  Ramp merging is one of the bottlenecks in traffic systems, which commonly
cause traffic congestion, accidents, and severe carbon emissions. In order to
address this essential issue and enhance the safety and efficiency of connected
and autonomous vehicles (CAVs) at multi-lane merging zones, we propose a novel
collaborative decision-making framework, named AgentsCoMerge, to leverage large
language models (LLMs). Specifically, we first design a scene observation and
understanding module to allow an agent to capture the traffic environment. Then
we propose a hierarchical planning module to enable the agent to make decisions
and plan trajectories based on the observation and the agent's own state. In
addition, in order to facilitate collaboration among multiple agents, we
introduce a communication module to enable the surrounding agents to exchange
necessary information and coordinate their actions. Finally, we develop a
reinforcement reflection guided training paradigm to further enhance the
decision-making capability of the framework. Extensive experiments are
conducted to evaluate the performance of our proposed method, demonstrating its
superior efficiency and effectiveness for multi-agent collaborative
decision-making under various ramp merging scenarios.
