A Novel Machine Learning-Based Trust Management Against Multiple Misbehaviors for Connected and Automated Vehicles

Connected and Automated Vehicles (CAVs) are exposed to various threats in the dynamic, open and multi-domain network. Applying machine learning-based trust management for CAVs becomes imperative to capture complex features and adapt to dynamic situations. To deal with multiple misbehaviors and real traffic scenarios challenges, a trust management method for CAVs is proposed with data fusion, trust factor computation and two-tier trust prediction. First of all, three types of features on CAVs are analyzed, including spatio-temporal logic features, behavioral features and traffic flow features. Then, data fusion methods that combine beacon messages with map and detector data are proposed to enhance trust-related data. A multi-dimensional trust factor computation approach is then introduced using statistical methods. Finally, per-minute and multi-minute machine learning-based trust prediction methods are performed at the node level using the computed trust labels from each beacon. The results showed the effectiveness and real-time capability of the data fusion process, as well as the completeness of the trust factor computation. The trust prediction results showed both high performance at the per-minute level with models like XGBoost and further improved performance at the multi-minute level with deep learning models.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3422423
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Xu, Qian
  • Zhang, Lei
  • Qin, Xiaojie
  • Zhou, Yixuan
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 16775-16790
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Data fusion; Machine learning
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01945586
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 12 2025 8:59AM