按需调用车辆数据，让自动驾驶数字孪生更省通信、更准定位
A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
- 数字孪生主动向车辆要数据，只传需要的信息
- 定位误差降24%，通信量减少40%
- 适合追求高效低耗的自动驾驶系统研发
数字孪生（DT）在自动驾驶等场景中可实现无风险仿真，保障服务的确定性与高可靠性。传统依赖车辆实时状态同步的方法，易因冗余数据导致计算与通信开销过大。为此，本文提出一种查询驱动的数字孪生架构，使数字孪生根据仿真结果主动请求所需环境数据。同时构建优化问题，在保证数字孪生保真度与通信约束的前提下，最小化自动驾驶定位误差。进一步设计跨时间步的渐进式查询机制以提升通信效率。仿真结果显示，该方法相比传统方法定位误差降低24%，通信开销减少40%。
原文摘要 · Abstract (English)
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%.
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