A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
This work addresses the problem of high communication and computation costs in digital twin-based autonomous driving by introducing a query-driven approach that improves efficiency and accuracy.
The paper proposes a query-driven digital twin architecture for autonomous driving that actively requests environment data from vehicles based on simulation needs, achieving a 24% reduction in planning position error and 40% lower communication overhead compared to traditional methods.
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%.