Digital Twin Technology for Intelligent Vehicles and Transportation Systems: A Survey on Applications, Challenges and Future Directions
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.
Authors
- Guoan Zhang (ORCID: https://orcid.org/0000-0001-8439-3478)
- Xiaohui Gu (ORCID: https://orcid.org/0000-0001-8406-0702)
- Limei Peng (ORCID: https://orcid.org/0000-0001-9984-9861)
- Feifei Gao (ORCID: https://orcid.org/0000-0001-8896-352X)
- Wei Duan (ORCID: https://orcid.org/0000-0003-1678-9520)
- Miaowen Wen (ORCID: https://orcid.org/0000-0002-9638-7048)
- Pin–Han Ho (ORCID: https://orcid.org/0000-0002-0717-1481)
- Min Chen (ORCID: https://orcid.org/0000-0002-0960-4447)
- Jia Hou
Institutions
- University of Electronic Science and Technology of China (CN)
- Nantong University (CN)
- Soochow University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- South China University of Technology (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- IEEE Communications Surveys & Tutorials
- Published
- 2025-06-19
- DOI
- https://doi.org/10.1109/comst.2025.3581152
- Citations
- 7
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 8.70
Funders
- National Natural Science Foundation of China