Graph Representation Method for Pedestrian Intention Recognition of Intelligent Vehicle

被引：0
|
作者：
Chao L. [1 ]
Cui G. [1 ]
Meng X. [1 ]
Lu J. [2 ]
Xu Y. [2 ]
Gong J. [1 ]
机构：
[1] School of Mechanical Engineering, Beijing Institute of Technology, Beijing
[2] SAIC Motor Technical Center, Shanghai
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2022年 / 42卷 / 07期
关键词：
graph representation learning; machine learning; pedestrian intention recognition;
D O I：
10.15918/j.tbit1001-0645.2021.330
中图分类号：
学科分类号：
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. © 2022 Beijing Institute of Technology. All rights reserved.
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页码：688 / 695
页数：7
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