吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (5): 1474-1480.doi: 10.13229/j.cnki.jdxbgxb.20210904

• 通信与控制工程 • 上一篇    

基于注意力与深度交互的周车多模态行为轨迹预测

田彦涛1,2(),黄兴1,卢辉遒1,王凯歌1,许富强1   

  1. 1.吉林大学 通信工程学院，长春 130022
    2.吉林大学 工程仿生教育部重点实验室，长春 130022
  • 收稿日期:2021-09-09 出版日期:2023-05-01 发布日期:2023-05-25
  • 作者简介:田彦涛（1958-），男，教授，博士.研究方向：复杂系统建模，优化与控制.E-mail：tianyt@jlu.edu.cn
  • 基金资助:
    国家自然科学基金区域创新发展联合基金项目(U19A2069)

Multi⁃mode behavior trajectory prediction of surrounding vehicle based on attention and depth interaction

Yan-tao TIAN1,2(),Xing HUANG1,Hui-qiu LU1,Kai-ge WANG1,Fu-qiang XU1   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130022，China
    2.Key Laboratory of Bionic Engineering，Ministry of Education，Jilin University，Changchun 130022，China
  • Received:2021-09-09 Online:2023-05-01 Published:2023-05-25

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摘要：

设计了一种车辆深度交互编码并结合基于注意力机制的解码器模型，该模型同时输出车辆多模态行为预测结果和未来轨迹预测分布。使用公开的NGSIM US-101和I-80数据集评估所提出的模型，并且对模型多模态行为机动预测进行了定性分析。结果表明：该模型具有较好的均方根误差值（RMSE），在提升了计算效率的基础上获得了更高的轨迹预测精度。

关键词: 车辆工程, 轨迹预测, 多模态预测, 注意力机制, 门控循环单元

Abstract:

A vehicle deep-interaction coding model combined with a decoder based on the attention-mechanism to solve this problem was presented in this work. The model's multi-modal behavior predictions and trajectory predictions were output. The proposed model is evaluated by the public NGSIM US-101 and I-80 data sets. The results show that the model has a better root mean square error and achieves higher trajectory prediction accuracy while improving computational efficiency. This paper also shows a qualitative analysis of the prediction of multi-modal behavior maneuvering.

Key words: vehicle engineering, trajectory prediction, multimodal prediction, attention mechanism, gated recurrent unit

中图分类号: 

  • U495

图1

参考坐标系与驾驶行为集"

图2

基于注意力机制的深度交互GRU模型"

图3

GRU的单元结构"

图4

NGSIM数据集I-80路段"

表1

不同模型的RMSE值 (m)"

模 型预测时间/s
12345
V-LSTM0.681.652.914.466.27
C-VGMM+VIM0.661.562.754.246.68
CS-LSTM0.651.342.403.564.54
DCS-LSTM0.581.262.213.464.43
ADI-DCS-GRU0.551.242.103.124.23
ADI-DCS-GRU（M）0.571.262.153.234.35

图5

负对数似然误差"

图6

换道过程的预测分析"

图7

周围车辆对多模态行为轨迹预测的影响"

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