A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
Digital twins offer risk-free simulation for autonomous driving but suffer from high computing and communication costs due to redundant data. This paper proposes a query-driven digital twin architecture where the twin actively requests needed environmental data from vehicles based on simulation results. A cross-time-step progressive query mechanism is also designed to improve communication efficiency. Simulations show a 24% reduction in planning position error and 40% lower communication overhead compared to traditional methods.
[2606.28384] A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
[Submitted on 22 Jun 2026]
Title:A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
View a PDF of the paper titled A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving, by Nuocheng Yang and Longyu Zhou and Sihua Wang and Changchuan Yin and Tony Q. S. Quek
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Abstract:Digital twins (DTs) have become a potential technology to perform risk-free simulation of physical entities for deterministic and high-reliability services in diverse scenarios such as autonomous driving and low-altitude economy. In the autonomous driving scenario, traditional DT methods that rely solely on vehicle's real-time state synchronization, however, might lead to unacceptable computing and communication consumption for construction of high-fidelity DT with redundant data. To address this issue, we first propose a query-driven DT architecture to enable the DT to actively request the desired environment data from vehicles based on its simulation result. Then, we formulate an optimization problem whose goal is to minimize autonomous driving position error while accounting for DT fidelity and communication constraints. We also design a cross-time-step progressive query mechanism to further improve communication efficiency. The simulation results show that our proposed method achieves a 24% reduction in planning position error compared to traditional methods, while reducing communication overhead by 40%.
Comments: 14 pages, 9 figures
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28384 [cs.RO]
(or arXiv:2606.28384v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28384
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Nuocheng Yang [view email] [v1] Mon, 22 Jun 2026 05:28:07 UTC (914 KB)
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