Road condition detection based on road temperature and solar radiation

被引：0

作者：

Lu J. ^{[1
,2
]}

Wang J. ^{[1
]}

Li K. ^{[1
]}

Lian X. ^{[1
]}

机构：

[1] State Key Laboratory of Automotive Safety and Energy, Tsinghua University

[2] Physics and Information Engineering Institute, Jianghan University

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | 2010年 / 41卷 / 05期

关键词：

BP neural network; Road condition detection; Road temperature; Solar radiation;

D O I：

10.3969/j.issn.1000-1298.2010.05.005

中图分类号：

学科分类号：

摘要：

Road temperature depends on road condition (dry, wet, snowy, icy) and solar radiation (mapped to season, geographical location, time, air temperature and air humidity), and there is the nonlinear causality among them, thus, road condition can be detected indirectly by road temperature and solar radiation with BP neural network. During the experiment to detect road condition (dry, wet), BP neural network was trained with 1344 group data and validated by 96 group data, the road condition detection accuracy reached 90%.

引用

页码：21 / 23+11

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