@inproceedings {PUBA234,
	title = {Image-based Lane Level Positioning using Spatial Ray Features},
	author = {Tobias K{\"u}hnl AND Franz Kummert AND Jannik Fritsch},
	year = {2013},
	month = {March},
	abstract = {This paper describes an approach for lane-level position estimation of a vehicle using only a single camera and no additional sensing equipment like, e.g., the typically employed IMU. The proposed method perceives a variety of local visual properties of the environment by means of base classifiers operating on patches extracted from monocular camera images, each represented in a metric confidence map obtained using inverse perspective mapping. The spatial layout of the scene is captured at selected metric points of these confidence maps using spatial ray (SPRAY) features and a boosting classifier is trained on these SPRAY features with ground truth information of the current lane-level position.
The high performance of the proposed approach on individual highway images shows the benefit of using spatial information for this task. By incorporating the total number of lanes from digital map data, a verification stage can be added to further increase the performance to more than 96\% correct lane assigments on typical highway scenes.},
	publisher = {IEEE},
	booktitle = {Intelligent Vehicles Symposium (IV)}
}