交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (4): 173-187.DOI: 10.16097/j.cnki.1009-6744.2026.04.015

• 智能交通系统与信息技术 • 上一篇    下一篇

考虑异质驾驶行为的智能网联车辆超车应对策略研究

王昊,陈旭梅*   

  1. 北京交通大学,综合交通运输大数据应用技术交通运输行业重点实验室,北京 100044
  • 收稿日期:2026-02-18 修回日期:2026-05-14 接受日期:2026-05-21 出版日期:2026-08-25 发布日期:2026-08-21
  • 作者简介:王昊(1999— ),男,河南信阳人,博士生
  • 基金资助:
    国家自然科学基金 (72271020)。

Connected and Automated Vehicles Overtaking Response Strategy Considering Heterogeneous Driving Behaviors

WANG Hao, CHEN Xumei*   

  1. Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
  • Received:2026-02-18 Revised:2026-05-14 Accepted:2026-05-21 Online:2026-08-25 Published:2026-08-21
  • Supported by:
    National Natural Science Foundation of China (72271020)

摘要: 城市信号路段处智能网联车辆(Connected and Automated Vehicle, CAV)规划的平滑轨迹往往局部车速较低,易引起后方人工驾驶车辆(Human-driving Vehicle, HV)的超车。为提升超车场景下混合交通流运行和能耗效益,本文提出一种应对后方HV超车的CAV轨迹规划方法。首先,按对CAV及其混合车队的跟随意愿将HV分为跟随型与常规型,设计一个信号路段混合交通仿真框架,采用有限状态机描述车辆交互行为;其次,建立混合整数二次规划模型用于规划 CAV 轨迹,以最小化 CAV 的行程时间和油耗并最大 化驾驶舒适度为目标;最后,设计轨迹预测与超车感知算法,并基于此提出改进被动让行策略(Improved Passive Yielding Strategy, IPYS)用于更新CAV的规划轨迹,减小HV超车产生的负面影响。仿真结果表明,IPYS策略在不同CAV占比和流量水平下效益提升整体优于其他对比策略;当CAV占比为20%~30%,各种策略之间的差异最显著;跟随型HV占比的提升有助于提高交通流效益。同时,敏感性分析显示,路段和周期长度会显著影响优化效果;当路段长度在 500~700 m 之间时,不同周期长度下 CAV 被超车频率的差异较大。

关键词: 智能交通, 超车应对, 轨迹规划, 智能网联车辆, 有限状态机

Abstract: At signalized urban roads, smooth trajectories planned for Connected and Automated Vehicles (CAVs) often involve relatively low local speeds, which can respond to overtaking by following human-driven vehicles (HVs). To enhance the operational efficiency and energy economy of mixed traffic flow in overtaking scenarios, this paper proposes a CAV trajectory planning method that responds to overtaking by rear HVs is proposed. First, HVs are categorized into general HVs and following HVs based on their willingness to follow CAVs and mixed platoons led by CAVs. A simulation framework is designed for mixed traffic on signalized road segments, which uses finite state machines to model vehicular interactions. A mixed-integer quadratic programming model is developed to plan CAV trajectories, aiming to minimize travel time and fuel consumption while maximizing driving comfort. A trajectory prediction and overtaking detection algorithm is developed and an improved passive yielding strategy (IPYS) is proposed to update CAV trajectories and alleviate the negative effects of HV overtaking. Simulation results demonstrate that the IPYS consistently outperforms benchmark strategies across various CAV penetration rates and traffic demand levels. The differences among strategies are most pronounced when CAV penetration ranges between 20% and 30%. An increase in the proportion of following HVs contributes to improved traffic efficiency. Sensitivity analysis further indicates that road segment length and signal cycle length significantly influence optimization outcomes. When the road segment length is between 500 and 700 meters, the frequency of CAVs being overtaken varies considerably with changes in cycle length.

Key words: intelligent transportation, overtaking response, trajectory planning, connected and automated vehicle(CAV), finite-state machine

中图分类号: