基于多前车信息融合的智能网联车辆跟驰模型

doi:10.11772/j.issn.1001-9081.2019050902

计算机应用 ›› 2019, Vol. 39 ›› Issue (12): 3685-3690.DOI: 10.11772/j.issn.1001-9081.2019050902

• 应用前沿、交叉与综合 • 上一篇下一篇

基于多前车信息融合的智能网联车辆跟驰模型

纪艺, 史昕, 赵祥模

长安大学信息工程学院, 西安 710064

收稿日期:2019-05-29 修回日期:2019-07-17 出版日期:2019-12-10 发布日期:2019-07-31
作者简介:纪艺(1995-),女,山东济南人,硕士研究生,主要研究方向:智能交通、车联网;史昕(1987-),男,河南南阳人,讲师,博士,CCF会员,主要研究方向:智能交通、车联网;赵祥模(1966-),男,重庆人,教授,博士生导师,博士,主要研究方向:智能交通、车联网、分布式网络测控。
基金资助:
国家重点研发计划项目（2017YFC0804806）；陕西省技术创新引导专项（2018HJCG-13）；中央高校基本科研业务费专项资金资助项目（300102248204）。

Car-following model for intelligent connected vehicles based on multiple headway information fusion

JI Yi, SHI Xin, ZHAO Xiangmo

School of Information Engineering, Chang'an University, Xi'an Shaanxi 710064, China

Received:2019-05-29 Revised:2019-07-17 Online:2019-12-10 Published:2019-07-31
Contact: 史昕
Supported by:
This work is partially supported by the National Key Research and Development Program of China (2017YFC0804806), the Technology Innovation Guide Project of Shaanxi Province (2018HJCG-13), the Fundamental Research Fund for the Central Universities (300102248204).

摘要/Abstract

摘要： 为了进一步提高交通流的稳定性，在经典基于驾驶员记忆的最优速度（OVCM）模型的基础上，提出了一种基于多前车最优速度与紧邻加速度（MHOVA）的智能网联车辆跟驰模型。首先，引入k辆前车的最优速度变化量与紧邻前车的加速度改进OVCM模型，并分别以参数γ和ω表示其权重；然后，结合改进模型利用线性稳定性分析获得交通流的临界稳定条件；最后，利用Matlab对车队施加扰动后的速度和车头距等参数进行数值模拟与分析。仿真结果表明：在车队启动和停止过程的仿真中，所提模型比OVCM模型使得车队整体达到稳定状态的时间更短；在环形道路上车队施加扰动的仿真中，所提模型相比于全速度差（FVD）模型、OVCM和多前车最优速度（MHOV）模型，在合理加速度敏感系数ω和前车数k约束下的速度和车头距波动幅度相对较小，尤其当ω为0.3且k为5时车辆速度的向上和向下波动率最小可达0.67%和0.47%，表明改进模型能较好地吸收交通扰动和增强车队行驶稳定性。

关键词: 交通流, 跟驰模型, 稳定性分析, 最优速度, 加速度

Abstract: In order to further enhance the stability of traffic flow, based on the classical Optimal Velocity Changes with Memory (OVCM) model, a novel car-following model for intelligent connected vehicles based on Multiple Headway Optimal Velocity and Acceleration (MHOVA) was proposed. Firstly, the optimal velocity change of k leading cars was introduced with the weight γ, as well as the acceleration of the nearest leading car was considered with the weight ω. Then, the critical stability conditions of traffic flow were obtained based on the proposed model and by the linear stability analysis. Finally, the numerical simulations and analyses were carried out on the parameters such as velocity and headway of the fleet with disturbance by Matlab. Simulation results show that, in the simulation of the starting and stopping processes of the fleet, the proposed model reduces the time to obtain the stable state of the fleet compared to OVCM does, in the simulation of a disturbance to the fleet on the annular road, if both ω and k are of rationality, the proposed model can perform the less fluctuations in terms of velocity and headway, compared with the Full Velocity Difference (FVD) model, OVCM and the Multiple Headway Optimal Velocity (MHOV) model. Especially when ω is 0.3 and k is 5, the minimum upward and downward fluctuations of vehicle velocity can be 0.67% and 0.47% respectively. Consequently, the proposed model can better absorb traffic disturbance and enhance the driving stability of fleet.

Key words: traffic flow, car-following model, stability analysis, optimal velocity, acceleration

中图分类号:

纪艺, 史昕, 赵祥模. 基于多前车信息融合的智能网联车辆跟驰模型[J]. 计算机应用, 2019, 39(12): 3685-3690.

JI Yi, SHI Xin, ZHAO Xiangmo. Car-following model for intelligent connected vehicles based on multiple headway information fusion[J]. Journal of Computer Applications, 2019, 39(12): 3685-3690.

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基于多前车信息融合的智能网联车辆跟驰模型

Car-following model for intelligent connected vehicles based on multiple headway information fusion

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