|Table of Contents|

Classification method of running environment features for unmanned vehicle(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2016年06期
Page:
140-148
Research Field:
交通信息工程及控制
Publishing date:
2016-12-20

Info

Title:
Classification method of running environment features for unmanned vehicle
Author(s):
KANG Jun-min;  ZHAO Xiang-mo;  XU Zhi-gang
School of Information Engineering, Chang’an University, Xi’an 710064, Shaanxi, China
Keywords:
information engineering;  feature classification;  machine learning;  unmanned vehicle;  simultaneous localization;  map creating
PACS:
U491.5
DOI:
-
Abstract:
In order to improve the barrier classification ability of mobile 2D LiDAR in urban environment, the creating accuracy of environmental map, and the safety and accuracy of autonomic behavior decision-making for unmanned vehicle, a classification method of environmental features based on machine learning was proposed. The data from 2D LiDAR were divided into independent data segments, and each data segment contains one environmental barrier. In 2D Gaussian probability density space of data segments, the elliptical axial lengths of contour lines, the log likelihood values and the maximum density were taken as the elements of sample data of artificial neural network, and the data segments were classified by the artificial neural network. The classification validity was estimated according to the weights of artificial neural network’s output data to retain the effective environmental features, and the features were extracted from the classified data. Computational result shows that in the same test scenario, when the judging condition of classification validity is relaxed, under which the classification stability interval is [0.55, 1], the classification transition interval is [0.45, 0.55), and the classification invalid interval is [0, 0.45), 98 environmental features are extracted, the maximum standard deviation of classified extraction results for the multiple observation data of one environmental feature is 30.7 mm, and the average standard deviation for all features is 5.1 mm; when the judging condition of classification validity is strict, under which the classification stability interval is [0.65, 1], the classification transition interval is [0.35, 0.65), and the classification invalid interval is [0, 0.35), 93 environmental features are extracted, the maximum standard deviation of classified extraction results for multiple observation data of one environmental feature is 22.0 mm, and the average standard deviation for all features is 4.2 mm. Therefore, the proposed classification method has higher noise tolerance ability and classification accuracy. 4 tabs, 19 figs, 25 refs.

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Last Update: 2016-12-20