Introducing mOS: A Scalable, Interoperable Framework for Real-time Intelligent decision support

Previous studies on V2X-based vehicular communication and intersection management have predominantly focused on simulation environments or constrained testbeds, overlooking critical aspects, including the scalability of the autonomous driving environment, as well as infrastructures, vehicles, and control data exchange. This limitation is particularly pronounced in existing research on intelligent intersections, where most coordination algorithms assume idealized infrastructure, low-latency communication, and fully autonomous agents. Moreover, standardized message sets like SAE J2735 lack the flexibility to accommodate the dynamic and high-volume data required for emerging CAV services. As a result, the system's frequent updates often necessitate the implementation of inefficient workarounds or entirely new protocols.

To address these limitations and validate infrastructure-guided coordination in realistic conditions, researchers at Korea Advanced Institute of Science and Technology (KAIST) developed 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 at Munji Campus testbed of KAIST. The proposed mOS supports plug-and-play integration of V2X communication modules, real-time intersection management algorithms, and bidirectional interaction 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.  Results confirm that the mOS successfully improves safety and behavioral predictability under complex intersection scenarios. This study demonstrates the feasibility of MR-integrated infrastructure intelligence and offers a scalable pathway for deploying AV coordination systems in next-generation innovative mobility ecosystems.

They published their study on Scalable and interoperable C-V2X framework for real-time intelligence decision support in autonomous mobility, in Communications in Transportation Research (https://doi.org/10.26599/COMMTR.2026.9640001).

“As autonomous driving technology advances toward commercialization, real-time interaction between vehicles and infrastructure is emerging as a crucial factor in ensuring traffic efficiency and safety. However, current V2X systems remain heavily focused on the communication layer and lack sufficient intelligence at the infrastructure level to respond adaptively to the complexity of physical world traffic scenarios. In particular, unsignalized intersections, non-connected entities, and heterogeneous road users present challenges that existing message structures and decision algorithms are not fully equipped to handle.

To address these issues, this study proposed a modular, edge-intelligence-based mOS and validated it through actual intersection deployments and metaverse-integrated MR simulation environments. The proposed framework, built on a containerized architecture and an extensible message structure, demonstrated flexibility in supporting a variety of decision-making algorithms and communication protocols. By enabling interaction between physical vehicles and virtual objects, the testbed facilitated the safe and repeatable evaluation of high-risk intersection scenarios, as well as the quantitative assessment of algorithm effectiveness.

The results show that the system successfully overcomes the limitations of the existing J2735-based binary message structure while maintaining real-time performance. The intelligent control algorithm applied to unsignalized intersections was shown to produce meaningful improvements in vehicle behavior control. Additionally, the use of virtual object injection in sensor-limited environments and the integration of metaverse-based experiments reduced both cost and safety risks, offering a practical platform for advanced testing.

Although the study was conducted using a commercial 5G network rather than a dedicated C-V2X infrastructure, the observed latency and jitter were within acceptable thresholds. It is expected that future deployment using dedicated C-V2X networks will further enhance system stability and precision. Furthermore, to more rigorously validate the effectiveness of the proposed framework, a direct comparative evaluation against existing V2X systems will be an important direction for future research. In conclusion, this research presented a novel intersection management model that integrates intelligent infrastructure design, flexible message architecture, and virtual-real experimental platforms.”

This work was supported by the Ministry of Land, Infrastructure and Transport of Korean government [grant number code RS-2023-00233952].

About Communications in Transportation Research

Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Xiaopeng (Shaw) Li from University of Wisconsin–Madison. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems,aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.

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From Volume 6 (2026), Communications in Transportation Research will be published by Tsinghua University Press on the SciOpen platform with the official journal website at https://www.sciopen.com/journal/2097-5023. We kindly request that all new manuscript submissions be made through the journal’s submission system at https://mc03.manuscriptcentral.com/commtr. For any submission-related inquiries, please contact the Editorial Office at commtr_e@mail.tsinghua.edu.cn.