Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance
This paper presents a constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads with infrastructure assistance. Specifically, this paper systematically formulates trajectory optimization problems in a spatial domain and a curvilinear coordinate. As an alternative...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6184790 |
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| _version_ | 1832550879691014144 |
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| author | Ran Yi Yang Zhou Xin Wang Zhiyuan Liu Xiaotian Li Bin Ran |
| author_facet | Ran Yi Yang Zhou Xin Wang Zhiyuan Liu Xiaotian Li Bin Ran |
| author_sort | Ran Yi |
| collection | DOAJ |
| description | This paper presents a constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads with infrastructure assistance. Specifically, this paper systematically formulates trajectory optimization problems in a spatial domain and a curvilinear coordinate. As an alternative of temporal domain and Cartesian coordinate formulation, our formulation provides the constrained trajectory optimization flexibility to describe complex road geometries, traffic regulations, and road obstacles, which are usually spatially varying rather than temporal varying, with assistances vehicle to infrastructure (V2I) communication. Based on the formulation, we first conducted a mathematical proof on the controllability of our system, to show that our system can be controlled in the spatial domain and curvilinear coordinate. Further, a multiobjective model predictive control (MPC) approach is designed to optimize the trajectories in a rolling horizon fashion and satisfy the collision avoidances, traffic regulations, and vehicle kinematics constraints simultaneously. To verify the control efficiency of our method, multiscenario numerical simulations are conducted. Suggested by the results, our proposed method can provide smooth vehicular trajectories, avoid road obstacles, and simultaneously follow traffic regulations in different scenarios. Moreover, our method is robust to the spatial change of road geometries and other potential disturbances by the road curvature, work zone, and speed limit change. |
| format | Article |
| id | doaj-art-0132dd09e0a14729a7bb658b42802524 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0132dd09e0a14729a7bb658b428025242025-02-03T06:05:25ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6184790Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure AssistanceRan Yi0Yang Zhou1Xin Wang2Zhiyuan Liu3Xiaotian Li4Bin Ran5Department of Civil and Environmental EngineeringDepartment of Civil and Environmental EngineeringDepartment of Industrial and Systems EngineeringJiangsu Key Laboratory of Urban ITSDepartment of Civil and Environmental EngineeringDepartment of Civil and Environmental EngineeringThis paper presents a constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads with infrastructure assistance. Specifically, this paper systematically formulates trajectory optimization problems in a spatial domain and a curvilinear coordinate. As an alternative of temporal domain and Cartesian coordinate formulation, our formulation provides the constrained trajectory optimization flexibility to describe complex road geometries, traffic regulations, and road obstacles, which are usually spatially varying rather than temporal varying, with assistances vehicle to infrastructure (V2I) communication. Based on the formulation, we first conducted a mathematical proof on the controllability of our system, to show that our system can be controlled in the spatial domain and curvilinear coordinate. Further, a multiobjective model predictive control (MPC) approach is designed to optimize the trajectories in a rolling horizon fashion and satisfy the collision avoidances, traffic regulations, and vehicle kinematics constraints simultaneously. To verify the control efficiency of our method, multiscenario numerical simulations are conducted. Suggested by the results, our proposed method can provide smooth vehicular trajectories, avoid road obstacles, and simultaneously follow traffic regulations in different scenarios. Moreover, our method is robust to the spatial change of road geometries and other potential disturbances by the road curvature, work zone, and speed limit change.http://dx.doi.org/10.1155/2022/6184790 |
| spellingShingle | Ran Yi Yang Zhou Xin Wang Zhiyuan Liu Xiaotian Li Bin Ran Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance Journal of Advanced Transportation |
| title | Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance |
| title_full | Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance |
| title_fullStr | Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance |
| title_full_unstemmed | Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance |
| title_short | Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance |
| title_sort | spatially formulated connected automated vehicle trajectory optimization with infrastructure assistance |
| url | http://dx.doi.org/10.1155/2022/6184790 |
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