@inproceedings {pub2707,
	title = {Pedestrian Crossing Prediction using Multiple Context-based Models},
	author = {Sarah Bonnin AND Thomas H Weisswange AND Franz Kummert AND Jens Schm{\"u}dderich},
	year = {2014},
	month = {October},
	abstract = { In inner-city, most vehicle-pedestrian collisions
occur when a pedestrian is crossing the road and the driver
does not see or pay attention to him. Current ADAS warn the
driver or apply the brakes shortly before the collision, but in
some situations the collision cannot be fully avoided because
most systems react only when the pedestrian is already in front
of the vehicle. To fully avoid a collision, a driver should be
warned earlier. Behavior prediction is a solution that can be
used to warn a driver before the pedestrian starts crossing. In
this paper, we propose a generic context based model to predict
crossing behaviors of pedestrians in inner-city. We will show
that our model provides accurate prediction at an early time.
However, there are specific locations such as zebra crossings,
where based on expert driving experience, one would expect
that a prediction can be done even earlier. Therefore, we have
developed an additional specific model fitted to the context of
zebra crossings. The experiments show that this model produces
both, better and earlier predictions in this specific context.
Because our goal is to build a generic crossing prediction
system, we finally apply the framework of the {\textquoteleft}Context Model
Tree{\textquoteright} to combine the two models. We demonstrate that this
multi-model system is well suited to provide early predictions
for realistic data, including both, generic inner-city situations
and zebra crossings.},
	publisher = {IEEE},
	booktitle = {17th International IEEE Conference on Intelligent Transportation Systems},
	city = {Qingdao, China}
}