现代电子技术2024，Vol.47Issue(11)：175-180,6.DOI:10.16652/j.issn.1004-373x.2024.11.029
SW-SAN:基于Seq2Seq结合注意力机制与滑动窗口的车辆轨迹预测模型
SW-SAN:Vehicle trajectory prediction model based on Seq2Seq combined with attention mechanism and sliding window
摘要
Abstract
A vehicle trajectory prediction model(SW-SAN)based on Seq2Seq combined with attention mechanism and sliding window is proposed to address the issue of poor accuracy in predicting vehicle trajectories over a long period of time(4～5s).The sliding window method is used to update the historical trajectory state set,and an encoder is used to encode the historical trajectory data of the vehicle(the object)to obtain the historical trajectory feature vector.The attention mechanism is used to calculate the relevant scores,time attention weighting factors,and historical time related feature vectors for each moment in the historical time.The decoder is used to take the historical time correlation feature vector as the input,and the decoding layer is cycled for multiple times to output the future predicted trajectory of the vehicle(the object).The experimental results show that the root mean square error(RMSE)of the SW-SAN model in predicting trajectories at 4s and 5s are 1.99 m and 1.94 m,respectively,so the prediction error of SW-SAN model is relatively lower over a longer period of time(4～5s),and its performance in vehicle trajectory prediction is better.关键词
交通工程/轨迹预测/深度学习/编-解码器结构/注意力机制/滑动窗口Key words
traffic engineering/trajectory prediction/deep learning/encoder-decoder structure/attention mechanism/sliding window分类
信息技术与安全科学引用本文复制引用
朱云鹤,刘明剑,祝朗千,李沐阳..SW-SAN:基于Seq2Seq结合注意力机制与滑动窗口的车辆轨迹预测模型[J].现代电子技术,2024,47(11):175-180,6.基金项目
国家自然科学基金资助项目(61802046) （61802046）
辽宁省教育厅科学研究经费资助项目(QL202015) （QL202015）
