Problem
Research questions and friction points this paper is trying to address.
Digital Twins
Autonomous Driving
Communication Efficiency
Query-Driven
High-Fidelity Simulation
A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
🤖 AI Summary
This study addresses the excessive computational and communication overhead in conventional digital twin systems for autonomous driving, which arises from redundant data generated by continuous real-time synchronization of vehicle states. To mitigate this issue, the authors propose a query-driven digital twin architecture that enables the twin to proactively request only the environmental information necessary for simulation. A progressive cross-timestep querying mechanism is introduced to enhance communication efficiency, coupled with an optimization model that minimizes positional error under communication constraints. This approach significantly reduces resource consumption while preserving simulation fidelity. Experimental results demonstrate that, compared to traditional methods, the proposed framework achieves a 24% reduction in planning positional error and a 40% decrease in communication overhead.
📝 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%.
Innovation
Methods, ideas, or system contributions that make the work stand out.
query-driven
communication-efficient
digital twins
autonomous driving
progressive query mechanism
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