基于行驶状态估计的车车协同纵向安全距离模型

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摘要 The key of improving performance requirements for longitudinal collision avoidance system under collaborative vehicle is the accurate judgment and the estimation of vehicle driving state and appropriate safety distance model. In order to improve performance requirements of longitudinal collision avoidance system under collaborative vehicle, the longitudinal safety distance model based on vehicle driving state estimation is proposed. First through the establishment of the vehicle kinematics mode which considers the vehicle front and rear axle centre position and the state equation of vehicle running state parameter estimation, using the extended kalman filter to realize the accurate estimation of vehicle driving state parameters; then based on the accurate estimation of vehicle driving state parameters, according to the front vehicle' different state, the longitudinal safety distance models are built when the front vehicle is stationary, in uniform motion and braking. The simulation platforms of vehicle state estimation and longitudinal safety distance model are built based on Matlab/Simulink, the simulation results show that the accuracy of vehicle driving state parameters estimation and the effectiveness of longitudinal collision avoidance safety distance model.

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	唐自强
	龚贤武
	潘勇
	唐超
	王玮琳

关键词 ： automobile engineering, longitudinal collision avoidance, state estimation, safety distance model, extended kalman filter, cooperative vehicles

Abstract：The key of improving performance requirements for longitudinal collision avoidance system under collaborative vehicle is the accurate judgment and the estimation of vehicle driving state and appropriate safety distance model. In order to improve performance requirements of longitudinal collision avoidance system under collaborative vehicle, the longitudinal safety distance model based on vehicle driving state estimation is proposed. First through the establishment of the vehicle kinematics mode which considers the vehicle front and rear axle centre position and the state equation of vehicle running state parameter estimation, using the extended kalman filter to realize the accurate estimation of vehicle driving state parameters; then based on the accurate estimation of vehicle driving state parameters, according to the front vehicle' different state, the longitudinal safety distance models are built when the front vehicle is stationary, in uniform motion and braking. The simulation platforms of vehicle state estimation and longitudinal safety distance model are built based on Matlab/Simulink, the simulation results show that the accuracy of vehicle driving state parameters estimation and the effectiveness of longitudinal collision avoidance safety distance model.

Key words： automobile engineering longitudinal collision avoidance state estimation safety distance model extended kalman filter cooperative vehicles

收稿日期: 2016-09-26

基金资助:Supported by the Natural Science Basic Research Project in Shaanxi Province (No.2014JQ7269); the National Natural Science Foundation of Youth Project (No.51507013); the Special Fund Project of Central University Basic Scientific Research Business Expenses (No.2014g1321040, No.310822153201, No.310822151025) and the National College Students' Innovative Entrepreneurial Training Program (No.201510710036)

通讯作者: TANG Zi-qiang E-mail: tangzqa@126.com

引用本文:

唐自强, 龚贤武, 潘勇, 唐超, 王玮琳. 基于行驶状态估计的车车协同纵向安全距离模型[J]. Journal of Highway and Transportation Research and Development, 2017, 11(2): 103-110.
TANG Zi-qiang, GONG Xian-wu, PAN Yong, TANG Chao, WANG Wei-lin. Collaborative Vehicle Longitudinal Safety Distance Model Based on Driving State Estimation. Journal of Highway and Transportation Research and Development, 2017, 11(2): 103-110.

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http://manu27.magtech.com.cn/Jwk_gljtkj_en/CN/ 或 http://manu27.magtech.com.cn/Jwk_gljtkj_en/CN/Y2017/V11/I2/103

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