Volume 42 Issue 7
Aug 2022
Turn off MathJax
Article Contents
JOURNAL OF MECHANICAL ENGINEERING  > 2022  >  42(7): 688-695.
LÜ Chao, CUI Gege, MENG Xianghao, LU Junyan, XU Youzhi, GONG Jianwei. Graph Representation Method for Pedestrian Intention Recognition of Intelligent Vehicle[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 42(7): 688-695. doi: 10.15918/j.tbit1001-0645.2021.330
Citation: LÜ Chao, CUI Gege, MENG Xianghao, LU Junyan, XU Youzhi, GONG Jianwei. Graph Representation Method for Pedestrian Intention Recognition of Intelligent Vehicle[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 42(7): 688-695. doi: 10.15918/j.tbit1001-0645.2021.330
shu

Graph Representation Method for Pedestrian Intention Recognition of Intelligent Vehicle

doi: 10.15918/j.tbit1001-0645.2021.330
  • 1.

    School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

  • 2.

    SAIC Motor Technical Center, Shanghai 201804, China

  • Received Date: 30 Nov 2021
  • Accepted Date: 22 Mar 2022
  • Issue Publish Date: 17 Aug 2022
    Abstract
  • The problem of pedestrian-vehicle conflict in intelligent driving scenes is closely related to pedestrian crossing behavior. In order to enable advanced driving assistance system (ADAS) to have the function of identifying pedestrian crossing intentions and raising advanced warning of pedestrian-vehicle collision events, a pedestrian crossing intention recognition framework based on graph representation learning (GRL) method is proposed. It uses open source tools to generate pedestrian skeleton information. Then it establishes a graph model to represent the characteristics of pedestrian action sequence by taking the skeleton key points of each frame of pedestrian within a sequence as nodes, as well as taking the natural connections, the topological correlations and time-domain relationships between skeleton joints as edges. Taking the graph structure data as the input, the pedestrian crossing intention recognition model is trained based on support vector machine (SVM). The results show that the classification accuracy of pedestrian crossing intention can reach 90.29%. The proposed method can effectively identify the pedestrian crossing intention, which is of great significance to improve the safety of intelligent vehicle decision-making.

     

    • FullText(HTML)
  • loading
    • References(9)
  • [1]
    MUHAMMAD I P, WIWIK A, HANUGRA A S, et al. Early warning pedestrian crossing intention from its head gesture using head pose estimation[C]//International Seminar on Intelligent Technology and Its Applications (ISITIA). Surabaya, Indonesia. IEEE, 2021: 402 − 407.
    [2]
    RAÚL Q M, IGNACIO P A, DAVID F, et al. Pedestrian path, pose, and intention prediction through Gaussian process dynamical models and pedestrian activity recognition[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(5):1803 − 1814.
    [3]
    FANG Z, VAZQUEZ D, LOPEZ A M. On-board detection of pedestrian intentions[J]. SENSORS. 2017, 17(10): 2193
    [4]
    FRANCESCO P, RAJARATHNAM B, MARIA J P, et al. FuSSI-net: fusion of Spatio-temporal skeletons for intention prediction network[C]//54th Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, CA, USA: IEEE, 2020: 68 − 72.
    [5]
    PABLO R G C, MING Y, QIAN Y Q, et al. Pedestrian Graph: Pedestrian crossing prediction based on 2D pose estimation and graph convolutional networks[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). Auckland, New Zealand: IEEE, 2019: 2000 − 2005.
    [6]
    SVEN K, LORENZO B, ALEXANDRE A. PifPaf: composite fields for human pose estimation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019: 11969 − 11978.
    [7]
    NEUMANN M, GARNETT R, BAUCKHAGE C, et al. Propagation kernels: efficient graph kernels from propagated information[J]. Machine Learning, 2016, 102(2): 209 − 245.
    [8]
    KONDOR R, PAN H. The multiscale laplacian graph kernel[C]//30th Conference on Neural Information Processing Systems (NIPS).Barcelona,Spain:Curran Associates,Inc., 2016: 2990 − 2998.
    [9]
    RASOULI A, KOTSERUBA I, KUNIC T, et al. PIE: a large-scale dataset and models for pedestrian intention estimation and trajectory prediction[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE, 2019: 6261 − 6270.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    Figures(5)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views(167) PDF downloads(0) Cited by()
    Proportional views
    Related

    Copyright © 2009《 石油钻探技术 》 All Rights Reserved.

    Address：North-Star Times Tower, No.8 Beichendong Road, Chaoyang District, Beijing, China China Pos：100101

    Tel：010-84988317，84988356，84988357 Fax：021-64253812 Email：syzt@vip.163.com

    Beijing public network security equipment 11010502036328　Beijing ICP backup 17033152

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return