Pedestrian trajectory prediction method based on group perception

WANG Ruyan; ZHOU Yudie; WU Dapeng; DUAN Ang; CUI Yaping; HE Peng

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

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Pedestrian trajectory prediction method based on group perception

Papers | 更新时间：2025-01-14

- Pedestrian trajectory prediction method based on group perception
- Journal on Communications Vol. 45, Issue 12, Pages: 44-56(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61801065;62271096;61871062;U20A20157;62061007;U24A20211);The Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000603;KJQN202300621);The Natural Science Foundation of Chongqing(CSTB2024NSCQ-LZX0124;CSTB2022NSCQ-MSX0468;cstc2020jcyjzdxmX0024;CSTB2023NSCQ-LZX0134);The University Innovation Research Group of Chongqing(CXQT20017);The Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04);Sichuan Science and Technology Program(2024YFHZ0093)
- DOI：10.11959/j.issn.1000-436x.2024224
  CLC： TP18
- Received：10 July 2024，
  
  Revised：2024-11-05，
  
  Published：25 December 2024
- 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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