基于车辆视角数据的行人轨迹预测与风险等级评定

Zheyu Zhang, Lü Chao*, Jinghang Li, Guangming Xiong, Shaobin Wu, Jianwei Gong

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

The commonly used pedestrian trajectory and risk prediction model based on roadbed-perspective data often cannot avoid complex modeling calculation and manual judgment. For succinctly and effectively predicting pedestrian trajectory and evaluating risk grade, a pedestrian trajectory and risk grade prediction model is created based on vehicle-perspective pedestrian data in this paper. The acquisition of vehicle-perspective pedestrian data, the prediction of pedestrian trajectory based on long-short term memory neural network and the assessment of risk grade based on clustering analysis - support vector machine method are successively conducted. The results of experiments show that the data-driven model built based on vehicle-perspective pedestrian data can capture the movement tendency and interaction characteristics of pedestrian and vehicle and is capable of predicting pedestrian trajectory and assessing risk grade.

投稿的翻译标题Pedestrian Trajectory Prediction and Risk Grade Assessment Based on Vehicle-Perspective Pedestrian Data
源语言繁体中文
页(从-至)675-683
页数9
期刊Qiche Gongcheng/Automotive Engineering
44
5
DOI
出版状态已出版 - 25 5月 2022
已对外发布

关键词

  • Clustering analysis
  • Long short term memory neural network
  • Pedestrian risk grade assessment
  • Pedestrian trajectory prediction
  • Support vector machine
  • Vehicle-perspective pedestrian data

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