mlsanityData to 14 Oct 2026

Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey

arXiv 2505.07882 ↗First posted 10 May 2025cs.AI cs.LGcited by 0

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Abstract (arXiv)

Connected Autonomous Vehicles (CAVs) operate in dynamic, open, and multi-domain networks, rendering them vulnerable to various threats. Trust Management Systems (TMS) systematically organize essential steps in the trust mechanism, identifying malicious nodes against internal threats and external threats, as well as ensuring reliable decision-making for more cooperative tasks. Recent advances in machine learning (ML) offer significant potential to enhance TMS, especially for the strict requirements of CAVs, such as CAV nodes moving at varying speeds, and opportunistic and intermittent network behavior. Those features distinguish ML-based TMS from social networks, static IoT, and Social IoT. This survey proposes a novel three-layer ML-based TMS framework for CAVs in the vehicle-road-cloud integration system, i.e., trust data layer, trust calculation layer and trust incentive layer. A six-dimensional taxonomy of objectives is proposed. Furthermore, the principles of ML methods for each module in each layer are analyzed. Then, recent studies are categorized based on traffic scenarios that are against the proposed objectives. Finally, future directions are suggested, addressing the open issues and meeting the research trend. We maintain an active repository that contains up-to-date literature and open-source projects at https://github.com/octoberzzzzz/ML-based-TMS-CAV-Survey.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 204 references that are in the map. First posted on arXiv 10 May 2025.

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29 of 204 references.

References · 29

  1. 2020Self-supervised Learning: Generative or Contrastivecited by 694
  2. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 265
  3. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 231
  4. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 207
  5. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  6. 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
  7. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  8. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  9. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 30
  10. 2020Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunitiescited by 27
  11. 2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
  12. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
17 more
  1. 2023TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Supportcited by 12
  2. 2022A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networkscited by 11
  3. 2022TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Naturecited by 10
  4. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  5. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  6. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
  7. 2016Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systemscited by 6
  8. 2019Evaluating Reputation Management Schemes of Internet of Vehicles based on Evolutionary Game Theorycited by 3
  9. 2021Towards a Machine Learning-driven Trust Evaluation Model for Social Internet of Things: A Time-aware Approachcited by 3
  10. 2022Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learningcited by 3
  11. 2022Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddingscited by 3
  12. 2022Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networkscited by 3
  13. 2023Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approachcited by 3
  14. 2022Understanding the Trustworthiness Management in the Social Internet of Things: A Surveycited by 2
  15. 2024FEDQ-Trust: Efficient Data-Driven Trust Prediction for Mobile Edge-Based IoT Systemscited by 2
  16. 2023Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networkscited by 1
  17. 2023Reinforcement Learning Based Neighbour Selection for VANET with Adaptive Trust Managementcited by 1

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