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

arXiv: https://arxiv.org/abs/2012.07753 · HTML: https://mlsanity.com/paper/2012.07753/

## Key facts

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

## Built on by (most cited first, 24 of 24)

- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control](https://mlsanity.com/paper/2312.05090/) (2023)
- [Towards Semantic Communication Protocols: A Probabilistic Logic Perspective](https://mlsanity.com/paper/2207.03920/) (2022)
- [A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles](https://mlsanity.com/paper/2403.12997/) (2024)
- [Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN](https://mlsanity.com/paper/2105.01478/) (2021)
- [Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs](https://mlsanity.com/paper/2401.10361/) (2024)
- [Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks](https://mlsanity.com/paper/2312.02471/) (2023)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation](https://mlsanity.com/paper/2412.07428/) (2024)
- [Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networks](https://mlsanity.com/paper/2503.01116/) (2025)
- [Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks](https://mlsanity.com/paper/2209.13006/) (2022)
- [Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles](https://mlsanity.com/paper/2407.17211/) (2024)
- [Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction](https://mlsanity.com/paper/2409.09978/) (2024)
- [Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2505.01186/) (2025)
- [A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications](https://mlsanity.com/paper/2506.09512/) (2025)
- [A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning](https://mlsanity.com/paper/2305.09832/) (2023)
- [Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications](https://mlsanity.com/paper/2401.02323/) (2024)
- [Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection](https://mlsanity.com/paper/2405.17497/) (2024)
- [Joint Channel Selection using FedDRL in V2X](https://mlsanity.com/paper/2410.20687/) (2024)
- [AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks](https://mlsanity.com/paper/2508.00011/) (2025)
- [A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking](https://mlsanity.com/paper/2512.10874/) (2025)
- [Hierarchical Multi-Task Federated Learning in VANETs](https://mlsanity.com/paper/2608.08111/) (2026)
- [HMS-SCP: Task-Oriented Multi-Scale Semantic Communication for V2X Cooperative Perception](https://mlsanity.com/paper/2608.14603/) (2026)
- [Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs](https://mlsanity.com/paper/2609.36157/) (2026)

## 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.