北京航空航天大学学报2026，Vol.52Issue(1)：15-27,13.DOI:10.13700/j.bh.1001-5965.2023.0717
一种融合MSC和时空双重注意力的TCN航迹预测方法
A TCN trajectory prediction method fusing MSC and spatio-temporal dual attention
摘要
Abstract
Aiming at the problem that existing trajectory prediction models are difficult to effectively extract multi-scale spatio-temporal features,which leads to limited prediction accuracy,a new method MDAT-Net for trajectory prediction based on a temporal convolutional network(TCN)fused with a multi-scale convolutional(MSC)network and spatio-temporal dual attention(STDA)is proposed.The MDAT-Net model contains the MSAT,MTAT trajectory prediction module and voting module.First,in the prediction module,a multi-scale convolution architecture is built using different scale convolution kernels to better extract spatio-temporal features at various scales and solve the fixed kernel size issue in the traditional temporal convolutional network.Secondly,in order to dynamically mine the potential correlation between hidden features and target features,spatial attention mechanism and temporal attention mechanism are introduced to adaptively focus on important information and skip secondary information.Finally,the voting module decides which model to apply for each dimension prediction,allowing the benefits of both prediction models to be combined and high-precision trajectory prediction to be achieved.The experimental results show that the MDAT-Net model can improve the root mean square error(RMSE)up to 83.33%and the mean absolute error(MAE)up to 85.85%with high accuracy and robustness.关键词
航迹预测/多尺度卷积/时间卷积网络/注意力机制/时空特征Key words
trajectory prediction/multi-scale convolution/temporal convolutional network/attention mecha-nism/spatio-temporal features分类
航空航天引用本文复制引用
焦卫东,杨蓓..一种融合MSC和时空双重注意力的TCN航迹预测方法[J].北京航空航天大学学报,2026,52(1):15-27,13.基金项目
国家重点研发计划(2020YFB1600101) National Key Research and Development Program of China(2020YFB1600101) （2020YFB1600101）
