Digital twins (DTs), which are virtual environments that simulate, predict, and optimize the performance of their physical counterparts, are envisioned to be essential technologies for advancing next-generation wireless networks. While DTs have been studied extensively for wireless networks, their use in conjunction with autonomous vehicles with programmable mobility remains relatively under-explored. In this paper, we study DTs used as a development environment to design, deploy, and test artificial intelligence (AI) techniques that use real-time observations, e.g. radio key performance indicators, for vehicle trajectory and network optimization decisions in an autonomous vehicle networks (AVN). We first compare and contrast the use of simulation, digital twin (software in the loop (SITL)), sandbox (hardware-in-the-loop (HITL)), and physical testbed environments for their suitability in developing and testing AI algorithms for AVNs. We then review various representative use cases of DTs for AVN scenarios. Finally, we provide an example from the NSF AERPAW platform where a DT is used to develop and test AI-aided solutions for autonomous unmanned aerial vehicles for localizing a signal source based solely on link quality measurements. Our results in the physical testbed show that SITL DTs, when supplemented with data from real-world (RW) measurements and simulations, can serve as an ideal environment for developing and testing innovative AI solutions for AVNs.

翻译：数字孪生（DT）作为模拟、预测并优化其物理对应物性能的虚拟环境，被视为推进下一代无线网络的关键技术。尽管DT在无线网络领域已有广泛研究，但其与可编程移动性的自主车辆结合的应用仍相对未被充分探索。本文研究了将DT作为开发环境，用于设计、部署和测试基于实时观测（例如无线关键性能指标）的人工智能（AI）技术，以在自主车辆网络（AVN）中实现车辆轨迹与网络优化决策。我们首先对比分析了仿真、数字孪生（软件在环（SITL））、沙箱（硬件在环（HITL））及物理测试平台在开发和测试AVN AI算法中的适用性。随后综述了DT在AVN场景中的多种代表性用例。最后，我们以美国国家科学基金会AERPAW平台为例，展示了利用DT开发并测试基于AI的自主无人机方案，该方案仅依据链路质量测量实现信号源定位。在物理测试平台的实验结果表明，通过补充真实世界（RW）测量与仿真数据，SITL数字孪生可作为开发与测试AVN创新AI方案的理想环境。