The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks. The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge servers to support intelligent driving decision making and enhanced on-board experiences. However, such cross-reality interactions may cause serious location privacy risks, as adversaries can infer AV trajectories by correlating the location reported when AVs request LBS in reality with the location of the edge servers on which their corresponding AI agents are deployed in virtuality. To address this challenge, we design a cross-reality location privacy protection framework based on hybrid actions, including continuous location perturbation in reality and discrete privacy-aware AI agent migration in virtuality. In this framework, a new privacy metric, termed cross-reality location entropy, is proposed to effectively quantify the privacy levels of AVs. Based on this metric, we formulate an optimization problem to optimize the hybrid action, focusing on achieving a balance between location protection, service latency reduction, and quality of service maintenance. To solve the complex mixed-integer problem, we develop a novel LLM-enhanced Hybrid Diffusion Proximal Policy Optimization (LHDPPO) algorithm, which integrates LLM-driven informative reward design to enhance environment understanding with double Generative Diffusion Models-based policy exploration to handle high-dimensional action spaces, thereby enabling reliable determination of optimal hybrid actions. Extensive experiments on real-world datasets demonstrate that the proposed framework effectively mitigates cross-reality location privacy leakage for AVs while maintaining strong user immersion within 6G-enabled vehicular metaverse scenarios.

翻译：6G赋能的车辆元宇宙的出现使得自动驾驶车辆能够通过空天地海一体化网络在物理空间和虚拟空间中运行。自动驾驶车辆可以在边缘服务器上部署由大型AI模型驱动的AI代理作为个性化助手，以支持智能驾驶决策和增强的车载体验。然而，这种跨现实交互可能引发严重的位置隐私风险，因为攻击者可以通过关联自动驾驶车辆在现实中请求位置服务时上报的位置与其对应AI代理在虚拟空间中部署的边缘服务器位置，来推断自动驾驶车辆的轨迹。为应对这一挑战，我们设计了一种基于混合动作的跨现实位置隐私保护框架，包括现实中的连续位置扰动和虚拟空间中的离散隐私感知AI代理迁移。在该框架中，我们提出了一种新的隐私度量指标，称为跨现实位置熵，以有效量化自动驾驶车辆的隐私水平。基于此指标，我们构建了一个优化问题来优化混合动作，重点在于实现位置保护、服务延迟降低和服务质量维持之间的平衡。为解决这一复杂的混合整数问题，我们开发了一种新颖的LLM增强的混合扩散近端策略优化算法，该算法集成了LLM驱动的信息奖励设计以增强环境理解，并结合基于双重生成扩散模型的策略探索来处理高维动作空间，从而能够可靠地确定最优混合动作。在真实世界数据集上进行的大量实验表明，所提出的框架在6G赋能的车辆元宇宙场景中有效缓解了自动驾驶车辆的跨现实位置隐私泄露，同时保持了强大的用户沉浸感。