arXiv:2606.28384v1 Announce Type: cross 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 archit
The proliferation of digital twin applications, especially in high-data environments like autonomous driving, necessitates more efficient communication frameworks to overcome current computational and bandwidth limitations.
This development addresses a critical bottleneck in deploying high-fidelity digital twins for autonomous systems, impacting their safety, reliability, and economic viability by reducing operational costs and improving data management.
Digital twin designs for autonomous driving will evolve from raw real-time synchronization to more intelligent, query-driven data exchange, optimizing resource consumption and enabling broader adoption.
- · Autonomous vehicle manufacturers
- · Digital twin platform providers
- · Edge computing infrastructure
- · Telecommunication companies
- · Traditional cloud-centric digital twin architectures
- · Companies relying on high-bandwidth, low-efficiency data transfer
- · Early-stage digital twin solutions without intelligence layers
More efficient and scalable digital twin deployments for autonomous driving become feasible, enhancing development and testing capabilities.
Reduced operational costs for autonomous fleets due to optimized communication and computation, accelerating commercialization.
The methodology could be extended to other complex 'cyber-physical systems' (CPS) applications beyond autonomous driving, revolutionizing industrial IoT and smart city infrastructure.
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