基于Seq2Seq的船舶轨迹变步长预测方法研究

A Variable-Step-Size Prediction Method of Vessel Trajectory Based on Seq2Seq

针对船舶轨迹的历史轨迹相似性低、预测精度不高等问题,基于序列到序列(Seq2Seq)模型提出一种轨迹预测算法——vSeq2Seq。首先,用一阶差分法处理AIS数据,降低时间依赖性,减弱通信延迟产生的干扰,突出船只运动规律;然后,采用滑窗法处理数据,构建模型数据集,通过Seq2Seq模型进行可变步长的轨迹预测。实验结果证明,vSeq2Seq算法能够从船只轨迹中提取出轨迹变化特征,针对船只不同运动状态改变预测步长、灵活地进行预测,对比传统LSTM模型和GRU模型,预测精度有显著提升。

To solve the problems of low similarity of historical trajectories and low prediction accuracy in vessel trajectory prediction,a trajectory prediction algorithm vSeq2Seq based on Sequence-to-Sequence(Seq2Seq)model is proposed.Firstly,the first-order differential method is used to process the AIS data,so as to reduce time dependence,weaken the impact of communication delays,and highlight the rules of vessel movement.Then,the sliding window method is used to process the data,a data set of the model is built,and the trajectory prediction with variable step sizes is conducted through Seq2Seq model.The experimental results show that the vSeq2Seq algorithm can extract the features from varied trajectories of the vessels,changing the prediction step size in different motion states of vessels to predict flexibly.Compared with that of the traditional LSTM and GRU models,the prediction accuracy is significantly improved.