发表机构
Hamm-Lippstadt University of Applied Sciences（哈姆-利普施塔特应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对自动驾驶中强化学习等方法的上下文推理缺陷，提出结合LLM常识推理的混合多智能体协同框架，经CARLA场景验证可保留结构化控制与安全机制，具备应用潜力。
AI 中文摘要
自动驾驶车辆需要强大的感知与决策能力，以在多样且未知场景中运行。尽管强化学习和基于规则的方法能提供有效的控制与安全机制，但在需要上下文推理的场景中性能可能下降。大语言模型（LLM）已展现出理解多模态信息、生成上下文推理的强大能力，不过将其直接用于车辆控制会引入延迟和幻觉风险。为解决这些局限，本文提出一种混合框架：该系统使用一个协调器来协调经PPO训练的强化学习与PID控制，LLM的常识推理被应用于整个框架，还会迭代利用LLM推理来优化动态驾驶环境下的强化学习奖励函数。该框架在高度随机的CARLA场景（涵盖多样环境与交通状况）中进行评估，结果表明，将基于LLM的推理与传统自动驾驶方法相结合，同时保留结构化控制与安全机制，具有应用潜力。
英文摘要
Autonomous vehicles require robust perception and decision-making capabilities to operate in diverse and unseen scenarios. While reinforcement learning and rule-based methods can provide effective control and safety mechanisms, their performance may degrade in situations requiring contextual reasoning. Large Language Models (LLMs) have demonstrated strong capabilities in understanding multimodal information and generating contextual reasoning, however, their use for direct vehicle control can introduce latency and hallucination risks. To address these limitations, a hybrid framework is proposed. This system uses an orchestrator to coordinate PPO-trained reinforcement learning and PID control, with LLM common-sense reasoning applied throughout the framework. LLM reasoning is further employed iteratively to refine the RL reward function for dynamic driving environments. The proposed framework is evaluated in highly randomized CARLA scenarios under diverse environmental and traffic conditions. The results demonstrate the potential of integrating LLM-based reasoning with conventional autonomous driving methods while retaining structured control and safety mechanism.
Comments16 pages, 7 figures
