@inproceedings {pub2979,
	title = {Using Context Information and Probabilistic Classification for Making Extended Long-Term Trajectory Predictions},
	author = {Stefan Klingelschmitt AND Julian Eggert},
	year = {2015},
	month = {September},
	abstract = {Intersections are among the most accident prone spots in traffic. Future Advanced Driver Assistance Systems (ADAS) are aiming to assist the driving task in these complex scenarios. This can be realized by assessing the criticality of possible occurring situations. For such criticality assessment techniques predicting the trajectories of the involved traffic participants several seconds in advance are required. In this paper we outline a method that makes exhaustive use of context information to reliably predict maneuver-specific trajectories up to 5 seconds into the future. Since traffic scenes generally do not evolve deterministically, approximating future states in form of probability density functions will provide great benefit in terms of robustness and reliability. Since methods for approximating probability distributions are complex and in most cases computationally inefficient, we introduce an approach that is able to efficiently construct a probability distribution by reformulating the problem as a probabilistic multiclass classification problem. The presented approach is evaluated on a real-world data set containing 85 different intersections. We show that we can make reliable state estimations, even for a prediction horizon of up to 5 seconds.},
	publisher = {IEEE},
	booktitle = {18th IEEE International Conference on Intelligent Transportation Systems 2015 (ITSC)},
	city = {Las Palmas},
	pages = {705-711}
}