基于图表示的智能车行人意图识别方法

Lü Chao*, Gege Cui, Xianghao Meng, Junyan Lu, Youzhi Xu, Jianwei Gong

*此作品的通讯作者

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摘要

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.

投稿的翻译标题Graph Representation Method for Pedestrian Intention Recognition of Intelligent Vehicle
源语言繁体中文
页(从-至)688-695
页数8
期刊Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
42
7
DOI
出版状态已出版 - 7月 2022

关键词

  • graph representation learning
  • machine learning
  • pedestrian intention recognition

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引用此

Chao, L., Cui, G., Meng, X., Lu, J., Xu, Y., & Gong, J. (2022). 基于图表示的智能车行人意图识别方法. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 42(7), 688-695. https://doi.org/10.15918/j.tbit1001-0645.2021.330