Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design. Our demonstration video can be found at https://youtu.be/3waQwlaHQkk .

The technical exploration of safe automated driving has evolved from single vehicle intelligence to vehicular networking. Therefore, to a certain extent, the safety requirements have been translated into the requirements for network performance. Although Vehicle-to-Everything (V2X) communication technologies have developed decades of years, from the Dedicated Short-Range Communication (DSRC) to the Long Term Evolution V2X (LTE-V2X), the introduction of millimeter wave (mmWave) in the 5G era will achieve an unprecedented leap in V2X performance by empowering multi-gigabit data transmission with ultra-low latency. Meanwhile, the emergence of Software Defined Networking (SDN) enables centralized network management and allows flexible scheduling of communication resources to match V2X capacities. Their benefits for vehicular networks have been revealed by plenty of studies respectively. Nevertheless, no researchers investigate their combination to improve the safety of automated driving, especially lack of scalable prototype systems for proof-of-concepts (PoCs), not to mention the field test with real traffic. In this paper, we firstly propose a novel architecture, i.e. software-defined dynamic mmWave V2X network, to support cooperative perception. And then, necessary hardware and software platforms are introduced to compose prototype systems. Step by step, we conduct PoCs from the indoors to the outdoors, which demonstrate the network functions, the prototype scalability and most importantly, the significant enhancement to the vehicle perception for safe automated driving.

The transformation and innovation brought by vehicle-to-everything (V2X) communications are ongoing. Enhanced road safety, traffic efficiency, and vehicle automation are all promised on condition that the envisioned V2X applications can be realized. "Share what I can see" is one of the visions, known as cooperative perception (CP). CP enables sensor data sharing, while the types of data are diverse according to the levels of vehicle automation. To support sharing raw, pre-processed, and fully processed data, V2X is advancing. Emerging radio access technologies (RATs) like millimeter waves (mmWave) are introduced. V2X networks become more heterogeneous than ever before. To fill in the gaps that existing V2X networks lack interoperability and a network function to manage heterogeneous V2X, this paper proposes Het-SDVN, a software defined networking (SDN)-based vehicular network (SDVN) architecture for heterogeneous V2X. Compared to the counterparts, Het-SDVN features hierarchical control planes (C-planes), which can reduce overhead and latency, provide scalability, and guarantee the availability of CP. This paper also presents the demonstration of Het-SDVN in a real-life proof of concept. Through the response to diverse CP requests, the required V2X performance, the capability of local SDVN, and the importance of interference management are all revealed. Besides, a benchmarking test in flow installation for local and global SDVN controllers is carried out. Finally, this paper shows the visualization results of CP.

The emergence of 5G-IoT opens up unprecedented connectivity possibilities for new service use cases and players. Multi-access edge computing (MEC) is a crucial technology and enabler for Beyond 5G, supporting next-generation communications with service guarantees (e.g., ultra-low latency, high security) from an end-to-end (E2E) perspective. On the other hand, one notable advance is the platform that supports virtualization from RAN to applications. Deploying Radio Access Networks (RAN) and MEC, including third-party applications on virtualization platforms, and renting other equipment from legacy telecom operators will make it easier for new telecom operators, called Private/Local Telecom Operators, to join the ecosystem. Our preliminary studies have discussed the ecosystem for private and local telecom operators regarding business potential and revenue and provided numerical results. What remains is how Private/Local Telecom Operators can manage and deploy their MEC applications. In this paper, we designed the architecture for fully virtualized MEC 5G cellular networks with local use cases (e.g., stadiums, campuses). We propose an MEC/Cloud Orchestrator implementation for intelligent deployment selection. In addition, we provide implementation schemes in several cases held by either existing cloud owners or private and local operators. In order to verify the proposal’s feasibility, we designed the system level in E2E and constructed a Beyond 5G testbed at the Ōokayama Campus of the Tokyo Institute of Technology. Through proof-of-concept in the outdoor field, the proposed system’s feasibility is verified by E2E performance evaluation. The verification results prove that the proposed approach can reduce latency and provide a more stable throughput than conventional cloud services.

During the past decade, smart mobility and intelligent vehicles have attracted increasing attention, because they promise to create a highly efficient and safe transportation system in the future. Meanwhile, digital twin, as an emerging technology, will play an important role in automated driving and intelligent transportation systems. This technology is applied in this paper to design a platform for smart mobility, providing large-scale route planning services. Utilizing sensing technologies and cloud/edge computing, we build a digital twin system model that reflects the static and dynamic objects from the real world in real time. With the smart mobility platform, we realize traffic monitoring and route planning through cooperative environment perception to help automated vehicles circumvent jams. A proof-of-concept test with a real vehicle in real traffic is conducted to validate the functions and the delay performance of the proposed platform.

Vehicle-road collaboration is a promising approach for enhancing the safety and efficiency of autonomous driving by extending the intelligence of onboard systems to smart roadside infrastructures. The introduction of digital twins (DTs), particularly local DTs (LDTs) at the edge, in smart mobility presents a new embodiment of augmented intelligence, which could enhance information exchange and extract human driving expertise to improve onboard intelligence. This paper presents a novel LDT-assisted hybrid autonomous driving system for improving safety and efficiency in traffic intersections. By leveraging roadside units (RSUs) equipped with sensory and computing capabilities, the proposed system continuously monitors traffic, extracts human driving knowledge, and generates intersectionspecific local driving agents through an offline reinforcement learning (RL) framework. When connected and automated vehicles (CAVs) pass through RSU-equipped intersections, RSUs can provide local agents to support safe and efficient driving in local areas. Meanwhile, they provide real-time cooperative perception (CP) to broaden onboard sensory horizons. The proposed LDT-assisted hybrid system is implemented with stateof-the-art products, e.g., CAVs and RSUs, and technologies, e.g., millimeter-wave (mmWave) communications. Hardware-in-theloop (HiL) simulations and proof-of-concept (PoC) tests validate system performance from two standpoints: (i) The peak latency for CP and local agent downloading are 8.51 ms and 146 ms, respectively, aligning with 3GPP requirements for vehicle-toeverything (V2X) and model transfer use cases. Moreover, (ii) local driving agents can improve safety measures by 10% and reduce travel time by 15% compared with conventional onboard systems. The implemented prototype also demonstrates reliable real-time performance, fulfilling the targets of the proposed system design.