mlsanityData to 14 Oct 2026

A Survey on Semantic Communications in Internet of Vehicles

arXiv 2503.03767 ↗First posted 3 Mar 2025cs.NI cs.LG eess.SPcited by 3

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Abstract (arXiv)

Internet of Vehicles (IoV), as the core of intelligent transportation system, enables comprehensive interconnection between vehicles and their surroundings through multiple communication modes, which is significant for autonomous driving and intelligent traffic management. However, with the emergence of new applications, traditional communication technologies face the problems of scarce spectrum resources and high latency. Semantic communication, which focuses on extracting, transmitting, and recovering some useful semantic information from messages, can reduce redundant data transmission, improve spectrum utilization, and provide innovative solutions to communication challenges in the IoV. This paper systematically reviews state of art of semantic communications in the IoV, elaborates the technical background of IoV and semantic communications, and deeply discusses key technologies of semantic communications in IoV, including semantic information extraction, semantic communication architecture, resource allocation and management, and so on. Through specific case studies, it demonstrates that semantic communications can be effectively employed in the scenarios of traffic environment perception and understanding, intelligent driving decision support, IoV service optimization, and intelligent traffic management. Additionally, it analyzes the current challenges and future research directions. This survey reveals that semantic communications has broad application prospects in IoV, but it is necessary to solve the real existing problems by combining advanced technologies to promote its wide application in IoV and contributing to the development of intelligent transportation system.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 61 of its 147 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

61 of 147 references.

References · 61

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38 more
  1. 2022High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networkscited by 26
  2. 2023Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Studycited by 24
  3. 2022Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Methodcited by 23
  4. 2023Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoonscited by 23
  5. 2023URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computingcited by 22
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  7. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
  8. 2024Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learningcited by 17
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  16. 2023Generative AI-driven Semantic Communication Framework for NextG Wireless Networkcited by 10
  17. 2024A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehiclescited by 10
  18. 2024Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systemscited by 9
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  21. 2023Semantic Communication for Cooperative Perception based on Importance Mapcited by 8
  22. 2023Scalable Multi-task Semantic Communication System with Feature Importance Rankingcited by 7
  23. 2023Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaversescited by 7
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  25. 2024Agent-driven Generative Semantic Communication with Cross-Modality and Predictioncited by 7
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  38. 2023Task-driven Semantic-aware Green Cooperative Transmission Strategy for Vehicular Networkscited by 4

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networkscited by 3
  2. 2025Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPMcited by 1
  3. 2025Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Applicationcited by 0