交通运输系统工程与信息 ›› 2018, Vol. 18 ›› Issue (3): 48-54.

• 智能交通系统与信息技术 • 上一篇    下一篇

考虑多前车反馈的智能网联车辆跟驰模型

秦严严 1a, 1b, 1c, 2,王昊*1a, 1b, 1c,冉斌 1c, 2   

  1. 1. 东南大学 a. 城市智能交通江苏省重点实验室,b. 现代城市交通技术江苏高校协同创新中心, c. 交通学院,南京 210096;2. 美国威斯康星大学麦迪逊分校 土木与环境工程系,麦迪逊 53706,美国
  • 收稿日期:2018-01-08 修回日期:2018-03-26 出版日期:2018-06-25 发布日期:2018-06-25
  • 作者简介:秦严严(1989-),男,江苏沛县人,博士生.
  • 基金资助:

    国家自然科学基金/ National Natural Science Foundation of China(51478113);东南大学优秀博士学位论文培育基金/ Scientific Research Foundation of the Graduate School of Southeast University(3221008737);中央高校基本科研业务费专项资金资助与江苏省研究生科研与实践创新计划项目/ Fundamental Research Funds for the Central Universities and Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX17_0146).

Car-following Model of Connected and Autonomous Vehicles Considering Multiple Feedbacks

QIN Yan-yan1a, 1b, 1c, 2, WANG Hao1a, 1b, 1c, RAN Bin1c, 2   

  1. 1a. Jiangsu Key Laboratory of Urban ITS, 1b. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies,1c. School of Transportation, Southeast University, Nanjing 210096, China; 2. Department of Civil and Environment Engineering, University of Wisconsin-Madison, Madison 53706, USA
  • Received:2018-01-08 Revised:2018-03-26 Online:2018-06-25 Published:2018-06-25

摘要:

基于智能网联车辆(Connected Autonomous Vehicle, CAV)跟驰特性,本文研究CAV跟驰模型.考虑多前车电子节气门角度反馈,构建CAV跟驰模型,并应用稳定性分析方法,推导所提模型稳定性判别条件.以考虑3辆前导车的CAV跟驰模型为例,设计数值仿真实验,分析不同CAV比例时混合交通流的安全性.模型稳定性分析表明:所提模型相比已有模型(CAV的T-FVD模型及常规车辆FVD模型)具备更优的稳定域,且考虑前车数量越多、多前车反馈权重系数越大,所提模型的稳定性越好;相同取值条件下,距离越远处的前车反馈权重系数对所提模型稳定性的影响越大.数值仿真表明,CAV有利于降低交通流的车辆尾部碰撞安全风险.

关键词: 交通工程, 跟驰模型, 稳定性分析, 智能网联车辆

Abstract:

Based on the car-following characteristic of connected autonomous vehicles (CAV), the CAV carfollowing model is studied. By considering electronic throttle angle feedbacks from multiple preceding vehicles, this paper proposes CAV’s car-following model. Then, stability analysis method is used to derive stability condition for the proposed model. Taking three-head vehicles into consideration, the numerical simulations are performed to study the safety of mixed traffic flow under different CAV rates. Stability analysis shows that the presented CAV model has better stability regions than the previous models (T-FVD model of CAV and FVD model of regular vehicles). In addition, the proposed CAV car-following model is more stable, with the increase of the number of preceding vehicles and the corresponding feedback weight coefficients. Moreover, the feedback weight coefficient of a preceding vehicle has greater effect on stability, if this vehicle is farther away. Besides, simulations indicate that CAV can reduce rear-end collision risks of traffic flow.

Key words: traffic engineering, car-following model, stability analysis, connected and autonomous vehicles

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