基于生成式人工智能的车辆混合现实元域自动驾驶仿真
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
摘要：
在车载混合现实（MR）Metaverse中，物理和虚拟实体之间的距离可以通过在自动驾驶系统中融合物理和虚拟环境与多维通信来克服。在数字孪生（DT）技术的辅助下，联网的自动驾驶汽车（AV）、路侧单元（RSU）和虚拟模拟器可以通过数字模拟来维护车载MR Metaverse，以共享数据并协同做出驾驶决策。然而，通过真实的数据汇集和融合物理世界来进行大规模交通和驾驶模拟，用于自动驾驶系统的在线预测和离线训练是困难和昂贵的。在本文中，我们提出了一种自动驾驶架构，其中生成式人工智能被用来合成模拟中的无限条件交通和驾驶数据，以提高驾驶安全性和交通效率。首先，我们提出了一个多任务DT卸载模型的可靠执行异构DT任务在RSU的不同要求。然后，基于AV的DT的偏好和收集的现实数据，虚拟模拟器可以合成无限的条件驾驶和交通数据集，以进一步提高鲁棒性。最后，我们提出了一个多任务增强拍卖为基础的机制，提供细粒度的激励RSU在提供自动驾驶资源。性能分析和实验结果表明，所提出的机制和架构是防策略和有效的，分别。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
