摘 要：针对传统的轨迹预测存在精度低和计算较复杂的问题，文章提出一种基于自动编码器（AE）和门控循环单元（GRU）模型的数据驱动方法，利用历史信息和各种轨迹属性预测轨迹位置。该方法将数据预处理层、AE 层和 GRU 层与定制的批处理过程融合在一起。该模型在真实轨迹数据集上训练。通过与现有预测方法进行比较，结果验证所提模型性能相比于RNN，AE-RNN，LSTM 和 GRU 有显著的提高。
关键词：轨迹预测；自动编码器；门控循环单元；数据驱动
DOI:10.19850/j.cnki.2096-4706.2021.02.036
中图分类号：TP183 文献标识码：A 文章编号：2096-4706（2021）02-0149-05
Research on Trajectory Prediction Based on AE and GRU Network
ZHANG Hao，LIU Daming
（School of Computer Science and Technology，Shanghai University of Electric Power，Shanghai 201303，China）
Abstract：Aiming at the problems of low precision and computational complexity in traditional trajectory prediction，a data driven method based on auto encoder（AE）and gated recurrent unit（GRU）model was proposed，which used historical information and various trajectory attributes to predict trajectory location. This method fuses the data preprocessing layer，AE layer and GRU layer with the custom batch process. The model is trained on real trajectory data sets. Compared with the existing prediction methods，the results show that the performance of the proposed model is significantly better than that of RNN，AE-RNN，LSTM and GRU.
Keywords：trajectory prediction；AE；GRU；data driven
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作者简介：张浩（1996—），男，汉族，安徽蚌埠人，硕士研究生在读，研究方向：轨迹预测，物联网技术；刘大明（1971—），男，汉族，上海人，副教授，博士，研究方向：物联网技术，嵌入式系统与设计，智能工业机器人等。