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

arXiv: https://arxiv.org/abs/2404.00954 · HTML: https://mlsanity.com/paper/2404.00954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [Diffusion Models for Smarter UAVs: Decision-Making and Modeling](https://mlsanity.com/paper/2501.05819/) (2025)
- [AI-Driven Low-Altitude Economy: Spectrum, Mobility, and Validation](https://mlsanity.com/paper/2506.01378/) (2025)
- [TWIST: Closed-Loop token Synchronization for Application-Aware Wireless Digital Twins](https://mlsanity.com/paper/2605.27205/) (2026)

## Abstract (arXiv)

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.