A Survey on Semantic Communications in Internet of Vehicles.

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2025-04-20 DOI:10.3390/e27040445
Sha Ye, Qiong Wu, Pingyi Fan, Qiang Fan
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引用次数: 0

Abstract

The 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-the-art semantic communications in the IoV, elaborates the technical background of the IoV and semantic communications, and deeply discusses key technologies of semantic communications in the 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 have broad application prospects in the IoV, but it is necessary to solve the real existing problems by combining advanced technologies to promote their wide application in the IoV and contributing to the development of intelligent transportation systems.

车联网语义通信研究进展。
车联网(IoV)作为智能交通系统的核心,通过多种通信方式实现车辆与周围环境的全面互联,对自动驾驶和智能交通管理具有重要意义。然而,随着新应用的出现,传统通信技术面临频谱资源稀缺和高延迟的问题。语义通信侧重于从消息中提取、传输和恢复一些有用的语义信息,可以减少冗余数据传输,提高频谱利用率,为车联网通信挑战提供创新的解决方案。本文系统综述了车联网语义通信的研究现状,阐述了车联网和语义通信的技术背景,并对车联网语义通信的关键技术进行了深入探讨,包括语义信息提取、语义通信架构、资源分配与管理等。通过具体案例研究,论证了语义通信可以有效应用于交通环境感知与理解、智能驾驶决策支持、车联网服务优化和智能交通管理等场景。并分析了当前面临的挑战和未来的研究方向。研究表明,语义通信在车联网中具有广阔的应用前景,但需要结合先进技术解决现实存在的问题,促进语义通信在车联网中的广泛应用,为智能交通系统的发展做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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