Adaptive Lane Departure Warning Strategy Considering Driver's Driving Style

被引：0
|
作者：
Zhu B. [1 ,2 ]
Li W. [1 ]
Zhao J. [1 ]
Han J. [1 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun
[2] Key Laboratory of Bionic Engineering of the Ministry of Education, Jilin University, Changchun
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2019年 / 47卷
关键词：
Driving style; Fuzzy clustering; Generalized regression neural network(GRNN); Lane departure warning;
D O I：
10.11908/j.issn.0253-374x.19708
中图分类号：
学科分类号：
摘要：
An adaptive lane departure warning strategy considering driver's driving style is proposed. Firstly, the driver's driving style data are collected from the real vehicle driving data acquisition platform, and the driving data are clustered based on the fuzzy clustering. Then, the driver's driving style identification strategy is established by using the generalized regression neural network model. Secondly, the estimation model of lane departure time is established, and the individualized lane departure is designed. Finally, the departure warning system is validated by a driving simulator. The results show that the proposed strategy could improve the applicability of lane departure warning based on the effective identification of driver's driving style. © 2019, Editorial Department of Journal of Tongji University. All right reserved.
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收藏
页码：171 / 177
页数：6
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