Scalable and interoperable C-V2X framework for real-time intelligent decision support in autonomous mobility

While V2X-based vehicle communication and intersection management have been widely studied, most existing approaches remain limited to simulation-based environments or constrained testbeds. Furthermore, standardized message formats such as SAE J2735 lack the extensibility required to support emerging autonomous vehicle services, leading to inefficient or inflexible system implementations. To address these limitations, this study proposes a modular, edge-intelligent framework−the mobility operating system (mOS)−integrated with a mixed-reality testbed for realistic validation of infrastructure-guided autonomous vehicle coordination. The proposed mOS supports plug-and-play integration of V2X communication modules, real-time intersection management algorithms, and bidirectional interactions between physical and virtual agents. Scenarios involving vehicle-to-vehicle and vehicle-to-pedestrian interactions were conducted to evaluate the effectiveness of mOS under realistic latency and behavioral uncertainty. Key performance metrics, including vehicle speed trajectory and communication latency, were used to measure the responsiveness and accuracy of coordination. The results confirm that the mOS successfully improves safety and behavioral predictability under complex intersection scenarios. This study demonstrates the feasibility of Mixed reality integrated infrastructure intelligence and offers a scalable pathway for deploying AV coordination systems in next-generation innovative mobility ecosystems.