Digital Twins for Supporting AI Research with Autonomous Vehicle Networks
arxiv(2024)
摘要
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.
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