Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle
In order to improve the safety, stability, and efficiency of lane change operating, this paper proposes a multivehicle-coordinated strategy under the vehicle network environment. The feasibility of collaborative lane change operation is established by establishing a gain function based on the incent...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8672928 |
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| _version_ | 1832547654724222976 |
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| author | Jie Ni Jingwen Han Fei Dong |
| author_facet | Jie Ni Jingwen Han Fei Dong |
| author_sort | Jie Ni |
| collection | DOAJ |
| description | In order to improve the safety, stability, and efficiency of lane change operating, this paper proposes a multivehicle-coordinated strategy under the vehicle network environment. The feasibility of collaborative lane change operation is established by establishing a gain function based on the incentive model. By comparing lane change gain with lane keeping gain, whether it is feasible to perform the collaboration under current conditions can be judged. Based on the model predictive control (MPC), a multiobjective optimization control function for cooperative lane change is established to realize the distributed control. A novel two-stage cooperative lane change framework is proposed, which divides the lane change process into the lane change phase and the longitudinal headway adjustment phase. It is significant to solve the difficult numerical problem caused by the dimension of collision-avoidance constraints and the nonlinearity of vehicle kinematics. In the first stage, the subject vehicle completes lane change operation. Both longitudinal and lateral movements of the vehicle are considered to optimize the acceleration and the error of following distance at this stage; in the second stage, the operation of adjusting longitudinal headway between vehicles in the target lane is completed, and at this period, only the longitudinal motion of the vehicle is considered to optimize the vehicle acceleration error. The rolling optimization time domain algorithm is used to solve the optimization control problem step by step. Finally, based on the US NGSIM open-source traffic flow database, the accuracy and feasibility of the proposed strategy are verified. |
| format | Article |
| id | doaj-art-98b6bb05c1e244a39c28ec32d77f34e8 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-98b6bb05c1e244a39c28ec32d77f34e82025-02-03T06:43:50ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/86729288672928Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected VehicleJie Ni0Jingwen Han1Fei Dong2School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaIn order to improve the safety, stability, and efficiency of lane change operating, this paper proposes a multivehicle-coordinated strategy under the vehicle network environment. The feasibility of collaborative lane change operation is established by establishing a gain function based on the incentive model. By comparing lane change gain with lane keeping gain, whether it is feasible to perform the collaboration under current conditions can be judged. Based on the model predictive control (MPC), a multiobjective optimization control function for cooperative lane change is established to realize the distributed control. A novel two-stage cooperative lane change framework is proposed, which divides the lane change process into the lane change phase and the longitudinal headway adjustment phase. It is significant to solve the difficult numerical problem caused by the dimension of collision-avoidance constraints and the nonlinearity of vehicle kinematics. In the first stage, the subject vehicle completes lane change operation. Both longitudinal and lateral movements of the vehicle are considered to optimize the acceleration and the error of following distance at this stage; in the second stage, the operation of adjusting longitudinal headway between vehicles in the target lane is completed, and at this period, only the longitudinal motion of the vehicle is considered to optimize the vehicle acceleration error. The rolling optimization time domain algorithm is used to solve the optimization control problem step by step. Finally, based on the US NGSIM open-source traffic flow database, the accuracy and feasibility of the proposed strategy are verified.http://dx.doi.org/10.1155/2020/8672928 |
| spellingShingle | Jie Ni Jingwen Han Fei Dong Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle Journal of Advanced Transportation |
| title | Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle |
| title_full | Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle |
| title_fullStr | Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle |
| title_full_unstemmed | Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle |
| title_short | Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle |
| title_sort | multivehicle cooperative lane change control strategy for intelligent connected vehicle |
| url | http://dx.doi.org/10.1155/2020/8672928 |
| work_keys_str_mv | AT jieni multivehiclecooperativelanechangecontrolstrategyforintelligentconnectedvehicle AT jingwenhan multivehiclecooperativelanechangecontrolstrategyforintelligentconnectedvehicle AT feidong multivehiclecooperativelanechangecontrolstrategyforintelligentconnectedvehicle |