Volume 41 Issue 4
Aug.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(4): 80-87
WANG Zhongqi, CHEN Wei, DU Luyao, LEI Zhen, LEI Ting. Cooperative Positioning of Vehicle Fleets Using Road Probability Field[J]. Journal of Transport Information and Safety, 2023, 41(4): 80-87. doi: 10.3963/j.jssn.1674-4861.2023.04.009
Citation: WANG Zhongqi, CHEN Wei, DU Luyao, LEI Zhen, LEI Ting. Cooperative Positioning of Vehicle Fleets Using Road Probability Field[J]. Journal of Transport Information and Safety, 2023, 41(4): 80-87. doi: 10.3963/j.jssn.1674-4861.2023.04.009
shu

Cooperative Positioning of Vehicle Fleets Using Road Probability Field

doi: 10.3963/j.jssn.1674-4861.2023.04.009
  • 1.

    School of Automation, Wuhan University of Technology, Wuhan 430070, China

  • 2.

    University of Kansas, Lawrence 66045, USA

  • Received Date: 2023-02-10
    Available Online: 2023-11-23
    Abstract
  • Scalar field methods are widely used in the coordinated positioning of groups of unmanned automatic vehicles (UAVs) and submarines. However, there are difficulties in applying similar scalar fields such as magnetic anomaly fields and water depth fields in vehicle fleet scenarios. To address this issue, a vehicle fleet collaborative positioning method based on road probability field and vehicle motion model is proposed an open-source database is used to obtain electronic maps, and the electronic maps were buffered, rasterized, and processed with mathematical morphology to construct road probability field. At the same time, a vehicle motion model is established based on GNSS technology, using the relative positions between vehicles in the fleet as collaborative information, taking the value of the predicted position of the vehicle in the road probability field as a weight calculation criterion, and using particle filtering localization algorithms to continually update the predicted trajectory of the vehicle. The new method establishes the road probability field as a scalar field, using the road probability value corresponding to the vehicle position as an important basis for determining the vehicle position, and applying the geographic spatial information contained in the electronic map to the collaborative positioning of the vehicle fleet. Unlike traditional scalar field methods, road probability fields do not require new specialized measurements and can be generated using the massive electronic map resources already available, and vehicles do not require new sensors. A vehicle motion model is designed based on the application scenario, and the trajectory is continuously optimized using the road probability field during the dynamic process of vehicle driving, reflecting the difference with traditional fleet positioning methods that pay more attention to single time point positioning. Comparative tests were conducted on different buffer widths and vehicle numbers in real and simulation experiments. The results show that using positioning error as the criterion for determining the positioning effect, compared to the classic extended Kalman filtering method using vehicle motion models, proposed method achieves improvements of 49.6% and 49.8% in simulated and real scenarios, respectively. Compared with the fleet collaborative localization method based on empty road probability field, this method has improved by 59.5% and 50.3% in simulation and real scenarios, respectively. This study provides a new method for cooperative positioning by constructing a road probability field and utilizing a vehicle motion model. Compared to traditional methods, this method improves the accuracy and reliability of positioning and has important application prospects.

     

    • FullText(HTML)
  • loading
    • References(24)
  • [1]
    李博峰, 陈广鄂. GNSS/INS组合车辆协同精密定位方法[J]. 真实值观测值EKF预测值VMMCL-RPF预测值86测绘学报, 2022, 51(8): 1708-1716. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202208004.htm

    LI B F, CHEN G E. Precise cooperative positioning for vehicles with GNSS and INS integration[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(8): 1708-1716. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202208004.htm
    [2]
    上官伟, 庞晓宇, 李秋艳, 等. 车路协同环境下群体车辆诱导与协同运行方法[J]. 交通运输工程学报, 2022, 22(3): 68-78. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202203005.htm

    SHANG G W, PANG X Y, LI Q Y, et al. Guidance and cooperative operation method for group vehicles in vehicle-infrastructure cooperative environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 68-78. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202203005.htm
    [3]
    张毅, 裴华鑫, 姚丹亚. 车路协同环境下车辆群体协同决策研究综述[J]. 交通运输工程学报, 2022, 22(3): 1-18. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202203001.htm

    ZHANG Y, PEI H X, YAO D Y. Research review on cooperative decision-making for vehicle swarms in vehicle-infrastructure cooperative environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 1-18. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202203001.htm
    [4]
    FERRI G, MunafòA, TESEI A, et al. Cooperative robotic networks for underwater surveillance: an overview[J]. Iet Radar Sonar & Navigation, 2017, 11(12): 1740-1761.
    [5]
    ROHANI M, GINGRAS D, GRUYER D. Vehicular cooperative map matching[C]. International Conference on Connected Vehicles & Expo, Vienna, Austria: IEEE, 2014.
    [6]
    QIAN C, ZHANG H, LI W, et al. Cooperative GNSS-RTK ambiguity resolution with GNSS, INS, and LiDAR data for connected vehicles[J]. Remote Sensing, 2020, 12(6): 949-972. doi: 10.3390/rs12060949
    [7]
    ALAM N, BALAEI A T, DEMPSTER A G. A DSRC doppler-based cooperative positioning enhancement for vehicular networks with GPS availability[J]. IEEE Transactions on Vehicular Technology, 2011, 60(9): 4462-4470. doi: 10.1109/TVT.2011.2168249
    [8]
    ROHANI M, GINGRAS D, GRUYER D. A novel approach for improved vehicular positioning using cooperative map matching and dynamic base station DGPS concept[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(1): 230-239. doi: 10.1109/TITS.2015.2465141
    [9]
    SORENSON H W. Kalman filtering: theory and applications[M]. New York: IEEE Press, 1985.
    [10]
    SIMON D. Optimal State Estimation: Kalman, H∞, and nonlinear approaches[M]. New York: Wiley-Interscience, 2006.
    [11]
    BOUNINI F, GINGRAS D, POLLART H, et al. Real time cooperative localization for autonomous vehicles[C]. 2016 IEEE 19th International Conference on Intelligent Transportation Systems(ITSC), Rio de Janeiro, Brazil, IEEE, 2016.
    [12]
    SAKR A H, BANSAL G. Cooperative localization via DSRC and multi-sensor multi-target track association[C]. IEEE International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil, IEEE, 2016.
    [13]
    RIVOIRARD L, WAHL M, SONDI P, et al. A cooperative vehicle ego-localization application using V2V communications with CBL clustering[C]. 2018 IEEE Intelligent Vehicles Symposium(Ⅳ), Changshu, China: IEEE, 2018.
    [14]
    SONG X, LING Y, CAO H, et al. Cooperative vehicle localisation method based on the fusion of GPS, inter-vehicle distance, and bearing angle measurements[J]. Intelligent Transport Systems, IET, 2019, 13(4): 644-653. doi: 10.1049/iet-its.2018.5091
    [15]
    付梦印, 邓志红, 张继伟. Kalman滤波理论及其在导航系统中的作用[M]. 北京: 科学出版社, 2003.

    FU M Y, DENG Z H, ZHANG J W. Kalman filter theory and its role in navigation systems[M]. Beijing: Science Press, 2003. (in Chinese)
    [16]
    SHEN C, ZHANG Y, GUO X T, et al. Seamless GNSS/inertial navigation system based on self-learning square-root cubature Kalman filter[J]. IEEE Transaction On Industrial Electronics, 2021, 68(1): 499-508. doi: 10.1109/TIE.2020.2967671
    [17]
    YANG C, STRADER J, GU Y, et al. Cooperative navigation using pairwise communication with ranging and magnetic anomaly measurements[J]. Journal of Aerospace Information Systems, 2020, 17(11): 624-633. doi: 10.2514/1.I010785
    [18]
    CANCIANI A, RAQUET J. Absolute positioning using the earth's magnetic anomaly field[J]. Navigation-Journal of The Institute of Navigation, 2016, 63(2): 111-126. doi: 10.1002/navi.138
    [19]
    MELO J, MATOS A. Survey on advances on terrain based navigation for autonomous underwater vehicles[J]. Ocean Engineering, 2017, 139(15): 250-264.
    [20]
    HAN Y R, WANG B, DENG Z H, et al. A combined matching algorithm for underwater gravity-aided navigation[J]. IEEE-Asme Transactions On Mechatronics, 2018, 23(1): 233-241.
    [21]
    YANG C, STRADER J, GU Y. A scalable framework for map matching based cooperative localization[J]. Sensors, 2021, 21(19): 6400-6414.
    [22]
    WARMERDAM F. The geospatial data abstraction library[J]. Open Source Approaches in Spatial Data Handling, 2008, 2(1): 87-104.
    [23]
    李俊山, 李旭辉. 数字图像处理[M]. 2版. 北京: 清华大学出版社, 2013.

    LI J S, LI X H. Digital image processing[M]. 2nd ed Beiing: Tsinghua University Press, 2013. (in Chinese)
    [24]
    ZHIYAO ZHAO H F. Towards exploring patterns of editing behavior on openstreetmap[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2): 85-97.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (387) PDF downloads(12) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return