In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively.

翻译：在车辆混合现实（MR）元宇宙中，通过融合物理与虚拟环境并借助多维通信技术，可消除自主驾驶系统中物理实体与虚拟实体间的空间距离。依托数字孪生（DT）技术，互联自动驾驶车辆（AVs）、路侧单元（RSU）及虚拟仿真器能够通过数字化仿真维持车辆MR元宇宙的运行，实现数据共享与协同驾驶决策。然而，在自主驾驶系统中，通过采集物理世界的真实数据进行大规模交通与驾驶仿真（用于在线预测与离线训练）既困难又成本高昂。本文提出一种自主驾驶架构，利用生成式AI在仿真中合成无限制的条件化交通与驾驶数据，以提升驾驶安全性与交通效率。首先，针对路侧单元上具有不同需求的异构数字孪生任务，提出一种多任务DT卸载模型以确保可靠执行。其次，基于AV的数字孪生偏好与采集的真实数据，虚拟仿真器可合成无限制的条件化驾驶与交通数据集，进一步增强鲁棒性。最后，提出一种基于多任务增强拍卖的激励机制，为路侧单元提供细粒度的资源供给激励，以支持自主驾驶。性质分析与实验结果表明，所提机制与架构分别满足策略抵抗性与有效性。