Digital Twins and Testbeds for Supporting AI Research with Autonomous Vehicle Networks

TL;DR

This paper explores the use of digital twins as virtual development environments for AI research in autonomous vehicle networks, comparing different testing platforms and demonstrating their effectiveness with real-world data.

Contribution

It provides a comprehensive comparison of simulation, digital twin, sandbox, and physical testbed environments for AI development in autonomous vehicle networks and showcases a practical example using the NSF AERPAW platform.

Findings

SITL digital twins, combined with real-world data, are effective for AI development in AVNs.

Digital twins can bridge the gap between simulation and physical testing.

The NSF AERPAW platform demonstrates successful AI solutions for UAV localization.

Abstract

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.