基于稀疏栅格优化的蜂窝车联网定位算法

doi:10.3969/j.issn.0255-8297.2021.02.003

Abstract

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

CLC Number:

TN913

XIA Xiaohan, CAI Chao, QIU Jiahui, YANG Jingyuan, ZHANG Xiangyun, XIAO Ran. New Location Algorithm Based on Sparse Grid Optimization in C-V2X[J]. Journal of Applied Sciences, 2021, 39(2): 210-221.

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

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