# A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving

arXiv: https://arxiv.org/abs/2606.28384 · HTML: https://mlsanity.com/paper/2606.28384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking](https://mlsanity.com/paper/2109.03805/) (2021)
- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept](https://mlsanity.com/paper/2402.12682/) (2024)
- [CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception](https://mlsanity.com/paper/2507.19239/) (2025)
- [YOLO-based Semantic Communication with Generative AI-aided Resource Allocation for Digital Twins Construction](https://mlsanity.com/paper/2306.14138/) (2023)
- [Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions](https://mlsanity.com/paper/2212.11819/) (2022)
- [Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments](https://mlsanity.com/paper/2305.03312/) (2023)
- [Digital Twin-Empowered Communications: A New Frontier of Wireless Networks](https://mlsanity.com/paper/2307.00973/) (2023)
- [Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks](https://mlsanity.com/paper/2502.05116/) (2025)
- [Active Digital Twins via Active Inference](https://mlsanity.com/paper/2506.14453/) (2025)
- [Robust Safety for Mixed-Autonomy Traffic with Delays and Disturbances](https://mlsanity.com/paper/2310.04007/) (2023)
- [Goal-oriented Semantic Communication for Robot Arm Reconstruction in Digital Twin: Feature and Temporal Selections](https://mlsanity.com/paper/2411.08835/) (2024)
- [Continual Reinforcement Learning for Digital Twin Synchronization Optimization](https://mlsanity.com/paper/2501.08045/) (2025)
- [Pull-Based Query Scheduling for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2503.06725/) (2025)
- [Digital Twin Assisted Proactive Management in Zero Touch Networks](https://mlsanity.com/paper/2508.17941/) (2025)
- [Goal-Oriented Access Optimization for ISAC-Enabled Digital Twins](https://mlsanity.com/paper/2603.01781/) (2026)
- [Toward Efficient Deployment and Synchronization in Digital Twins-Empowered Networks](https://mlsanity.com/paper/2604.00566/) (2026)

## Abstract (arXiv)

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%.