Vehicle high-precision positioning considering communication delay for intelligent vehicle-infrastructure cooperation system

被引:0
作者
Zhang H. [1 ,2 ]
Qian C. [3 ]
Zhao Q. [1 ]
Li W. [1 ]
Li B. [1 ,2 ]
机构
[1] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan
[2] Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan
[3] Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan
来源
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2024年 / 53卷 / 01期
关键词
factor graph; high-precision positioning; intelligent vehicle-infrastructure cooperation systems; roadside perception; target recognition and localization; wireless communication delay;
D O I
10.11947/j.AGCS.2024.20220626
中图分类号
学科分类号
摘要
In recent years, with the development of intelligent transportation and communication technology, intelligent vehicle-infrastructure cooperation systems have attracted widespread attention. The location features of vehicles are the basic elements in intelligent transportation. In the vehicle-infrastructure collaborative environment, the vehicle can receive the positioning information of the roadside unit through the communication device for self-vehicle positioning. This paper aims to solve the problem of positioning errors caused by unstable communication delays in the vehicle-infrastructure collaborative environment and proposes a high-precision vehicle positioning model based on factor graphs that considers communication delays. In the absence of global navigation satellite system (GNSS) information, the target vehicle is identified and located based on the roadside light detection and ranging (LiDAR) point cloud clustering method. The target positioning result is sent to the vehicle through the 4G communication network. The factor graph is used to directly fuse the measurement information of the vehicle inertial measurement unit (IMU) at the current moment with the lagging roadside target location results. Based on the incremental smoothing inference method, the optimal estimation of the vehicle position, speed and attitude is realized. Finally, combined with the measured and simulated data, the method proposed in this paper is verified by real vehicle experiments. Compared with the traditional extrapolation method of processing time delay, the results show that our method can improve the accuracy of vehicle positioning and speed measurement and eliminate the influence of highly unstable communication delay on positioning. © 2024 SinoMaps Press. All rights reserved.
引用
收藏
页码:101 / 117
页数:16
相关论文
共 38 条
[1]  
ZHANG Yi, YAO Danya, LI Li, Et al., Technologies and applications for intelligent vehicle-infrastructure cooperation systems, Journal of Transportation Systems Engineering and Information Technology, 21, 5, pp. 40-51, (2021)
[2]  
OU C H., A roadside unit-based localization scheme for vehicular ad hoc networks, International Journal of Communication Systems, 27, 1, pp. 135-150, (2014)
[3]  
MA Sugang, WEN Fuxi, ZHAO Xiangmo, Et al., An efficient V2X based vehicle localization using single RSU and single receiver, IEEE Access, 7, pp. 46114-46121, (2019)
[4]  
DONG Zhi, YAO Bobin, Angle-awareness based joint cooperative positioning and warning for intelligent transportation systems, Sensors, 20, 20, (2020)
[5]  
FASCISTA A, CICCARESE G, COLUCCIA A, Et al., A localization algorithm based on V2I communications and AOA estimation, IEEE Signal Processing Letters, 24, 1, pp. 126-130, (2017)
[6]  
QIU Yifei, LU Peng, LIU Xiaokai, Et al., Research on prefilter location algorithm based on RSSI, Radio Engineering, 51, 5, pp. 367-372, (2021)
[7]  
OU C H, WU Bingyi, CAI Lin, GPS-free vehicular localization system using roadside units with directional antennas, Journal of Communications and Networks, 21, 1, pp. 12-24, (2019)
[8]  
SANTOS F A, AKABANE A T, YOKOYAMA R S, Et al., A roadside unit-based localization scheme to improve positioning for vehicular networks, Proceedings of 2016 Vehicular Technology Conference, pp. 1-5, (2016)
[9]  
YU Biao, DONG Lin, XUE Deyi, Et al., A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization, Journal of Intelligent Transportation Systems, 23, 1, pp. 84-98, (2019)
[10]  
CAO Libo, CHEN Zheng, YAN Lingbo, Et al., Vehicle positioning system based on RFID, vision and UWB, Automotive Engineering, 39, 2, pp. 225-231, (2017)