发明授权
US5586028A Road surface condition-detecting system and anti-lock brake system
employing same
失效
路面状态检测系统及采用该防抱死制动系统的防抱死系统
- 专利标题: Road surface condition-detecting system and anti-lock brake system employing same
- 专利标题(中): 路面状态检测系统及采用该防抱死制动系统的防抱死系统
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申请号: US353979申请日: 1994-12-06
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公开(公告)号: US5586028A公开(公告)日: 1996-12-17
- 发明人: Hiroshi Sekine , Shintaro Yokoyama , Ichiro Harada , Nobuyoshi Asanuma , Yorihisa Yamamoto , Yutaka Horiuchi , Shohei Matsuda , Makoto Otabe , Atsushi Itakagi , Hideki Kubonoya
- 申请人: Hiroshi Sekine , Shintaro Yokoyama , Ichiro Harada , Nobuyoshi Asanuma , Yorihisa Yamamoto , Yutaka Horiuchi , Shohei Matsuda , Makoto Otabe , Atsushi Itakagi , Hideki Kubonoya
- 申请人地址: JPX Tokyo
- 专利权人: Honda Giken Kogyo Kabushiki Kaisha
- 当前专利权人: Honda Giken Kogyo Kabushiki Kaisha
- 当前专利权人地址: JPX Tokyo
- 优先权: JPX5-340052 19931207; JPX6-293994 19941102; JPX6-295893 19941104; JPX6-295894 19941104; JPX6-297989 19941107; JPX6-298991 19941108
- 主分类号: B60T8/172
- IPC分类号: B60T8/172 ; B60T8/174 ; B60T8/1763 ; G01N19/02 ; B60T8/58
摘要:
A road surface condition-detecting system for a vehicle detects a road surface condition from road noise generated by a vehicle wheel. The road surface condition is determined based on parameter data of frequency components of the road noise, by a neural network. The road noise may be corrected by eliminating therefrom a disturbance, such as audio output and exhaust noise. A present state of the road surface condition may be determined based on at least two consecutive determinations made based on the road noise detected at regular time intervals. Exclusive neural networks may be used for respective road surface condition types. One of a plurality of neural networks provided for respective vehicle speed ranges may be selected according to an actual vehicle speed. Detected sound pressure levels of the road noise extracted by frequency analysis may be normalized within respective ranges defined by upper and lower limits set corresponding to predetermined frequency ranges before being supplied to the neural network.
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