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

arXiv: https://arxiv.org/abs/2505.07882 · HTML: https://mlsanity.com/paper/2505.07882/

## 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.

## Builds on (most cited first, 29 of 29)

- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
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- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [Federated Continual Learning via Knowledge Fusion: A Survey](https://mlsanity.com/paper/2312.16475/) (2023)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities](https://mlsanity.com/paper/2003.13213/) (2020)
- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support](https://mlsanity.com/paper/2306.13339/) (2023)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature](https://mlsanity.com/paper/2205.12784/) (2022)
- [Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey](https://mlsanity.com/paper/2307.04338/) (2023)
- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
- [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024)
- [Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/1610.06810/) (2016)
- [Evaluating Reputation Management Schemes of Internet of Vehicles based on Evolutionary Game Theory](https://mlsanity.com/paper/1902.04667/) (2019)
- [Towards a Machine Learning-driven Trust Evaluation Model for Social Internet of Things: A Time-aware Approach](https://mlsanity.com/paper/2102.10998/) (2021)
- [Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning](https://mlsanity.com/paper/2202.02452/) (2022)
- [Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings](https://mlsanity.com/paper/2204.06988/) (2022)
- [Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks](https://mlsanity.com/paper/2210.07707/) (2022)
- [Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach](https://mlsanity.com/paper/2306.04366/) (2023)
- [Understanding the Trustworthiness Management in the Social Internet of Things: A Survey](https://mlsanity.com/paper/2202.03624/) (2022)
- [FEDQ-Trust: Efficient Data-Driven Trust Prediction for Mobile Edge-Based IoT Systems](https://mlsanity.com/paper/2404.18356/) (2024)
- [Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks](https://mlsanity.com/paper/2304.08875/) (2023)
- [Reinforcement Learning Based Neighbour Selection for VANET with Adaptive Trust Management](https://mlsanity.com/paper/2310.00635/) (2023)

## 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.