  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.
