mlsanityData to 7 Oct 2026

Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept

arXiv 2402.12682 ↗First posted 20 Feb 2024eess.SY cs.SYcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 7.

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  1. 2024Digital Twin in Industries: A Comprehensive Surveycited by 4
  2. 2025SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimizationcited by 3
  3. 2025TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Controlcited by 3
  4. 2024Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approachcited by 2
  5. 2025Multi-Agent Reinforcement Learning-based Cooperative Autonomous Driving in Smart Intersectionscited by 2
  6. 2024Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Drivingcited by 1
  7. 2025Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detectioncited by 1
  8. 2025Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approachcited by 1
  9. 2026Network Digital Untwinning: Towards Backward Optimization of Digital Twinscited by 1
  10. 2026From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Drivingcited by 0
  11. 2026Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twinscited by 0
  12. 2026Multi-Pedestrian Safety Warning at Urban Intersections Use Case of Digital Twincited by 0
3 more
  1. 2026A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Drivingcited by 0
  2. 2026Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networkscited by 0
  3. 2026How Roadside Units Enhance Intersection Safety? Cooperative Autonomous Driving System Design and A Proof of Conceptcited by 0