Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design. Our demonstration video can be found at https://youtu.be/3waQwlaHQkk.
An Overview of Digital Twins Application Domains in Smart Energy Grid
Digital Twins为智能电网提供了有前途的解决方案，这些解决方案涉及能源资产的优化运营、管理和控制，以及安全可靠的能源分配。由于在电网边缘大规模采用分布式可再生资源，这些挑战比以往任何时候都更加紧迫。数字双胞胎利用物联网、大数据分析、机器学习和云计算等技术，分析来自不同能源传感器的数据，查看和验证物理能源资产的状态，并提取有用的信息来预测和优化资产性能。在本文中，我们将提供一个概述数字孪生应用领域的智能电网，同时分析现有的最先进的文献。我们专注于以下应用领域：能源资产建模，故障和安全诊断，运营优化和商业模型。所发现的大多数相关文献方法都是在最近三年内发表的，表明数字孪生在智能电网中的应用领域是热门的，并正在逐步发展。无论如何，对于数字孪生系统的实施以及与能源管理流程的集成还没有统一的看法，因此，仍然需要做很多工作来理解和自动化智能电网管理。
Physics Driven Digital Twin Model for Evaluation of GNSS User Receiver Equipment
Department of Aerospace Engineering, Indian Institute of Technology Kanpur（印度理工学院坎普尔分校航空航天工程系）；ADASI, EDGE Group, Abu Dhabi Emirate, United Arab Emirates（阿联酋阿布扎比酋长国EDGE集团ADASI）；Indian Institute of Technology Indore（印度理工学院印多尔分校）；Sardar Vallabhbhai National Institute of Technology Surat（萨达尔·瓦拉巴伊国家技术学院苏拉特分校）
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
Department of Mechanical Engineering, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校机械工程系）；Computer Science & Information Systems Department, Bradley University（布雷纳德大学计算机科学与信息系统系）
