A method of vehicle-infrastructure cooperative perception based vehicle state information fusion using improved kalman filter

被引:27
作者
Mo, Yanghui [1 ]
Zhang, Peilin [1 ]
Chen, Zhijun [1 ]
Ran, Bin [2 ]
机构
[1] Wuhan Univ Technol, Wuhan, Peoples R China
[2] Univ Wisconsin, Madison, WI USA
基金
国家重点研发计划;
关键词
Vehicle-infrastructure cooperative perception; Cooperative automated driving system; Position data fusion; Kalman filter; LIDAR; FRAMEWORK; TRACKING; CAMERA;
D O I
10.1007/s11042-020-10488-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
For the purpose of overcoming the technical bottlenecks and limitations of autonomous vehicles on the information perception, and improving the sensing range and performance of vehicle driving environment and traffic information, a framework of vehicle-infrastructure cooperative perception for the Cooperative Automated Driving System is proposed in this paper. Taking the vehicle state information as an example, it also introduced a calculation method of data fusion for vehicle-infrastructure cooperative perception. Besides, considering that the intelligent roadside equipment may appear short-term sensing failure, the proposed method improved the traditional Kalman Filter to output position information even when the roadside fails. Compared with the vehicle-only perception, the simulation experiments verified that the proposed method could improve the average positioning accuracy under the normal condition and the intelligent roadside failure by 18% and 19%, respectively. The proposed framework provided a solution for coordinating and fusing perception intelligence and functions between connected automated vehicles, intelligent infrastructure and intelligent control system. The proposed improved Kalman Filter method provides flexible strategies for practical application.
引用
收藏
页码:4603 / 4620
页数:18
相关论文
共 39 条
  • [1] Arnold E, 2019, ARXIV191212147V2, V1, P1
  • [2] On the Performance of IEEE 802.11p and LTE-V2V for the Cooperative Awareness of Connected Vehicles
    Bazzi, Alessandro
    Masini, Barbara M.
    Zanella, Alberto
    Thibault, Ilaria
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (11) : 10419 - 10432
  • [3] A study of the environmental impacts of intelligent automated vehicle control at intersections via V2V and V2I communications
    Bento, Luis Conde
    Parafita, Ricardo
    Rakha, Hesham A.
    Nunes, Urbano J.
    [J]. JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 23 (01) : 41 - 59
  • [4] A novel sparse representation model for pedestrian abnormal trajectory understanding
    Chen, Zhijun
    Cai, Hao
    Zhang, Yishi
    Wu, Chaozhong
    Mu, Mengchao
    Li, Zhixiong
    Sotelo, Miguel Angel
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2019, 138
  • [5] Understanding Individualization Driving States via Latent Dirichlet Allocation Model
    Chen, Zhijun
    Zhang, Yishi
    Wu, Chaozhong
    Ran, Bin
    [J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2019, 11 (02) : 41 - 53
  • [6] Dianati M, 2017, P 12 ITS EUR C, P1
  • [7] Angle of Arrival-Based Cooperative Positioning for Smart Vehicles
    Fascista, Alessio
    Ciccarese, Giovanni
    Coluccia, Angelo
    Ricci, Giuseppe
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 19 (09) : 2880 - 2892
  • [8] Hoang GM, 2017, IEEE INT VEH SYM, P1372, DOI 10.1109/IVS.2017.7995902
  • [9] Extrinsic calibration of a 3D LIDAR and a camera using a trihedron
    Gong, Xiaojin
    Lin, Ying
    Liu, Jilin
    [J]. OPTICS AND LASERS IN ENGINEERING, 2013, 51 (04) : 394 - 401
  • [10] Gulati D, 2018, 2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P2225, DOI 10.23919/ICIF.2018.8455268