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基于车辆行为分析的智能车联网关键技术研究

张海霞 李腆腆 李东阳 刘文杰

引用本文: 张海霞, 李腆腆, 李东阳, 刘文杰. 基于车辆行为分析的智能车联网关键技术研究[J]. 电子与信息学报, 2020, 42(1): 36-49. doi: 10.11999/JEIT190820 shu
Citation:  Haixia ZHANG, Tiantian LI, Dongyang LI, Wenjie LIU. Research on Vehicle Behavior Analysis Based Technologies for Intelligent Vehicular Networks[J]. Journal of Electronics and Information Technology, 2020, 42(1): 36-49. doi: 10.11999/JEIT190820 shu

基于车辆行为分析的智能车联网关键技术研究

    作者简介: 张海霞: 女,1979年生,教授,博士生导师,研究方向为智能通信与网络;
    李腆腆: 女,1985年生,博士生,研究方向为无线通信;
    李东阳: 男,1992年生,博士生,研究方向为无线大数据;
    刘文杰: 男,1995年生,博士生,研究方向为边缘缓存
    通讯作者: 张海霞,haixia.zhang@sdu.edu.cn
  • 基金项目: 国家自然科学基金(61860206005)

摘要: 车联网通信系统中通信节点的高移动性、移动行为的复杂性,使得此场景下通信业务呈现数据实时交互性强、空时分布不均、尺度多变、规律复杂的特征,导致传统的车联网网络部署、资源调配难以有效满足用户的差异化服务质量需求。因此,迫切需要设计“车-人-路-云”泛在互联的智能异构车联网网络,通过充分挖掘车辆行为数据的潜在价值,精准预测、刻画车辆行为的空时分布特性,以提升车联网资源利用率、改善车联网服务性能。该文全面梳理了国内外在车辆行为分析、网络部署与接入以及资源优化方面的相关工作,重点阐述了智能车联网关键使能技术,即如何借助先进的人工智能、数据分析技术,探索车联网中车辆行为的空时分布特性,建立车辆行为预测模型,进行智能化网络部署与多网接入、动态资源优化管理,实现高容量、高效率的智能车联网通信。

English

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  • 图 1  智能异构车联网网络架构

    图 2  智能车联网关键技术基本框架

    图 3  STDenseNet预测框架

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文章相关
  • 通讯作者:  张海霞, haixia.zhang@sdu.edu.cn
  • 收稿日期:  2019-10-24
  • 录用日期:  2019-12-01
  • 网络出版日期:  2019-12-10
  • 刊出日期:  2020-01-01
通讯作者: 陈斌, bchen63@163.com
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