Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps
Existing studies on platoon trajectory optimization of connected and automated vehicles face challenges in balancing computational efficiency, privacy, and safety. This study proposes a distributed optimization method that decomposes the platoon trajectory planning problem into independent individua...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/6/483 |
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| author | Meiqi Liu Ying Gao Yikai Zeng Ruochen Hao |
| author_facet | Meiqi Liu Ying Gao Yikai Zeng Ruochen Hao |
| author_sort | Meiqi Liu |
| collection | DOAJ |
| description | Existing studies on platoon trajectory optimization of connected and automated vehicles face challenges in balancing computational efficiency, privacy, and safety. This study proposes a distributed optimization method that decomposes the platoon trajectory planning problem into independent individual vehicle tasks while ensuring safe inter-vehicle following gaps and maximizing travel efficiencyand ride comfort. The individual vehicle problems independently optimize their trajectory to improve computational efficiency, and only exchange dual variables related to safe following gaps to preserve privacy. Simulation experiments were conducted under single-platoon scenarios with different simulation horizons, as well as multi-platoon and platoon-merging scenarios, to analyze the control performance of the distributed method in contrast to the centralized method. Simulation results demonstrate that the mean computation time is reduced by 50% and the fuel consumption is decreased by 4% compared to the centralized method while effectively maintaining the safe inter-vehicle following gaps. The distributed method shows its scalability and adaptability for large-scale problems. |
| format | Article |
| id | doaj-art-ffa9d774bd8b4ae1a27d1cbc149d3e1f |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-ffa9d774bd8b4ae1a27d1cbc149d3e1f2025-08-20T03:26:52ZengMDPI AGSystems2079-89542025-06-0113648310.3390/systems13060483Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following GapsMeiqi Liu0Ying Gao1Yikai Zeng2Ruochen Hao3School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaChair of Traffic Process Automation, “Friedrich List” Faculty of Transport and Traffic Sciences, TU Dresden, 01069 Dresden, GermanyThe Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200092, ChinaExisting studies on platoon trajectory optimization of connected and automated vehicles face challenges in balancing computational efficiency, privacy, and safety. This study proposes a distributed optimization method that decomposes the platoon trajectory planning problem into independent individual vehicle tasks while ensuring safe inter-vehicle following gaps and maximizing travel efficiencyand ride comfort. The individual vehicle problems independently optimize their trajectory to improve computational efficiency, and only exchange dual variables related to safe following gaps to preserve privacy. Simulation experiments were conducted under single-platoon scenarios with different simulation horizons, as well as multi-platoon and platoon-merging scenarios, to analyze the control performance of the distributed method in contrast to the centralized method. Simulation results demonstrate that the mean computation time is reduced by 50% and the fuel consumption is decreased by 4% compared to the centralized method while effectively maintaining the safe inter-vehicle following gaps. The distributed method shows its scalability and adaptability for large-scale problems.https://www.mdpi.com/2079-8954/13/6/483connected and automated vehiclestrajectory planningdistributed optimizationintelligent transportation systems |
| spellingShingle | Meiqi Liu Ying Gao Yikai Zeng Ruochen Hao Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps Systems connected and automated vehicles trajectory planning distributed optimization intelligent transportation systems |
| title | Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps |
| title_full | Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps |
| title_fullStr | Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps |
| title_full_unstemmed | Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps |
| title_short | Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps |
| title_sort | distributed trajectory optimization for connected and automated vehicle platoons considering safe inter vehicle following gaps |
| topic | connected and automated vehicles trajectory planning distributed optimization intelligent transportation systems |
| url | https://www.mdpi.com/2079-8954/13/6/483 |
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