群组感知的行人轨迹预测方法研究

王汝言; 周玉蝶; 吴大鹏; 段昂; 崔亚平; 何鹏

doi:10.11959/j.issn.1000-436x.2024224

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群组感知的行人轨迹预测方法研究

学术论文 | 更新时间：2025-01-14

- 群组感知的行人轨迹预测方法研究
- Pedestrian trajectory prediction method based on group perception
- 通信学报 2024年45卷第12期页码：44-56
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.先进网络与智能互联技术重庆市高校重点实验，重庆 400065
  3.泛在感知与互联重庆市重点实验室，重庆 400065
- 作者简介：
  
  [ "王汝言（1969- ），男，湖北浠水人，博士，重庆邮电大学教授、博士生导师，主要研究方向为泛在网络、多媒体信息处理等。" ]
  [ "周玉蝶（2000- ），女，四川宜宾人，重庆邮电大学硕士生，主要研究方向为自动驾驶、车联网等。" ]
  [ "吴大鹏（1979- ），男，黑龙江大庆人，博士，重庆邮电大学教授、博士生导师，主要研究方向为泛在网络、人工智能等。" ]
  [ "段昂（1991- ），男，重庆人，重庆邮电大学博士生，主要研究方向为车联网、自动驾驶、边缘计算等。" ]
  [ "崔亚平（1986- ），男，河南新乡人，博士，重庆邮电大学副教授，主要研究方向为车联网、目标检测和行为识别等。" ]
  [ "何鹏（1990- ），男，重庆人，博士，重庆邮电大学讲师，主要研究方向为计算机视觉、数据挖掘和分析等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61801065;62271096;61871062;U20A20157;62061007;U24A20211);重庆市教委科学技术研究基金资助项目(KJQN202000603;KJQN202300621);重庆市自然科学基金资助项目(CSTB2024NSCQ-LZX0124;CSTB2022NSCQ-MSX0468;cstc2020jcyjzdxmX0024;CSTB2023NSCQ-LZX0134);重庆市高校创新研究群体基金资助项目(CXQT20017);重邮信通青创团队支持计划基金资助项目(SCIE-QN-2022-04);四川省重点研发计划基金资助项目(2024YFHZ0093)
- DOI：10.11959/j.issn.1000-436x.2024224
  中图分类号： TP18
- 收稿：2024-07-10，
  
  修回：2024-11-05，
  
  纸质出版：2024-12-25
- 稿件说明：
移动端阅览
王汝言,周玉蝶,吴大鹏等.群组感知的行人轨迹预测方法研究[J].通信学报,2024,45(12):44-56.

WANG Ruyan,ZHOU Yudie,WU Dapeng,et al.Pedestrian trajectory prediction method based on group perception[J].Journal on Communications,2024,45(12):44-56.
王汝言,周玉蝶,吴大鹏等.群组感知的行人轨迹预测方法研究[J].通信学报,2024,45(12):44-56. DOI： 10.11959/j.issn.1000-436x.2024224.

WANG Ruyan,ZHOU Yudie,WU Dapeng,et al.Pedestrian trajectory prediction method based on group perception[J].Journal on Communications,2024,45(12):44-56. DOI： 10.11959/j.issn.1000-436x.2024224.

摘要

自动驾驶场景下，大多数方法没有对行人群体进行建模，这样会对道路交通的安全造成影响。因此，提出了一种针对群组感知的行人轨迹预测网络（GPCNet）。具体来说，在组内，从个体层面学习行人之间的交互，考虑不同行人的偏好问题。在组间，从群体层面学习行人组间的交互，使用社会力模型考虑行人轨迹的碰撞问题。仿真结果表明，与常用的轨迹预测方法相比，GPCNet在ETH和UCY数据集上的性能提高了约75.4%。

Abstract

Most methods do not model the pedestrian groups in autonomous driving

which will have an impact on road traffic safety. Therefore

a group perception pedestrian trajectory prediction network called GPCNet was proposed. Specifically

in intra-group

the interaction between pedestrian was learned at the individual level and the preference issue of different pedestrian was considered. In inter-group

the interaction between pedestrian groups was learned at the group level and the collision issue of pedestrian trajectory was considered using the social force model. Simulation results demonstrate that GPCNet improves the performance on the ETH and UCY datasets by 75.4% compared to the commonly used trajectory prediction methods.

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references

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