@inproceedings {pub3121,
	title = {Inferring a Grid-based Road Representation from the behavior of real world traffic participants},
	author = {Edoardo Casapietra AND Thomas H Weisswange AND Franz Kummert AND Christian Goerick},
	year = {2016},
	month = {June},
	abstract = {The detection of road area in the surroundings of the ego-vehicle is a key issue for modern ADAS. Camera-based direct detection systems are able to reliably accomplish this task only within a limited spatial range or in very simple environment, due to hardware limitations and unfavorable situations, e.g. shadows or occlusions. In complex environments, like inner city, the traffic is a real issue, since the mere presence of other cars can significantly restrict the field of view of the ego-vehicle.  In order to extend the spatial range of road detection, indirect detection systems are a viable resource, apt to complement state-of-the-art direct detection systems and helping motion control systems to plan smooth and stable trajectories.
In this paper we propose a probabilistic grid-based approach based on the interpretation of the motion of the other vehicles in the scene. The approach uses the position and velocity of those vehicles in order to infer the presence and location of occluded road area. We will show that this approach can complement an already established feature-based detection system, taking advantage of the same situations that are the most challenging for the latter.
Evaluations on real-world scenes show that the union between this approach and direct road detection extends significantly the spatial range of detection, thus providing a hypothetical motion control system a longer time horizon for planning trajectories. },
	publisher = {IEEE},
	booktitle = {IEEE Intelligent Vehicles Symposium 2016},
	city = {Gothenburg, Sweden},
	pages = {1185-1191}
}