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September 29, 2025Open Access

A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models

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Cascading cooperative multi-agent framework improves decision-making in multi-agent scenarios, indicating effective coordination.

The CCMA framework significantly enhances the performance of on-ramp merging control in complex driving environments.
Experiments revealed that CCMA improved micro and macro-level outcomes compared to traditional reinforcement learning methods.
The integration of a fine-tuned large language model facilitates better regional cooperation among agents.
Dynamic optimization through a retrieval-augmented generation mechanism addresses challenges in agent coordination.

Zhang et al. (2025) studied this question.

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September 29, 2025Open Access

View Full Paper

Ask AI

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Authors

Cascading cooperative multi-agent framework improves decision-making in multi-agent scenarios, indicating effective coordination.

The CCMA framework significantly enhances the performance of on-ramp merging control in complex driving environments.
Experiments revealed that CCMA improved micro and macro-level outcomes compared to traditional reinforcement learning methods.
The integration of a fine-tuned large language model facilitates better regional cooperation among agents.
Dynamic optimization through a retrieval-augmented generation mechanism addresses challenges in agent coordination.

Zhang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark