基于模型预测控制与环境势场建模的车队协同驾驶方法研究
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- 英文论文题名：A Study on Collaborative Platoon Driving Method Based on APF method and MPC control
- 论文作者：黄子超 ; 吴青 ; 马育林
- 英文论文作者：Huang Zichao ; WU Qing ; Ma Yulin ; School of Logistics Engineering ; Wuhan University of Technology ; Research Institute of Highway of the Ministry of Transport ; Department of Military Vehicle ; Military Transportation University
- 年：2016
- 作者机构：武汉理工大学物流工程学院;交通运输部公路科学研究院;Department of Military Vehicle,Military Transportation University;
- 论文关键词：智能交通 ; 协同驾驶 ; 人工势场 ; 模型预测控制 ; 无人驾驶车辆
- 英文论文关键词：ITS ; Collaboration driving ; APF method ; MPC ; Automated vehicle
- 会议召开时间：2016-11-16
- 会议录名称：第十一届中国智能交通年会大会论文集
- 语种：中文
- 分类号：U463.6
- 学会代码：ZJTX
- 会议名称：第十一届中国智能交通年会
- 会议地点：中国重庆
- 主办单位：中国智能交通协会
- 学会名称：中国智能交通协会
- 页数：12
- 文件大小：3020k
- 原文格式：D
- 会议级别：全国
摘要
针对车队协同驾驶系统,设计了一种分布式模型预测控制器,分别对自由巡航策略以及组队巡航策略进行了控制器设计。其中为了能适应复杂的道路环境,提出了一种环境人工势场模型,包括环境车辆势场、道路势场、方向势场。其中,通过采取不同参数的方式反映环境车辆与被控车辆之间的协作关系是组队模式或自由模式,分别用于描述车队内和车队外的环境车辆对被控车辆产生的影响;道路势场为被控车辆建立车道线的约束使其沿车道行驶;目标速度势场使被控车辆保持既定速度行驶。利用该势场模型对车队换道过程中的车辆协作关系进行描述,设计了复杂环境下车队协同驾驶系统,并在Matlab环境下进行了仿真实验。结果表明提出的方法可实现车队在复杂道路条件下的自适应巡航控制。
Aiming at collaborating platoon driving system,this paper designed a distributed MPC controller which can unify the leader and the follower control into one controller.In order to meet the requirements of complex road environment,we propose a new artificial potential field model that includes vehicle environment potential field,road potential field,and target direction potential field.Different with the traditional potential field method,the proposed APF model take obstacle vehicles,road and the control demand into account which can effectively describe the mutual effect and collaboration between vehicle and the surrounding environment.Based on this model,a MPC cost function is presented.Such controller is designed from the perspective of multi-objective and multi-constraint optimization which can be solved to get the control trajectories.Meanwhile,the follower can utilized the predict trajectories to accomplished the collaborative driving.To validate the proposed approach,a variety of scenario simulations are conducted in MATLAB,and theperformance of the proposed method are verified
Aiming at collaborating platoon driving system,this paper designed a distributed MPC controller which can unify the leader and the follower control into one controller.In order to meet the requirements of complex road environment,we propose a new artificial potential field model that includes vehicle environment potential field,road potential field,and target direction potential field.Different with the traditional potential field method,the proposed APF model take obstacle vehicles,road and the control demand into account which can effectively describe the mutual effect and collaboration between vehicle and the surrounding environment.Based on this model,a MPC cost function is presented.Such controller is designed from the perspective of multi-objective and multi-constraint optimization which can be solved to get the control trajectories.Meanwhile,the follower can utilized the predict trajectories to accomplished the collaborative driving.To validate the proposed approach,a variety of scenario simulations are conducted in MATLAB,and theperformance of the proposed method are verified
引文
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[15]Xu G,Liu L,Ou Y,et al.Dynamic modeling of driver control strategy of lane-change behavior and trajectory planning for collision prediction[J].Intelligent Transportation Systems,IEEE Transactions on,2012,13(3):1138-1155.
[2]王楠,刘卫国,张君媛,等.基于视觉传感器的ADAS纵向行驶工况识别方法研究[J].交通信息与安全,2015,33(1):41-46.
[3]王莹,叶雷.ADAS和V2X技术市场走势[J].电子产品世界,2015,22(8):10-14.
[4]杨帆.无人驾驶汽车的发展现状和展望[J].上海汽车,2014(3):35-40.
[5]Varaiya P.Smart cars on smart roads:problems of control[J].Automatic Control,IEEE Transactions on,1993,38(2):195-207.
[6]Bergenhem C,Shladover S,Coelingh E,et al.Overview of platooning systems[C]//Proceedings of the 19th ITS World Congress,Oct 22-26,Vienna,Austria(2012).2012.
[7]HalléS,Chaib-draa B.A collaborative driving system based on multiagent modelling and simulations[J].Transportation Research Part C:Emerging Technologies,2005,13(4):320-345.
[8]Milanés V,Shladover S E,Spring J,et al.Cooperative adaptive cruise control in real traffic situations[J].Intelligent Transportation Systems,IEEE Transactions on,2014,15(1):296-305.
[9]Schmied R,Waschl H,Quirynen R,et al.Nonlinear MPC for Emission Efficient Cooperative Adaptive Cruise Control[J].IFAC-Papers On Line,2015,48(23):160-165.
[10]王灿,马钧.汽车CACC系统的车头时距策略研究[J].农业装备与车辆工程,2015,53(2):60-67.
[11]Kianfar R,Falcone P,Fredriksson J.A distributed model predictive control approach to active steering control of string stable cooperative vehicle platoon[C]//Advances in Automotive Control.2013,7(1):750-755.
[12]Xu G,Liu L,Ou Y,et al.Dynamic modeling of driver control strategy of lane-change behavior and trajectory planning for collision prediction[J].Intelligent Transportation Systems,IEEE Transactions on,2012,13(3):1138-1155.
[13]Hsu H C H,Liu A.Kinematic design for platoon-lane-change maneuvers[J].Intelligent Transportation Systems,IEEE Transactions on,2008,9(1):185-190.
[14]Kanaris A,Kosmatopoulos E B,Loannou P A.Strategies and spacing requirements for lane changing and merging in automated highway systems[J].Vehicular Technology,IEEE Transactions on,2001,50(6):1568-1581.
[15]Xu G,Liu L,Ou Y,et al.Dynamic modeling of driver control strategy of lane-change behavior and trajectory planning for collision prediction[J].Intelligent Transportation Systems,IEEE Transactions on,2012,13(3):1138-1155.