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Vision-based robust road lane detection in urban environments

 
: Beyeler, Michael; Mirus, Florian; Verl, Alexander

:

Xi, Ning (General Chair) ; IEEE Robotics and Automation Society; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE ICRA 2014, International Conference on Robotics and Automation : Technologies Enabling New Economic Growth. May 31 - June 7, 2014, Hong Kong, China. Workshop & Tutorials. Proceedings
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-3684-7
S.4920-4925
International Conference on Robotics and Automation (ICRA) <2014, Hong Kong>
Englisch
Konferenzbeitrag
Fraunhofer IPA ()
Spurerkennung; Bilderkennung; Straße; Algorithmus

Abstract
Road and lane detection play an important role in autonomous driving and commercial driver-assistance systems. Vision-based road detection is an essential step towards autonomous driving, yet a challenging task due to illumination and complexity of the visual scenery. Urban scenes may present additional challenges such as intersections, multi-lane scenarios, or clutter due to heavy traffic. This paper presents an integrative approach to ego-lane detection that aims to be as simple as possible to enable real-time computation while being able to adapt to a variety of urban and rural traffic scenarios. The approach at hand combines and extends a road segmentation method in an illumination-invariant color image, lane markings detection using a ridge operator, and road geometry estimation using RANdom SAmple Consensus (RANSAC). Employing the segmented road region as a prior for lane markings extraction significantly improves the execution time and success rate of the RANSAC algorithm, and makes the detection of weakly pronounced ridge structures computationally tractable, thus enabling ego-lane detection even in the absence of lane markings. Segmentation performance is shown to increase when moving from a color-based to a histogram correlation-based model. The power and robustness of this algorithm has been demonstrated in a car simulation system as well as in the challenging KITTI data base of real-world urban traffic scenarios.

 

Die Veröffentlichung ist im Rahmen der Fraunhofer Systemforschung Elektromobilität (FSEM 2) bzw. dem Teilprojekt AfKar (autonomes Fahren und intelligentes Karosseriekonzept für ein All-Electric-Car) entstanden.

: http://publica.fraunhofer.de/dokumente/N-310489.html