基于图模型和注意力机制的车辆轨迹预测方法

连静; 丁荣琪; 李琳辉; 王雪成; 周雅夫

doi:10.12382/bgxb.2022.0117

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基于图模型和注意力机制的车辆轨迹预测方法

更新时间：2025-08-18

- 基于图模型和注意力机制的车辆轨迹预测方法
- Vehicle Trajectory Prediction Method Based on Graph Models and Attention Mechanism
- 兵工学报 2023年44卷第7期页码：2162-2170
- 作者机构：
  
  1. 大连理工大学工业装备结构分析国家重点实验室, 辽宁大连 116024
  2. 大连理工大学汽车工程学院, 辽宁大连 116024
- 作者简介：
  
  *邮箱: lilinhui@dlut.edu.cn
- 基金信息：
  
  国家自然科学基金项目(52172382);国家自然科学基金项目(61976039);大连市科技创新基金项目(2021JJ12GX015);辽宁省科学技术计划项目(2022JH1/1040030);中央高校基本科研业务费专项基金项目(DUT22JC09)
- DOI：10.12382/bgxb.2022.0117
  中图分类号：
- 收稿：2022-03-02，
  
  网络出版：2023-08-07，
  
  纸质出版：2023-07-30
- 稿件说明：
移动端阅览
连静, 丁荣琪, 李琳辉, 等. 基于图模型和注意力机制的车辆轨迹预测方法[J]. 兵工学报, 2023,44(7):2162-2170.

Jing LIAN, Rongqi DING, Linhui LI, et al. Vehicle Trajectory Prediction Method Based on Graph Models and Attention Mechanism[J]. Acta Armamentarii, 2023, 44(7): 2162-2170.
连静, 丁荣琪, 李琳辉, 等. 基于图模型和注意力机制的车辆轨迹预测方法[J]. 兵工学报, 2023,44(7):2162-2170. DOI： 10.12382/bgxb.2022.0117.

Jing LIAN, Rongqi DING, Linhui LI, et al. Vehicle Trajectory Prediction Method Based on Graph Models and Attention Mechanism[J]. Acta Armamentarii, 2023, 44(7): 2162-2170. DOI： 10.12382/bgxb.2022.0117.

摘要

为提高结构化道路场景下车辆轨迹预测准确度

提出一种基于图模型和注意力机制的多模态轨迹预测方法(GA-MTP)。构建车道图和车交互图

实现道路环境特征、车辆运动特征和车辆间交互特征建模;通过堆叠的注意力模块完成环境-车辆特征融合

统一交通场景静态特征和动态特征;由两分支解码网络模块得出最终轨迹预测和相应概率。在Argoverse数据集进行模型训练和测试

并进行结果分析。实验结果表明

新方法在结构化交通场景的车辆轨迹预测中取得优异的效果

预测准确程度优于当前主流方法。

Abstract

In order to improve the accuracy of vehicle trajectory prediction in structured road scenes

a multimodal trajectory prediction method based on graph models and attention mechanism is proposed. For the purpose of modeling road environment characteristics

vehicle motion characteristics and characteristics of interaction between vehicles

the lane graph and vehicle interaction graph are constructed. Environment-vehicle feature fusion is completed by stacked attention modules

so as to realize the unification of static and dynamic features of traffic scenes. The final predicted trajectories and corresponding probabilities are obtained through a two-branch decoding module. The Argoverse dataset is used to train and validate the proposed method. The experimental results show that the proposed method achieves excellent performance of motion prediction in structured road scenes. The prediction accuracy is better than the current mainstream methods.

关键词

Keywords

references

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