通信学报 ›› 2021, Vol. 42 ›› Issue (6): 52-61.doi: 10.11959/j.issn.1000-436x.2021110

• 学术论文 • 上一篇    下一篇

城市场景中车联网时空数据分析及其通达性方法

程久军1, 原桂远1, 崔杰2, 周爱国3, 吕博4, 李光耀5   

  1. 1 同济大学嵌入式系统与服务计算教育部重点实验室,上海 200092
    2 安徽大学计算机科学与技术学院,安徽 合肥 230601
    3 同济大学机械与能源工程学院,上海 200092
    4 上海师范大学天华学院,上海 201815
    5 同济大学电子与信息工程学院,上海 200092
  • 修回日期:2021-04-19 出版日期:2021-06-25 发布日期:2021-06-01
  • 作者简介:程久军(1974− ),男,安徽怀宁人,博士,同济大学教授、博士生导师,主要研究方向为车联网、无人驾驶等
    原桂远(1993− ),男,山东烟台人,同济大学博士生,主要研究方向为车联网、无人驾驶等
    崔杰(1980− ),男,河南淮阳人,博士,安徽大学教授、博士生导师,主要研究方向为车联网安全、物联网安全等
    周爱国(1973− ),男,湖南株洲人,博士,同济大学副教授、博士生导师,主要研究方向为机器人控制、无人驾驶等
    吕博(1977− ),女,辽宁抚顺人,博士,上海师范大学副教授,主要研究方向为计算机视觉、机器学习等
    李光耀(1965− ),男,安徽安庆人,博士,同济大学教授、博士生导师,主要研究方向为图形图像、数据挖掘及人工智能等
  • 基金资助:
    国家自然科学基金资助项目(61872271);中央高校基本科研业务费重点领域学科交叉重大基金资助项目(22120190208);网络与交换技术国家重点实验室(北京邮电大学)开放课题基金资助项目(SKLNST-2020-1-20)

Spatio-temporal data analysis and accessibility method for IoV in an urban scene

Jiujun CHENG1, Guiyuan YUAN1, Jie CUI2, Aiguo ZHOU3, Bo LYU4, Guangyao LI5   

  1. 1 Ministry of Education Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai 200092, China
    2 School of Computer Science and Technology, Anhui University, Hefei 230601, China
    3 School of Mechanical Engineering, Tongji University, Shanghai 200092, China
    4 Tianhua College, Shanghai Normal University, Shanghai 201815, China
    5 College of Electronic and Information Engineering, Tongji University, Shanghai 200092, China
  • Revised:2021-04-19 Online:2021-06-25 Published:2021-06-01
  • Supported by:
    The National Natural Science Foundation of China(61872271);The Fundamental Research Funds for the Central Universities(22120190208);Open Foundation of State key Laboratory of Networking and Switching Technology (Bei-jing University of Posts and Telecommunications)(SKLNST-2020-1-20)

摘要:

为了解决城市场景中车联网时空数据异构以及单个基础设施范围内存在连通效率低下的问题,提出一种车联网时空数据分析及其通达性方法。首先,给出基于噪声去除和数据填充的时空数据分析方法,构建基于张量因子聚合的神经网络预测车辆之间的连通强度;然后,基于车联网连通强度给出有基础设施车联网的通达性方法。仿真实验结果表明,基于张量因子聚合的神经网络可以有效预测车辆之间的连通强度,所提方法可以有效减少连通冗余和路边基础设施负载。

关键词: 车联网, 时空数据分析, 通达性, 城市场景

Abstract:

In order to solve the problems of diversity spatio-temporal data and low connectivity efficiency in a single road side unit for Internet of vehicles (IoV) in an urban scene, a spatio-temporal data analysis and accessibility method was presented.First, a spatio-temporal data analysis method based on de-noising and data filling was introduced, and a tensor factor aggregation-based neural network was constructed to predict connectivity intensity among vehicles.Then, a connectivity intensity prediction-based accessibility method was proposed.The simulation results demonstrate that the proposed connectivity intensity prediction method can accurately predict connectivity intensity among vehicles, and the proposed accessibility method can effectively reduce connectivity redundancy and loads of road side units.

Key words: Internet of vehicles, spatio-temporal data analysis, accessibility, urban scene

中图分类号: 

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