An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles
Abstract Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to t...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Autonomous Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s43684-024-00087-5 |
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| author | Zhengqin Liu Jinlong Lei Peng Yi Yiguang Hong |
| author_facet | Zhengqin Liu Jinlong Lei Peng Yi Yiguang Hong |
| author_sort | Zhengqin Liu |
| collection | DOAJ |
| description | Abstract Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constraints in a CAV trajectory planning problem. |
| format | Article |
| id | doaj-art-83fe11ee965b4a93b4e8a6873f2bad9c |
| institution | Kabale University |
| issn | 2730-616X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Autonomous Intelligent Systems |
| spelling | doaj-art-83fe11ee965b4a93b4e8a6873f2bad9c2025-01-05T12:41:46ZengSpringerAutonomous Intelligent Systems2730-616X2025-01-015112010.1007/s43684-024-00087-5An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehiclesZhengqin Liu0Jinlong Lei1Peng Yi2Yiguang Hong3Department of Control Science and Engineering, Tongji UniversityDepartment of Control Science and Engineering, Tongji UniversityDepartment of Control Science and Engineering, Tongji UniversityDepartment of Control Science and Engineering, Tongji UniversityAbstract Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constraints in a CAV trajectory planning problem.https://doi.org/10.1007/s43684-024-00087-5Connected and autonomous vehiclesTrajectory planningGame theoryVariational equilibrium |
| spellingShingle | Zhengqin Liu Jinlong Lei Peng Yi Yiguang Hong An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles Autonomous Intelligent Systems Connected and autonomous vehicles Trajectory planning Game theory Variational equilibrium |
| title | An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles |
| title_full | An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles |
| title_fullStr | An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles |
| title_full_unstemmed | An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles |
| title_short | An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles |
| title_sort | interaction fair semi decentralized trajectory planner for connected and autonomous vehicles |
| topic | Connected and autonomous vehicles Trajectory planning Game theory Variational equilibrium |
| url | https://doi.org/10.1007/s43684-024-00087-5 |
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