Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept
Kui Wang, Zongdian Li, Kazuma Nonomura, Tao Yu, Kei Sakaguchi, Omar Hashash, Walid Saad
TL;DR
This work presents a Smart Mobility Digital Twin (SMDT) platform that integrates cloud and edge computing with roadside and vehicle sensors to create a real-time traffic digital twin for connected and automated vehicles (CAVs). It introduces a cloud–edge architecture with RSU edges, CAV edges, and a central cloud, enabling DT-based traffic modeling and an event-triggered CAV navigation workflow that uses Dijkstra-based routing and INFINITY-driven re-planning when incidents occur. Proof-of-concept experiments in a real-world Tokyo Tech field and SUMO-based large-scale simulations demonstrate that the proposed system can reduce average travel time and incident-related blocking while meeting 3GPP latency and reliability requirements for SSMS and information sharing use cases. The results support end-to-end feasibility of DT-enabled autonomous driving and point to future city-scale deployments with enhanced predictive capabilities and broader smart-city integration.
Abstract
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.