摘要
采用深度学习进行船舶轨迹序列预测对于智能航运具有重要意义。船舶自动识别系统(Automatic Identification System,AIS)蕴藏着大量船舶轨迹特征,基于AIS数据预测船舶轨迹是近年智能航运研究的热点之一。文中提出了一种基于改进Seq2Seq的短时AIS轨迹序列预测模型,该模型使用门控循环单元网络将历史时空序列编码为一个上下文向量,用以保留轨迹空间点间的时序关系,同时缓解梯度下降的问题。通过使用门控循环单元网络作为解码器来预测船舶轨迹的时空序列。实验采用了大规模真实船舶AIS数据,选取两类典型河段(重庆弯曲河段和武汉顺直河段)为实验区域,以评估和验证模型的有效性和适用性。实验证明,该模型能够有效提高短时轨迹序列预测的准确性和效率,为智能航船碰撞预警提供了一种有效可行的方法。
Using deep learning to predict the vessel trajectory is of great significance for the intelligent shipping.AIS(Automatic Identification System)data contain a huge amount of information about vessel trajectory features.The prediction of ship trajectories based on AIS data becomes one of the research hotspots in the intelligent shipping realm.In this paper,an improved sequence-to-sequence model using AIS data streams is proposed for the short-term vessel trajectory prediction.The proposed model utilizes a GRU network to encode the historical spatio-temporal sequence into a context vector,which not only preserves the sequential relationship among those trail points,but also is helpful for the alleviation of the gradient descent problem.Meanwhile,a GRU network is used as a decoder to output target trail points sequence.In this paper,a large scale of real AIS data are used in the experiments.The Chongqing section and the Wuhan section of the Yangzi River are selected as typical experimental areas,which is for the evaluation of the validity and applicability of the model.Experimental results show that the proposed model improves the accuracy and efficiency of short-term ship trajectory prediction.The proposed model provides an effective solution for the intelligent shipping warning in the future.
作者
游兰
韩雪薇
何正伟
肖丝雨
何渡
潘筱萌
YOU Lan;HAN Xue-wei;HE Zheng-wei;XIAO Si-yu;HE Du;PAN Xiao-meng(School of Computer Science&Information Engineering,Hubei University,Wuhan 430062,China;School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Hubei Key Laboratory of Inland Shipping Technology,Wuhan University of Technology,Wuhan 430063,China;National Engineering Research Center for Water Transportation Safely,Wuhan 430063,China;Hubei Engineering Research Center for Education Information,Wuhan 430071,China)
出处
《计算机科学》
CSCD
北大核心
2020年第9期169-174,共6页
Computer Science
基金
湖北省自然科学基金面上项目(2019CFB757)
国家水运安全工程技术研究中心开放基金(A2019011)
内河航运技术湖北省重点实验室基金(NHHY2017001)
湖北省教育厅科学研究计划重点项目(D20161001)
中央高校基本科研业务费专项资金(2019III050GX,2019III007GX)。
关键词
轨迹预测
序列到序列模型
循环神经网络
船舶自动识别系统
时空数据挖掘
Trajectory prediction
Sequence-to-sequence model
Recurrent neural network
Automatic identification system
Spatio-temporal data mining
