计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (12): 157-165.doi: 10.11896/jsjkx.231100145

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于时空图注意力卷积神经网络的车辆轨迹预测

袁静, 夏英   

  1. 重庆邮电大学计算机科学与技术学院 重庆 400065
  • 收稿日期:2023-11-22 修回日期:2024-02-06 出版日期:2024-12-15 发布日期:2024-12-10
  • 通讯作者: 夏英(xiaying@cqupt.edu.cn)
  • 作者简介:(S210201129@stu.cqupt.edu.cn)
  • 基金资助:
    国家自然科学基金(41971365);重庆市教委重点合作项目(HZ2021008)

Vehicle Trajectory Prediction Based on Spatial-Temporal Graph Attention Convolutional Network

YUAN Jing, XIA Ying   

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2023-11-22 Revised:2024-02-06 Online:2024-12-15 Published:2024-12-10
  • About author:YUAN Jing,born in 1997,postgraduate.His main research interests include deep learning and trajectory prediction.
    XIA Ying,born in 1972,Ph.D,professor,Ph.D supervisor,is a senior member of CCF(No.10248S).Her main research interests include spatio-temporal big data and cross-media retrieval.
  • Supported by:
    National Natural Science Foundation of China(41971365) and Chongqing Municipal Education Commission Cooperation Projects(HZ2021008).

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摘要： 车辆轨迹预测是交通管理、智能汽车和自动驾驶等领域的一项关键技术。准确预测车辆轨迹,有利于汽车安全行驶。城市交通场景中,车辆轨迹数据的时空特征复杂多变。为充分获取数据中的动态时空相关性,提高轨迹预测精度,同时降低模型复杂度,提出了时空图注意力卷积神经网络模型(Spatial-Temporal Graph Attention Convolutional Network,STGACN)。该模型首先通过轨迹信息嵌入模块对车辆历史轨迹数据进行时空图转换,然后通过时空卷积块及其堆叠完成轨迹数据的时序特征和空间特征的提取与融合,最终由门控递归单元完成编码与解码工作,得到预测轨迹。模型采用由膨胀因果卷积和门控单元组成的门控卷积网络提取时序特征,避免了循环神经网络带来的冗余迭代,使得模型参数更少,轨迹预测推理速度更快;时空卷积块组的时空特征融合工作使模型关注到更丰富的场景特征,提高了预测精度。在真实轨迹数据集Argoverse和NGSIM上进行实验,结果表明STGACN模型与基线模型相比,具有更高的预测精度和效率。

关键词: 车辆轨迹预测, 时空相关性, 时空图, 图卷积网络, 注意力机制

Abstract: Vehicle trajectory prediction is a crucial technology in fields such as traffic management,intelligent-car,and autonomous driving.Accurately predicting vehicle trajectories contributes to safe driving.In urban traffic scenarios,the spatial- temporal features of vehicle trajectory data are complex and variable.To fully capture the dynamic spatial-temporal correlations in the data,enhance trajectory prediction accuracy,and simultaneously reduce model complexity,this paper proposes a spatial- temporal graph attention convolutional network(STGACN).It utilizes a trajectory information embedding module to transform historical vehicle trajectory data into spatial-temporal graphs.Subsequently,it extracts and combines temporal and spatial features of trajectory data through stacked spatial-temporal convolution blocks.Finally,encoding and decoding are performed by gated recurrent units to obtain the predicted trajectory.The model employs a gated convolutional network composed of dilated causal convolutions and gating units to extract temporal features,avoiding the redundant iterations introduced by recurrent neural network.The fusion of spatial- temporal features in the spatial-temporal convolution blocks group enables the model to focus on richer scene features.This results in a model with fewer parameters,faster trajectory prediction inference speed,and improved prediction accuracy.Experiments are conducted on real trajectory datasets,including Argoverse and NGSIM,and the results demonstrate that the proposed STGACN model exhibits higher prediction accuracy and efficiency than the compared baseline models.

Key words: Vehicle trajectory prediction, Spatial-temporal correlation, Spatial-temporal graph, Graph convolutional network, Attention mechanism

中图分类号: 

  • TP391
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