• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus 收录
  • 全国中文核心期刊
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

混行环境下CACC系统驾乘舒适性优化控制

梁军 于扬 王文飒 陈龙

梁军, 于扬, 王文飒, 陈龙. 混行环境下CACC系统驾乘舒适性优化控制[J]. 西南交通大学学报, 2021, 56(6): 1290-1297. doi: 10.3969/j.issn.0258-2724.20200514
引用本文: 梁军, 于扬, 王文飒, 陈龙. 混行环境下CACC系统驾乘舒适性优化控制[J]. 西南交通大学学报, 2021, 56(6): 1290-1297. doi: 10.3969/j.issn.0258-2724.20200514
LIANG Jun, YU Yang, WANG Wensa, CHEN Long. Optimal Control for Ride Comfort of Cooperative Adaptive Cruise Control System Under Mixed Traffic Flow[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1290-1297. doi: 10.3969/j.issn.0258-2724.20200514
Citation: LIANG Jun, YU Yang, WANG Wensa, CHEN Long. Optimal Control for Ride Comfort of Cooperative Adaptive Cruise Control System Under Mixed Traffic Flow[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1290-1297. doi: 10.3969/j.issn.0258-2724.20200514

混行环境下CACC系统驾乘舒适性优化控制

doi: 10.3969/j.issn.0258-2724.20200514
基金项目: 国家重点研发计划(2018YFB010500);江苏省高校自然科学研究重大项目(18KJA580002)
详细信息
    作者简介:

    梁军(1976—),男,教授,博士,博士生导师,研究方向为智能车辆与智能交通,E-mail:liangjun@ujs.edu.cn

  • 中图分类号: U461.4

Optimal Control for Ride Comfort of Cooperative Adaptive Cruise Control System Under Mixed Traffic Flow

  • 摘要:

    为提升协同式自适应巡航(cooperative adaptive cruise control,CACC)系统在由自动网联汽车(connected automated vehicle,CAV)和人工驾驶汽车(manual vehicle,MV)构成的混行交通流下的驾乘舒适性,提出考虑驾乘舒适性的双层控制策略(dual-layer control strategy considering ride comfort,RC-DCS). 上层控制器从宏观角度出发,采用两状态空间模型调整跟车间距及车速,并利用代价函数改善车队的整体稳定性和舒适性;下层控制器从微观角度出发,优化单车的油门和制动踏板切换逻辑,稳定实际加速度输出,降低车辆频繁加减速引起的自身俯仰. 试验结果表明:RC-DCS在跟随MV工况中跟车间距误差和加速度分别降低了72.44%和24.87%;在MV插入CACC车队工况中通过增大跟车时距0.4 s以减少加速度波动;在跟车、紧急制动、旁车切入3种典型工况中,单车加速度标准差分别降低了9.6%、10.4%、2.9%.

     

  • 图 1  CACC系统舒适度优化分层架构

    Figure 1.  Hierarchical architecture of CACC system

    图 2  CACC系统工作模式

    Figure 2.  Working mode of CACC system

    图 3  车队整体运行结果对比

    Figure 3.  Result comparison of fleet overall operation

    图 4  MV切入结果对比

    Figure 4.  Result comparison of MV cut-in

    图 5  典型工况下单车试验结果对比

    Figure 5.  Result comparison of single vehicle experiments under typical working conditions

    表  1  控制规则表

    Table  1.   Control rules

    evea
    NBNSZOPSPB
    NBNBNBNSNSZO
    NSNBNSNSZOPS
    ZONSNSZOPSPS
    PSNSZOPSPSPB
    PBZOPSPSPBPB
    下载: 导出CSV

    表  2  切换控制策略

    Table  2.   Switching control strategies

    当前
    状态
    输出方案
    减速区域
    ev < −0.1)
    保持区域
    (−0.1 ≤ ev ≤ 0.1)
    加速区域
    ev > 0.1)
    TA TC TC TC
    BA BC BC BC
    NA BC NO TC
    下载: 导出CSV
  • [1] BANSAL P, KOCKELMAN K M. Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies[J]. Transportation Research Part A:Policy and Practice, 2017, 95: 49-63. doi: 10.1016/j.tra.2016.10.013
    [2] KILINC A S, BAYBURA T. Determination of minimum horizontal curve radius used in the design of transportation structures, depending on the limit value of comfort criterion lateral jerk[R]. Rome: University of Saskatchewan, 2012.
    [3] FORSTBERG J. Ride comfort and motion sickness in tilting trains: Human responses to motion environments in train and simulator experiments[D]. Stockholm: Royal Institute of Technology, 2000.
    [4] POWELL J P, PALACÍN R. Passenger stability within moving railway vehicles:limits on maximum longitudinal acceleration[J]. Urban Rail Transit, 2015, 1(2): 95-103. doi: 10.1007/s40864-015-0012-y
    [5] GOLDING J F, MUELLER A G, GRESTY M A. A motion sickness maximum around the 0.2 Hz frequency range of horizontal translational oscillation[J]. Aviation,Space,and Environmental Medicine, 2001, 72(3): 188-192.
    [6] DONOHEW B E, GRIFFIN M J. Motion sickness:effect of the frequency of lateral oscillation[J]. Aviation,Space,and Environmental Medicine, 2004, 75(8): 649-656.
    [7] CHEUNG B, NAKASHIMA A. A review on the effects of frequency of oscillation on motion sickness[R]. Toronto: Defence Research and Development Toronto, 2006.
    [8] ELBANHAWI M, SIMIC M, JAZAR R. In the passenger seat:investigating ride comfort measures in autonomous cars[J]. IEEE Intelligent Transportation Systems Magazine, 2015, 7(3): 4-17. doi: 10.1109/MITS.2015.2405571
    [9] MILAKIS D, VAN AREM B, WEE B V. Policy and society related implications of automated driving:a review of literature and directions for future research[J]. Journal of Intelligent Transportation Systems, 2017, 21(4): 324-348. doi: 10.1080/15472450.2017.1291351
    [10] YANG C Y D, OZBAY K, BAN X. Developments in connected and automated vehicles[J]. Journal of Intelligent Transportation Systems, 2017, 21(1/2/3/4/5/6): 251-254.
    [11] HOOGENDOORN R G, VAN ARERM B, HOOGENDOOM S. Automated driving,traffic flow efficiency,and human factors:literature review[J]. Transportation Research Record, 2014, 2422(1): 113-120. doi: 10.3141/2422-13
    [12] TALEBPOUR A, MAHMASSANI H S. Influence of connected and autonomous vehicles on traffic flow stability and throughput[J]. Transportation Research Part C:Emerging Technologies, 2016, 71: 143-163. doi: 10.1016/j.trc.2016.07.007
    [13] SHLADOVER S E. Connected and automated vehicle systems:introduction and overview[J]. Journal of Intelligent Transportation Systems, 2018, 22(3): 190-200. doi: 10.1080/15472450.2017.1336053
    [14] JIA D Y, NGODUY D. Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication[J]. Transportation Research Part B:Methodological, 2016, 90: 172-191. doi: 10.1016/j.trb.2016.03.008
    [15] SUN J, ZHENG Z D, SUN J. Stability analysis methods and their applicability to car-following models in conventional and connected environments[J]. Transportation Research Part B:Methodological, 2018, 109: 212-237. doi: 10.1016/j.trb.2018.01.013
    [16] ZHOU Y, WANG M, AHN S. Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability[J]. Transportation Research Part B:Methodological, 2019, 128: 69-86. doi: 10.1016/j.trb.2019.07.001
    [17] MA Y L, LI Z X, MALEKIAN R, et al. Hierarchical fuzzy logic-based variable structure control for vehicles platooning[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(4): 1329-1340. doi: 10.1109/TITS.2018.2846198
    [18] 王平,高天赐,汪鑫,等. 基于拟合平纵断面的铁路特大桥梁线路平顺性评估[J]. 西南交通大学学报,2020,55(2): 231-237, 272. doi: 10.1007/s12239-018-0034-z

    WANG Ping, GAO Tianci, WANG Xin, et al. Smoothness estimation of super-large bridges in railway line based on fitting railway plane and profile[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 231-237, 272. doi: 10.1007/s12239-018-0034-z
    [19] SONG X L, WANG K, HE D F. Switching multi-objective receding horizon control for CACC of mixed vehicle strings[J]. IEEE Access, 2020, 8: 79684-79694. doi: 10.1109/ACCESS.2020.2990426
    [20] ZHU M, CHEN H Y, XIONG G M. A model predictive speed tracking control approach for autonomous ground vehicles[J]. Mechanical Systems and Signal Processing, 2017, 87: 138-152. doi: 10.1016/j.ymssp.2016.03.003
    [21] ZHOU Y J, ZHU H B, GUO M M, et al. Impact of CACC vehicles’ cooperative driving strategy on mixed four-lane highway traffic flow[J]. Physica A:Statistical Mechanics and Its Applications, 2020, 540: 122721.1-122721.13. doi: 10.1016/j.physa.2019.122721
    [22] HE Y L, ZHOU Q, MAKRIDIS M, et al. Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs[J]. IEEE Transactions on Transportation Electrification, 2020, 6(1): 346-355. doi: 10.1109/TTE.2020.2974588
    [23] NIE L Z, GUAN J Y, LU C H, et al. Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network[J]. IET Intelligent Transport Systems, 2018, 12(6): 485-494. doi: 10.1049/iet-its.2016.0293
  • 加载中
图(5) / 表(2)
计量
  • 文章访问数:  462
  • HTML全文浏览量:  290
  • PDF下载量:  19
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-08-06
  • 修回日期:  2020-12-01
  • 网络出版日期:  2021-04-15
  • 刊出日期:  2021-04-15

目录

    /

    返回文章
    返回