Digital twins (DTs), which are virtual environments that simulate, predict, and optimize the performance of their physical counterparts, hold great promise in revolutionizing next-generation wireless networks. While DTs have been extensively studied for wireless networks, their use in conjunction with autonomous vehicles featuring 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 utilize real-world (RW) observations, e.g. radio key performance indicators, for vehicle trajectory and network optimization decisions in 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 (PT) 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 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（DTs）作为模拟、预测和优化其物理对应体性能的虚拟环境，在革新下一代无线网络方面展现出巨大潜力。尽管数字孪生在无线网络领域已得到广泛研究，但其与具备可编程移动性的自动驾驶车辆结合应用仍相对不足。本文研究将数字孪生作为开发环境，用于设计、部署和测试利用现实世界观测数据（如无线关键性能指标）的人工智能技术，以实现自动驾驶车辆网络中的车辆轨迹与网络优化决策。我们首先比较仿真环境、数字孪生（软件在环）、沙箱环境（硬件在环）和物理测试平台在自动驾驶车辆网络人工智能算法开发与测试中的适用性差异。随后系统综述数字孪生在自动驾驶车辆网络场景中的各类典型应用案例。最后，通过美国国家科学基金会AERPAW平台的实例，展示如何利用数字孪生开发和测试基于纯链路质量测量的自主无人机信号源定位人工智能辅助方案。物理测试平台的实验结果表明，当软件在环数字孪生辅以现实世界测量数据与仿真数据时，可成为开发测试自动驾驶车辆网络创新人工智能解决方案的理想环境。