Probabilistic Lane Estimation using Basis Curves
Name
Teller_Probabilistic lane.pdf
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3.08 MB
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Adobe PDF
Checksum (MD5)
25567a694c2f2305e7862b77660e869b
Author(s)
Huang, Albert S.
Teller, Seth
Date Issued
June 2010
Journal
Robotics: Science and Systems (RSS). Proceedings
Publisher
Robotics: Science and Systems (RSS)
Citation
Huang, Albert S., Seth Teller. "Probabilistic Lane Estimation using Basis Curves" Robotics: Science and Systems (6th : 2010 : Zaragoza, Spain).
Version
Author's final manuscript
Abstract
Lane estimation for autonomous driving can be
formulated as a curve estimation problem, where local sensor
data provides partial and noisy observations of spatial curves.
The number of curves to estimate may be initially unknown and
many of the observations may be outliers or false detections
(due e.g. to to tree shadows or lens flare). The challenges lie in
detecting lanes when and where they exist, and updating lane
estimates as new observations are made.
This paper describes an efficient probabilistic lane estimation
algorithm based on a novel curve representation. The key
advance is a principled mechanism to describe many similar
curves as variations of a single basis curve. Locally observed road
paint and curb features are then fused to detect and estimate
all nearby travel lanes. The system handles roads with complex
geometries and makes no assumptions about the position and
orientation of the vehicle with respect to the roadway.
We evaluate our algorithm with a ground truth dataset
containing manually-labeled, fine-grained lane geometries for
vehicle travel in two large and diverse datasets that include more
than 300,000 images and 44km of roadway. The results illustrate
the capabilities of our algorithm for robust lane estimation in the
face of challenging conditions and unknown roadways.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
http://www.roboticsproceedings.org/rss06/p04.pdf
