mlsanityData to 13 Oct 2026

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

arXiv 2012.07753 ↗First posted 14 Dec 2020cs.IT cs.NI math.ITcited by 24

Not on the map. Why?

Abstract (arXiv)

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Key facts. Cited by 24 papers in the map (data through 13 Oct 2026). First posted on arXiv 14 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 9; 2025: 6; 2026: 3.

24 papers.

Sort:

By area:

  1. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  2. 2023UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Controlcited by 12
  3. 2022Towards Semantic Communication Protocols: A Probabilistic Logic Perspectivecited by 10
  4. 2024A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehiclescited by 10
  5. 2021Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RANcited by 9
  6. 2024Hierarchical Federated Learning in Multi-hop Cluster-Based VANETscited by 7
  7. 2023Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networkscited by 3
  8. 2024Combining Federated Learning and Control: A Surveycited by 3
  9. 2024Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocationcited by 3
  10. 2025Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networkscited by 2
  11. 2022Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networkscited by 1
  12. 2024Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehiclescited by 1
12 more
  1. 2024Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Predictioncited by 1
  2. 2025Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networkscited by 1
  3. 2025A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applicationscited by 1
  4. 2023A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioningcited by 0
  5. 2024Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communicationscited by 0
  6. 2024Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detectioncited by 0
  7. 2024Joint Channel Selection using FedDRL in V2Xcited by 0
  8. 2025AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networkscited by 0
  9. 2025A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networkingcited by 0
  10. 2026Hierarchical Multi-Task Federated Learning in VANETscited by 0
  11. 2026HMS-SCP: Task-Oriented Multi-Scale Semantic Communication for V2X Cooperative Perceptioncited by 0
  12. 2026Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETscited by 0