广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (04): 26-33.doi: 10.12052/gdutxb.240014

• 控制科学与工程 • 上一篇    下一篇

面向混合编队领航CAVs的博弈换道决策模型

卢洁楚, 傅惠   

  1. 广东工业大学 机电工程学院, 广东 广州 510006
  • 收稿日期:2024-01-25 出版日期:2024-07-25 发布日期:2024-08-13
  • 通信作者: 傅惠(1981–) ，男，教授，博士，主要研究方向为生产与服务系统建模、控制及仿真，E-mail：traffic2008@126.com
  • 作者简介:卢洁楚(1998–) ，男，硕士研究生，主要研究方向为高速公路混合交通流控制，E-mail：895483778@qq.com
  • 基金资助:
    国家自然科学基金青年基金资助项目(62273102)

Game-based Lane-changing Decision Model for Leading CAVs in Mixed Platoons

Lu Jie-chu, Fu Hui   

  1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2024-01-25 Online:2024-07-25 Published:2024-08-13

HTML

PDF (PC)

36

摘要： 以CAVs为领航车的混合编队有助于实现更顺畅和安全的道路交通，但当前研究较少关注于具有多车道分布特征的混合编队形成问题。对此，本文提出了一种面向混合编队领航CAVs的博弈换道决策模型。该模型建立数学优化与博弈论相结合的换道决策机制，建立以最小化CAVs换道次数为目标的领航CAVs目标车道初始化流程，并依据CAVs博弈换道收益，更新领航CAVs目标车道，实现混合编队多车道分布。同时，基于博弈论建立CAVs与HDVs非合作博弈矩阵，综合考虑换道效率和安全性，分别设计时间和安全收益函数，量化CAVs换道风险，并使用交通软件SUMO建立微观仿真。实验结果表明，与基准模型相比，本文提出的博弈换道策略在不同混合交通量下混合编队完成率维持在97%以上，每组领航CAVs换道时间平均缩短约40%，每组领航CAV换道次数保持较低水平。

关键词: 混合编队, 领航CAVs, 博弈换道, 微观仿真

Abstract: The inclusion of connected and automated vehicles as leading vehicles in mixed platoons has the potential to achieve smoother and safer road traffic, but current research seldom focuses on the formation of mixed platoons with multi-lane distribution characteristics. To address this problem, a game-based lane-changing decision model is proposed for leading CAVs in mixed platoons. The model establishes a lane-changing decision mechanism that combines optimized and game-based approaches. It initiates a leading CAVs' target lane initialization process with the objective of minimizing the number of CAVs lane changes. Based on the payoff from CAVs' game-based lane-changing, it updates the target lanes of leading CAVs to achieve multi-lane distribution of mixed platoons. Furthermore, a non-cooperative game matrix between CAVs and HDVs is established based on game theory, considering both lane-changing efficiency and safety. Time and safety payoff functions are designed to quantify the lane-changing risk of CAVs. Microscopic simulations are conducted using the traffic software SUMO. Experimental results demonstrate that compared to the baseline model, the proposed game-based lane-changing strategy maintains a mixed formation completion rate of over 97% under different mixed traffic volumes, with an average reduction of about 40% in the lane-changing time for each group of leading CAVs, while keeping the lane-changing frequency of each group of leading CAVs at a low level.

Key words: mixed platoons, leading CAVs, game-based lane-changing, microscopic simulation

中图分类号: 

  • U495
[1] GONG S, DU L. Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles [J]. Transportation Research Part B: Methodological, 2018, 116: 25-61.
[2] MAHBUB A M I, LE V A, MALIKOPOULOS A A. A safety-prioritized receding horizon control framework for platoon formation in a mixed traffic environment [J]. Automatica, 2023, 155: 111115.
[3] ORKI O, AROGETI S. Control of mixed platoons consist of automated and manual vehicles[C]//2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) . Graz: IEEE, 2019: 1-6.
[4] KATO S, KUNIBE M, YAMAZAKI R, et al. Group formation for cooperative highway merging considering undetected vehicles in mixed traffic[C]//2021 IEEE Vehicular Networking Conference (VNC). Ulm: IEEE, 2021: 68-71.
[5] HUA Z, ZHAN J, ZHANG L. Decentralized tube model predictive control for arbitrarily mixed vehicle platoons with HDV uncertainties[C]//2022 41st Chinese Control Conference (CCC) . Hefei: IEEE, 2022: 5429-5434.
[6] MAHBUB A M I, MALIKOPOULOS A A. A platoon formation framework in a mixed traffic environment [J]. IEEE Control Systems Letters, 2022, 6: 1370-1375.
[7] 王树凤, 王世皓, 王新凯. 基于改进跟驰模型的混合车辆编队研究[J]. 公路, 2023, 68(6): 289-297.
WANG S F, WANG S H, WANG X K. Research on hybrid vehicle formation based on improved car-following [J]. Highway, 2023, 68(6): 289-297.
[8] YAO Z, MA Y, REN T, et al. Impact of the heterogeneity and platoon size of connected vehicles on the capacity of mixed traffic flow [J]. Applied Mathematical Modelling, 2024, 125: 367-389.
[9] 崔冰艳, 李贺, 崔哲, 等. 智能网联汽车换道决策安全性研究综述[J]. 交通信息与安全, 2023, 41(4): 1-13.
CUI B Y, LI H, CUI Z, et al. A review of safety studies on lane change decision-makings for connected automated vehicles [J]. Journal of Transport Information and Safety, 2023, 41(4): 1-13.
[10] 赵杭, 赵敏, 孙棣华, 等. 面向快速路交通瓶颈的混合交通群体节流控制策略[J]. 交通运输工程学报, 2022, 22(3): 162-173.
ZHAO H, ZHAO M, SUN D H, et al. Mixed traffic group throttling control strategy for traffic bottleneck of expressway [J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 162-173.
[11] 胡祥旺, 倪颖, 孙剑. 车联网环境下匝道汇入区瓶颈换道优化[J]. 同济大学学报(自然科学版) , 2023, 51(9): 1424-1432.
HU X W, NI Y, SUN J. Freeway merging area lane changing advisory optimization under connected vehicles environment [J]. Journal of Tongji University (Natural Science) , 2023, 51(9): 1424-1432.
[12] GONG B, XU Z, WEI R, et al. Reinforcement learning-based lane change decision for CAVs in mixed traffic flow under low visibility conditions [J]. Mathematics, 2023, 11(6): 1556.
[13] WANG Z, HUANG H, TANG J, et al. A deep reinforcement learning-based approach for autonomous lane-changing velocity control in mixed flow of vehicle group level [J]. Expert Systems with Applications, 2024, 238: 122158.
[14] JI A, LEVINSON D. A review of game theory models of lane changing [J]. Transportmetrica A: Transport Science, 2020, 16(3): 1628-1647.
[15] 陈华. 基于博弈论的自动驾驶车辆协同换道分析[EB/OL]. 武汉理工大学学报(交通科学与工程版) . (2023-07-04) [2024-03-09]. http://kns.cnki.net/kcms/detail/42.1824.U.20230704.1126.032.html.
CHEN H. Cooperative lane changing analysis of autonomous vehicles based on game theory [EB/OL]. Journal of Wuhan University of Technology (Transportation Science & Engineering) . (2023-07-04) [2024-03-09]. http://kns.cnki.net/kcms/detail/42.1824.U.20230704.1126.032.html.
[16] ZHENG Y, DING W, RAN B, et al. Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory-based lane change strategy [J]. IET Intelligent Transport Systems, 2020, 14(13): 1864-1870.
[17] QU D, ZHANG K, SONG H, et al. Analysis and modeling of lane-changing game strategy for autonomous driving vehicles [J]. IEEE Access, 2022, 10: 69531-69542.
[18] FU M, LI S, GUO M, et al. Cooperative decision-making of multiple autonomous vehicles in a connected mixed traffic environment: a coalition game-based model [J]. Transportation Research Part C: Emerging Technologies, 2023, 157: 104415.
[19] GUERIAU M, DUSPARIC I. Quantifying the impact of connected and autonomous vehicles on traffic efficiency and safety in mixed traffic[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . New York: IEEE, 2020: 1-8.
[20] SONG L, FAN W (David) , LIU P. Exploring the effects of connected and automated vehicles at fixed and actuated signalized intersections with different market penetration rates [J]. Transportation Planning and Technology, 2021, 44(6): 577-593.
[21] YAO Z, WANG Y, LIU B, et al. Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway [J]. Energy, 2021, 230: 120766.
[22] YE L, YAMAMOTO T. Modeling connected and autonomous vehicles in heterogeneous traffic flow [J]. Physica A: Statistical Mechanics and its Applications, 2018, 490: 269-277.
[23] DING J, PENG H, ZHANG Y, et al. Penetration effect of connected and automated vehicles on cooperative on-ramp merging [J]. IET Intelligent Transport Systems, 2020, 14(1): 56-64.
[1] 林思能； . 基于模糊逻辑的瓶颈路段车辆变道决策模拟[J]. 广东工业大学学报, 2002, 19(3): 69-73.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发