@inproceedings {pub2576,
	title = {Visio-spatial Road Boundary Detection for Unmarked Urban and Rural Roads},
	author = {Tobias K{\"u}hnl AND Jannik Fritsch},
	year = {2014},
	month = {June},
	abstract = {The robust detection of road boundaries is an
prerequisite for Advanced Driver Assistant Systems (ADAS),
such as Lane Departure Warning and Lane Keeping Assistant
Systems. State-of-the-art ADAS rely on lane markings to draw
inference about the extend of lanes or the road area. However,
on many rural or urban roads markings are worn out or simply
not existing. Therefore, this publication proposes a system for
vision-based road boundary detection without requiring road
markings at the outer side of the road. The fundamental
approach is a boundary vicinity classification based on SPatial
RAY (SPRAY) features which combines visual and spatial
context information. More specifically, the inner road boundary
vicinity (IBV), the outer road boundary vicinity (OBV), and the
remaining part of the road area (RA) are detected. Because
these classes occur in a defined sequence, i.e., a road boundary
exhibits a transition from RA to IBV to OBV, this approach
extracts the horizontal boundary transition pattern to make
inference about possible locations of road boundaries and their
direction (left or right road side). The implemented system was
evaluated on unmarked urban and rural roads. The results
show that the system effectively detects road boundaries such
as curbstones and soft shoulders under challenging conditions.},
	publisher = {IEEE},
	booktitle = {Intelligent Vehicles Symposium (IV)}
}