Digital Twin Technology for Intelligent Vehicles and Transportation Systems: A Survey on Applications, Challenges and Future Directions

  • Xiaohui Gu
  • , Wei Duan
  • , Guoan Zhang
  • , Jia Hou
  • , Limei Peng
  • , Miaowen Wen
  • , Feifei Gao
  • , Min Chen
  • , Pin Han Ho

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

This survey provides a comprehensive analysis of digital twin (DT) technology as a transformative tool for advancing connected and autonomous vehicles (CAVs) and intelligent transportation systems (ITSs), focusing on advancements in vehicle safety, traffic management, and autonomous driving capabilities. The paper begins by discussing the foundational concepts and enabling technologies behind DT systems, setting the stage for their application in transportation networks. We review DT applications in vehicle safety, highlighting their role in real-time monitoring, predictive maintenance, and risk mitigation. Next, we explore the role of DT technology in optimizing traffic flow, enhancing traffic management, and enabling adaptive responses to dynamic conditions. The paper then examines the integration of DTs in intelligent and autonomous vehicles, emphasizing advancements in simulation, testing, and the development of autonomous driving functionalities. Finally, we outline future research opportunities and challenges for DT applications, providing a roadmap for their continued evolution in CAVs and ITS.

Original languageEnglish
Pages (from-to)3235-3271
Number of pages37
JournalIEEE Communications Surveys and Tutorials
Volume28
DOIs
StatePublished - 2026

Keywords

  • Digital twin
  • connected and autonomous vehicles
  • intelligent transportation systems
  • predictive maintenance
  • real-time data analytics
  • traffic management
  • vehicle safety

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