ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929733695963136 |
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| author | Lu, Liping He, Zhican Chu, Duanfeng Wang, Rukang Peng, Saiqian Zhou, Pan |
| author_facet | Lu, Liping He, Zhican Chu, Duanfeng Wang, Rukang Peng, Saiqian Zhou, Pan |
| contents | This paper proposes a novel method for multi-lane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. The code is available at: https://github.com/chuduanfeng/ConvoyLLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17529 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs Lu, Liping He, Zhican Chu, Duanfeng Wang, Rukang Peng, Saiqian Zhou, Pan Multiagent Systems Robotics This paper proposes a novel method for multi-lane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. The code is available at: https://github.com/chuduanfeng/ConvoyLLM. |
| title | ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs |
| topic | Multiagent Systems Robotics |
| url | https://arxiv.org/abs/2502.17529 |