Summary of Koma: Knowledge-driven Multi-agent Framework For Autonomous Driving with Large Language Models, by Kemou Jiang et al.
KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models
by Kemou Jiang, Xuan Cai, Zhiyong Cui, Aoyong Li, Yilong Ren, Haiyang Yu, Hao Yang, Daocheng Fu, Licheng Wen, Pinlong Cai
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for large language models (LLMs) to engage in cooperative knowledge sharing and cognitive synergy as autonomous agents. The KoMA framework consists of four modules: multi-agent interaction, multi-step planning, shared-memory, and ranking-based reflection. These modules enable LLM agents to analyze and infer the intentions of surrounding vehicles, make superior decisions, and evaluate and improve agent behavior. This approach enhances the robustness and adaptability of autonomous driving agents and elevates their generalization capabilities across diverse scenarios. The paper demonstrates the superiority of this approach over traditional methods in handling complex, unpredictable driving environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to work together as smart cars. It uses big language models to help these cars understand each other’s thoughts and make better decisions. This helps them drive more safely and efficiently. The computer system, called KoMA, has four parts that help the cars communicate and learn from each other. This makes them better at handling unexpected situations on the road. |
Keywords
» Artificial intelligence » Generalization
