TCN-SA: A Social Attention Network Based on Temporal Convolutional Network for Vehicle Trajectory Prediction

被引:0
|
作者
Li, Qin [1 ]
Ou, Bingguang [1 ]
Liang, Yifa [1 ]
Wang, Yong [2 ]
Yang, Xuan [1 ]
Li, Linchao [3 ]
机构
[1] Guangxi Univ, Sch Mech Engn, Nanning 530004, Peoples R China
[2] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
[3] Shenzhen Univ, Urban Smart Transportat Safety Maintenance, Shenzhen 518060, Peoples R China
关键词
MODEL; FRAMEWORK; BEHAVIOR;
D O I
10.1155/2023/1286977
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Vehicle trajectory prediction can provide important support for intelligent transportation systems in areas such as autonomous driving, traffic control, and traffic flow optimization. Predicting vehicle trajectories is an extremely challenging task that not only depends on the vehicle's historical trajectory but also on the dynamic and complex social-temporal relationships of the surrounding traffic network. The trajectory of the target vehicle is influenced by surrounding vehicles. However, existing methods have shortcomings in considering both time dependency and interactive dependency between vehicles or insufficient consideration of the impact of surrounding vehicles. To address this issue, we propose a hybrid deep learning model based on a temporal convolutional network (TCN) that considers local and global interactions between vehicles. Specifically, we use a social convolutional pooling layer to capture local interaction features between vehicles and a multihead self-attention layer to capture global interaction features between vehicles. Finally, we combine these two features using an encoder-decoder structure to predict vehicle trajectories. Through experiments on the Next-Generation Simulation (NGSIM) public dataset and ablation experiments, we validate the effectiveness of our model.
引用
收藏
页数:12
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