Finding road intersections in advance is crucial for
navigation and path planning of moving autonomous
vehicles, especially when there is no position or
geographic auxiliary information available. In this
paper, we investigate the use of a 3D point cloud
based solution for intersection and road segment
classification in front of an autonomous vehicle. It
is based on the analysis of the features from the
designed beam model. First, we build a grid map of
the point cloud and clear the cells which belong to
other vehicles. Then, the proposed beam model is
applied with a specified distance in front of
autonomous vehicle. A feature set based on the
length distribution of the beam is extracted from
the current frame and combined with a trained
classifier to solve the road-type classification
problem, i.e., segment and intersection. In
addition, we also make the distinction between
+-shaped and T-shaped intersections. The results are
reported over a series of real-world data. A
performance of above 80\% correct classification is
reported at a real-time classification rate of 5
Hz.
%0 Conference Paper
%1 IV2012
%A Chen, Long
%A Li, Qingquan
%A Zhu, Quanwen
%A Li, Ming
%A Nüchter, A.
%B Proceedings of the 2012 IEEE Intelligent Vehicles
Symposium (IV '12)
%C Alcala de Henares, Madrid, Spain
%D 2012
%K imported
%P 456--461
%R 10.1109/IVS.2012.6232219
%T 3D LIDAR Point Cloud based Intersection Recognition
for Autonomous Driving
%U https://robotik.informatik.uni-wuerzburg.de/telematics/download/iv2012.pdf
%X Finding road intersections in advance is crucial for
navigation and path planning of moving autonomous
vehicles, especially when there is no position or
geographic auxiliary information available. In this
paper, we investigate the use of a 3D point cloud
based solution for intersection and road segment
classification in front of an autonomous vehicle. It
is based on the analysis of the features from the
designed beam model. First, we build a grid map of
the point cloud and clear the cells which belong to
other vehicles. Then, the proposed beam model is
applied with a specified distance in front of
autonomous vehicle. A feature set based on the
length distribution of the beam is extracted from
the current frame and combined with a trained
classifier to solve the road-type classification
problem, i.e., segment and intersection. In
addition, we also make the distinction between
+-shaped and T-shaped intersections. The results are
reported over a series of real-world data. A
performance of above 80\% correct classification is
reported at a real-time classification rate of 5
Hz.
@inproceedings{IV2012,
abstract = {Finding road intersections in advance is crucial for
navigation and path planning of moving autonomous
vehicles, especially when there is no position or
geographic auxiliary information available. In this
paper, we investigate the use of a 3D point cloud
based solution for intersection and road segment
classification in front of an autonomous vehicle. It
is based on the analysis of the features from the
designed beam model. First, we build a grid map of
the point cloud and clear the cells which belong to
other vehicles. Then, the proposed beam model is
applied with a specified distance in front of
autonomous vehicle. A feature set based on the
length distribution of the beam is extracted from
the current frame and combined with a trained
classifier to solve the road-type classification
problem, i.e., segment and intersection. In
addition, we also make the distinction between
+-shaped and T-shaped intersections. The results are
reported over a series of real-world data. A
performance of above 80\% correct classification is
reported at a real-time classification rate of 5
Hz.},
added-at = {2017-09-19T13:40:53.000+0200},
address = {Alcala de Henares, Madrid, Spain},
author = {Chen, Long and Li, Qingquan and Zhu, Quanwen and Li, Ming and N{\"u}chter, A.},
biburl = {https://www.bibsonomy.org/bibtex/241abb2cf6e872aac7bfa365594afa571/nuechter76},
booktitle = {Proceedings of the 2012 IEEE Intelligent Vehicles
Symposium (IV '12)},
doi = {10.1109/IVS.2012.6232219},
interhash = {0c0769970dfdcb673caa8f61a54b0740},
intrahash = {41abb2cf6e872aac7bfa365594afa571},
keywords = {imported},
month = {June},
pages = {456--461},
timestamp = {2017-09-29T16:01:21.000+0200},
title = {{3D LIDAR Point Cloud based Intersection Recognition
for Autonomous Driving}},
url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/iv2012.pdf},
year = 2012
}