Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation

• URL: http://arxiv.org/abs/2605.19490v1
• Date: Tue, 19 May 2026 07:44:59 GMT
• Title: Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation
• Abstract summary: This paper proposes a novel real-time hybrid digital twin platform for connected and automated vehicles (CAVs) validation.<n>Its core innovation lies in the tight coupling of a high-fidelity CARLA-SUMO co-simulation with a physical test site and vehicle via a low-latency Vehicle-to-Everything (V2X) communication link.<n> Detailed implementation includes using photogrammetry for full-scale asset reconstruction and a cloud-edge collaborative architecture for scalable, multi-user operation.
• Score: 8.373850248021927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based testing offers scalability, existing approaches often lack seamless integration with real vehicles and field data, limiting their fidelity in capturing dynamic, real-world interactions. To bridge this gap, this paper proposes a novel real-time hybrid digital twin platform. Its core innovation lies in the tight coupling of a high-fidelity CARLA-SUMO co-simulation with a physical test site and vehicle via a low-latency Vehicle-to-Everything (V2X) communication link. A custom-developed middleware serves as the critical bridge, synchronizing a real CAV's kinematic state as a shadow vehicle in the simulation and translating virtual control commands into chassis-actuating Controller Area Network (CAN) messages for closed-loop control. Detailed implementation includes using photogrammetry for full-scale asset reconstruction and a cloud-edge collaborative architecture for scalable, multi-user operation. Experimental results demonstrate stable synchronization and effective closed-loop control with low latency, confirming the platform's practicality for multi-scenario CAV verification.

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