标题： 具身人工智能增强的车联网：一种集成大语言模型和强化学习的方法 显示英文标题

标题： Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method

摘要： 本文研究了在具身AI增强的车联网中自适应传输策略，通过整合大型语言模型（LLMs）进行语义信息提取和深度强化学习（DRL）进行决策。所提出的框架旨在通过制定一个结合韦伯-费希纳定律的优化问题，优化数据传输效率和决策准确性，作为平衡带宽利用率和用户体验（QoE）的度量标准。具体而言，我们使用大型语言和视觉助手（LLAVA）模型从具身AI代理（即车辆）捕获的原始图像数据中提取关键语义信息，将传输数据大小减少超过90%，同时保留用于车联网和决策的关键内容。在动态的车联网环境中，我们采用基于广义优势估计的近端策略优化（GAE-PPO）方法，在不确定性下稳定决策。仿真结果表明，LLAVA的注意力图突出了模型对相关图像区域的关注，提高了语义表示的准确性。此外，我们的传输策略相比DDPG提升了QoE高达36%，相比纯PPO减少了所需步骤高达47%。进一步分析表明，适应语义符号长度在传输质量和带宽之间提供了有效的权衡，在从4辆车辆扩展到8辆车辆时，QoE最高提升了61.4%。 显示英文摘要

摘要： This paper investigates adaptive transmission strategies in embodied AI-enhanced vehicular networks by integrating large language models (LLMs) for semantic information extraction and deep reinforcement learning (DRL) for decision-making. The proposed framework aims to optimize both data transmission efficiency and decision accuracy by formulating an optimization problem that incorporates the Weber-Fechner law, serving as a metric for balancing bandwidth utilization and quality of experience (QoE). Specifically, we employ the large language and vision assistant (LLAVA) model to extract critical semantic information from raw image data captured by embodied AI agents (i.e., vehicles), reducing transmission data size by approximately more than 90\% while retaining essential content for vehicular communication and decision-making. In the dynamic vehicular environment, we employ a generalized advantage estimation-based proximal policy optimization (GAE-PPO) method to stabilize decision-making under uncertainty. Simulation results show that attention maps from LLAVA highlight the model's focus on relevant image regions, enhancing semantic representation accuracy. Additionally, our proposed transmission strategy improves QoE by up to 36\% compared to DDPG and accelerates convergence by reducing required steps by up to 47\% compared to pure PPO. Further analysis indicates that adapting semantic symbol length provides an effective trade-off between transmission quality and bandwidth, achieving up to a 61.4\% improvement in QoE when scaling from 4 to 8 vehicles.