  Large language models (LLMs) as autonomous agents offer a novel avenue for
tackling real-world challenges through a knowledge-driven manner. These
LLM-enhanced methodologies excel in generalization and interpretability.
However, the complexity of driving tasks often necessitates the collaboration
of multiple, heterogeneous agents, underscoring the need for such LLM-driven
agents to engage in cooperative knowledge sharing and cognitive synergy.
Despite the promise of LLMs, current applications predominantly center around
single agent scenarios. To broaden the horizons of knowledge-driven strategies
and bolster the generalization capabilities of autonomous agents, we propose
the KoMA framework consisting of multi-agent interaction, multi-step planning,
shared-memory, and ranking-based reflection modules to enhance multi-agents'
decision-making in complex driving scenarios. Based on the framework's
generated text descriptions of driving scenarios, the multi-agent interaction
module enables LLM agents to analyze and infer the intentions of surrounding
vehicles, akin to human cognition. The multi-step planning module enables LLM
agents to analyze and obtain final action decisions layer by layer to ensure
consistent goals for short-term action decisions. The shared memory module can
accumulate collective experience to make superior decisions, and the
ranking-based reflection module can evaluate and improve agent behavior with
the aim of enhancing driving safety and efficiency. The KoMA framework not only
enhances the robustness and adaptability of autonomous driving agents but also
significantly elevates their generalization capabilities across diverse
scenarios. Empirical results demonstrate the superiority of our approach over
traditional methods, particularly in its ability to handle complex,
unpredictable driving environments without extensive retraining.
