens Schm{\"u}dderich AND Franz Kummert AND Alexander Gepperth},
	year = {2011},
	month = {October},
	abstract = {We present a system able to predict the future behavior of the ego-vehicle in an inner-city environment. Our system learns the mapping between the current perceived scene (information about the ego-vehicle and the preceding vehicle, as well as information about the possible traffic lights) and the future driving behavior of the ego-vehicle. We improve the prediction accuracy by estimating the prediction confidence and by discarding unconfident samples. The behavior of the driver is represented as a sequence of elementary states termed behavior primitives. These behavior primitives are abstractions from the raw actuator states. Behavior prediction is therefore considered to be a multi-class learning problem.
In this contribution, we explore the possibilities of situation-specific learning. We show that decomposing the perceived complex situation into a combination of simpler ones, each of them with a dedicated prediction, allows the system to reach a performance equivalent to a system without situation-specificity. We believe that this is advantageous for the scalability of the approach to the number of possible situations that the driver will encounter. The system is tested on a real world scenario, using streams recorded in inner-city scenes. The prediction is evaluated for a prediction horizon of 3s into the future, and the quality of the prediction is measured using established evaluation methods.},
	publisher = {IEEE},
	booktitle = {IEEE Intelligent Transportation Systems Conference (ITSC)},
	city = {Washington, DC, USA},
	pages = {1237 - 1242}
}