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应用科学学报 ›› 2021, Vol. 39 ›› Issue (2): 210-221.doi: 10.3969/j.issn.0255-8297.2021.02.003

• 智能交通信息新技术 • 上一篇    

基于稀疏栅格优化的蜂窝车联网定位算法

夏小涵1, 蔡超1, 邱佳慧1, 杨静远2, 张香云1, 肖然3   

  1. 1. 中国联合网络通信有限公司智网创新中心, 北京 100048;
    2. 生态环境部核与辐射安全中心, 北京 100082;
    3. 爱立信(中国) 通信有限公司, 北京 100102
  • 收稿日期:2020-11-21 发布日期:2021-04-01
  • 通信作者: 夏小涵,工程师,研究方向为高精定位、RSU、车路协同等。E-mail:sagahan@163.com E-mail:sagahan@163.com
  • 基金资助:
    国家自然科学基金（No.62071031）；国家重点研发计划（No.2018YFB1600600）资助

New Location Algorithm Based on Sparse Grid Optimization in C-V2X

XIA Xiaohan1, CAI Chao1, QIU Jiahui1, YANG Jingyuan2, ZHANG Xiangyun1, XIAO Ran3   

  1. 1. Center of Smart Network of China United Network Communications Co., Ltd., Beijing 100048, China;
    2. Nuclear and Radiation Safety Center, Ministry of Ecology and Environment, Beijing 100082, China;
    3. Ericsson(China) Communications Co., Ltd., Beijing 100102, China
  • Received:2020-11-21 Published:2021-04-01

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摘要： 蜂窝车联网（cellular-V2X，C-V2X）中的定位方案是车路协同与车联网业务发展的重要技术途径之一。目前基于基站、卫星等诸多定位方案，在车联网业务以及车路协同场景中常会遇到定位精度、定位处理时延、部署成本等诸多方面的挑战。针对这些问题，文章对已有栅格定位算法进行优化，提出一种基于统计信息网格（statistical information grid，STING）的稀疏栅格优化算法和基于极端梯度提升（extreme gradient boosting decision tree，XGBoost）进行指纹定位的车联网指纹定位算法。从栅格优化的角度出发，相较于传统指纹定位方法在定位精度和计算速率方面进行了优化，使其更适应于车路协同场景。该算法为目前的车联网定位提供了一种有效的定位方法。

关键词: 蜂窝车联网, 统计信息网格聚类, 指纹定位, 极端梯度提升, 稀疏栅格优化

Abstract: The location algorithm in cellular-V2X (C-V2X) has always been one of the important technical approaches for the development of vehicle-road collaboration and autonomous driving. Currently, in the vehicle-road collaboration scenarios of autonomous driving services, many positioning solutions including base stations and GNSS meet challenges in many aspects such as positioning accuracy, positioning processing delay and deployment cost. In response to these problems, a fingerprint location algorithm is proposed for C-V2X based on statistical information grid (STING) algorithm for grid optimization and extreme gradient boosting decision tree (XGBoost). Compared with traditional fingerprint positioning methods, the positioning accuracy and calculation rate are optimized after grid optimization. The new method is more suitable for vehicle-road collaboration scenarios, and provides an effective positioning method for C-V2X scenarios.

Key words: cellular-V2X (C-V2X), statistical information grid (STING), fingerprint positioning, extreme gradient boosting (XGBoost), sparse grid optimization

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