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JRM Vol.28 No.6 pp. 870-877
doi: 10.20965/jrm.2016.p0870
(2016)

Paper:

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Fundamental Study on Road Detection Method Using Multi-Layered Distance Data with HOG and SVM

Keisuke Kazama, Yasuhiro Akagi, Pongsathorn Raksincharoensak, and Hiroshi Mouri

Mechanical Systems Engineering, Tokyo University of Agriculture and Technology
2-24-16 Naka-cho, Kognei-shi, Tokyo 184-8588, Japan

Received:
July 14, 2016
Accepted:
September 21, 2016
Published:
December 20, 2016
Creative Commons License
Keywords:
autonomous vehicle, machine learning, histograms, gradients, active safety
Abstract
This paper describes a road area detection method using a support vector machine (SVM) and histogram of oriented gradient (HOG) features. The boundary lines have many features, such as changes in height, color, and brightness, but these are sensitive to noise. In terms of robustness, it is difficult to match road boundary lines with the boundary lines on 2D maps. Localization methods using texture matching are accurate, but they have disadvantages related to adapting to changes in the environment. We therefore decided to make a classifier to differentiate road areas from other areas by detecting the road plane. First, we calculate the HOG features from range data acquired by 3D LiDAR. We then create the road area classifier by applying SVM. Finally, we evaluate the basic performance of the proposed method in simulation and in the real world.
Road detection method with HOG and SVM

Road detection method with HOG and SVM

Cite this article as:
K. Kazama, Y. Akagi, P. Raksincharoensak, and H. Mouri, “Fundamental Study on Road Detection Method Using Multi-Layered Distance Data with HOG and SVM,” J. Robot. Mechatron., Vol.28 No.6, pp. 870-877, 2016.
Data files:
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