A Fast and Robust Approach to Lane Marking Detection and Lane Tracking Christian Lipski, Björn Scholz, Kai Berger, Christian Linz, Timo Stich, Marcus Magnor

Christian Lipski, Björn Scholz, Kai Berger, Christian Linz, Timo Stich, and Marcus Magnor: "A Fast and Robust Approach to Lane Marking Detection and Lane Tracking", in Proc. IEEE Southwest Symposium on Image Analysis and Interpretation, Washington, DC, USA, vol. 2008, pp. 57–60, IEEE Computer Society, July 2008. Part of project "Computer Vision Algorithms for the DARPA Urban Challenge 2007". [pdf] [bib]

Abstract

We present a lane detection algorithm that robustly detects
and tracks various lane markings in real-time. The first
part is a feature detection algorithm that transforms several
input images into a top view perspective and analyzes local
histograms. For this part we make use of state-of-the-art
graphics hardware. The second part fits a very simple and
flexible lane model to these lane marking features. The algorithm
was thoroughly tested on an autonomous vehicle
that was one of the finalists in the 2007DARPAUrban Challenge.
In combination with other sensors, i.e. a lidar, radar
and vision based obstacle detection and surface classification,
the autonomous vehicle is able to drive in an urban
scenario at up to 15 mp/h.

TU Braunschweig - Fakultät für Mathematik und Informatik - Computer Graphics - Publications - A Fast and Robust Approach to Lane Marking Detection and Lane Tracking