NDLI: Lane boundary and curb estimation with lateral uncertainties

Content Provider	IEEE Xplore Digital Library
Author	Huang, A.S. Teller, S.
Copyright Year	2009
Description	Author affiliation: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA (Huang, A.S.; Teller, S.)
Abstract	This paper describes an algorithm for estimating lane boundaries and curbs from a moving vehicle using noisy observations and a probabilistic model of curvature. The primary contribution of this paper is a curve model we call lateral uncertainty, which describes the uncertainty of a curve estimate along the lateral direction at various points on the curve, and does not attempt to capture uncertainty along the longitudinal direction of the curve. Additionally, our method incorporates expected road curvature information derived from an empirical study of a real road network. Our method is notable in that it accurately captures the geometry of arbitrarily complex lane boundary curves that are not well approximated by straight lines or low-order polynomial curves. Our method operates independently of the direction of travel of the vehicle, and incorporates sensor uncertainty associated with individual observations. We analyze the benefits and drawbacks of the approach, and show results of our algorithm applied to real world data sets
Starting Page	1729
Ending Page	1734
File Size	435396
Page Count	6
File Format	PDF
ISBN	9781424438037
DOI	10.1109/IROS.2009.5354428
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Shape Geometry Remotely operated vehicles Computer vision Road vehicles Sensor systems Road safety Vehicle safety Mobile robots
Content Type	Text
Resource Type	Article

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