Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse

• URL: http://arxiv.org/abs/2301.07636v2
• Date: Thu, 19 Jan 2023 04:15:41 GMT
• Title: Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse
• Authors: Minrui Xu, Dusit Niyato, Hongliang Zhang, Jiawen Kang, Zehui Xiong, Shiwen Mao, and Zhu Han
• Abstract summary: We propose a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In virtual-to-physical synchronization, MARs customize diverse and personal AR recommendations via generative AI models based on user preferences.
• Score: 129.8037449161817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Metaverse seamlessly blends the physical world and virtual space via ubiquitous communication and computing infrastructure. In transportation systems, the vehicular Metaverse can provide a fully-immersive and hyperreal traveling experience (e.g., via augmented reality head-up displays, AR-HUDs) to drivers and users in autonomous vehicles (AVs) via roadside units (RSUs). However, provisioning real-time and immersive services necessitates effective physical-virtual synchronization between physical and virtual entities, i.e., AVs and Metaverse AR recommenders (MARs). In this paper, we propose a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In physical-to-virtual synchronization, digital twin (DT) tasks generated by AVs are offloaded for execution in RSU with future route generation. In virtual-to-physical synchronization, MARs customize diverse and personal AR recommendations via generative AI models based on user preferences. Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and MARs for RSUs to provision real-time and effective services. Finally, property analysis and experimental results demonstrate that the proposed mechanism is strategy-proof and adverse-selection free while increasing social surplus by 50%.

Related papers

• Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks [55.15079732226397]
  Embodied AI is a rapidly advancing field that bridges the gap between cyberspace and physical space. In VEANET, embodied AI twins act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving.
  arXiv  Detail & Related papers  (2024-10-02T02:20:42Z)
• Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses [58.264499654343226]
  Vehicle Twins (VTs) are digital twins that provide immersive virtual services for Vehicular Metaverse Users (VMUs) High mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations. We propose a secure and reliable VT migration framework in vehicular metaverses.
  arXiv  Detail & Related papers  (2024-07-05T11:11:33Z)
• Haptic Repurposing with GenAI [5.424247121310253]
  Mixed Reality aims to merge the digital and physical worlds to create immersive human-computer interactions. This paper introduces Haptic Repurposing with GenAI, an innovative approach to enhance MR interactions by transforming any physical objects into adaptive haptic interfaces for AI-generated virtual assets.
  arXiv  Detail & Related papers  (2024-06-11T13:06:28Z)
• Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach [85.65587846913793]
  Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles. We propose an attribute-aware auction-based mechanism to optimize resource allocation during VTs migration. We train a DDA auctioneer using a generative pre-trained transformer (GPT)-based deep reinforcement learning (DRL) algorithm to adjust the auction clocks efficiently.
  arXiv  Detail & Related papers  (2024-06-08T09:41:38Z)
• Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach [57.15309977293297]
  The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses. We propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
  arXiv  Detail & Related papers  (2024-01-18T02:14:13Z)
• Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning [8.513938423514636]
  Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted driving, physical environment scenes are first captured by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central Metaverse Map Service Provider (MMSP) will aggregate information from all IoVs to develop a central Metaverse Map. Information from the Metaverse Map can then be downloaded into individual IoVs on demand and be delivered as AR scenes to the driver.
  arXiv  Detail & Related papers  (2023-03-18T00:03:50Z)
• Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses [130.15554653948897]
  In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome. Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly. We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
  arXiv  Detail & Related papers  (2023-02-16T16:54:10Z)
• User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks [8.513938423514636]
  In this paper, we consider a system consisting of a Metaverse server and multiple VR users. In our multi-user VR scenario for the Metaverse, users have different characteristics and demands for Frames Per Second (FPS) Our proposed user-centric DRL algorithm is called User-centric Critic with Heterogenous Actors (UCHA)
  arXiv  Detail & Related papers  (2023-02-03T00:12:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.