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交汇水域船舶轨迹预测与航行意图识别

王知昊 元海文 李维娜 肖长诗

王知昊, 元海文, 李维娜, 肖长诗. 交汇水域船舶轨迹预测与航行意图识别[J]. 交通信息与安全, 2022, 40(4): 101-109. doi: 10.3963/j.jssn.1674-4861.2022.04.011
引用本文: 王知昊, 元海文, 李维娜, 肖长诗. 交汇水域船舶轨迹预测与航行意图识别[J]. 交通信息与安全, 2022, 40(4): 101-109. doi: 10.3963/j.jssn.1674-4861.2022.04.011
WANG Zhihao, YUAN Haiwen, LI Weina, XIAO Changshi. Trajectory Prediction and Intention Identification of Ships in Confluence Waters[J]. Journal of Transport Information and Safety, 2022, 40(4): 101-109. doi: 10.3963/j.jssn.1674-4861.2022.04.011
Citation: WANG Zhihao, YUAN Haiwen, LI Weina, XIAO Changshi. Trajectory Prediction and Intention Identification of Ships in Confluence Waters[J]. Journal of Transport Information and Safety, 2022, 40(4): 101-109. doi: 10.3963/j.jssn.1674-4861.2022.04.011

交汇水域船舶轨迹预测与航行意图识别

doi: 10.3963/j.jssn.1674-4861.2022.04.011
基金项目: 

国家自然科学基金项目 52001235

中国博士后科学基金项目 2020M682504

详细信息
    作者简介:

    王知昊(1997—),硕士研究生. 研究方向:海事安全、信息导航. E-mail: 21903010049@stu.wit.edu.cn

    通讯作者:

    元海文(1988—),博士,副教授. 研究方向:交通信息工程及控制、信息导航与海事安全保障. E-mail: hw_yuan@whut.edu.cn

  • 中图分类号: U675.79

Trajectory Prediction and Intention Identification of Ships in Confluence Waters

  • 摘要: 针对典型水上交通场景交汇水域,研究了1种数据驱动的船舶轨迹预测与航行意图识别方法。设计CNN+LSTM组合神经网络,通过学习交汇水域船舶的历史轨迹,以CNN+LSTM网络为编码器提取其通航环境及船舶航行时空特征,LSTM与全连接层为解码器同步输出未来时段内船舶轨迹序列和航路选择,从而形成船舶轨迹与航行意图识别模型。同时,引入Dropout网络结构描述该模型的预测不确定性,采用随机关闭CNN+ LSTM核心网络部分神经单元的方式,以相同轨迹序列作为输入获取多组相近的预测结果,根据其统计均值与方差对船舶轨迹预测的不确定性进行量化。以美国沿海某交汇水域公开AIS数据为对象开展实验,创建了该交汇水域船舶航行轨迹数据集,以输入时长60 min,采样频率3 min作为输入条件,Dropout值取0.5,实验结果表明:所提方法对未来60 min时段内的轨迹预测误差为3.946 n mile,航行意图识别准确率达87%,不确定性估计覆盖率达85.7%。与LSTM预测方法相比,当船舶操纵性发生改变时,所提CNN+LSTM模型的轨迹预测误差降低了31.6%,而且兼具船舶航行意图识别及预测不确定性估计能力,有利于智能航行与海事监管技术发展。

     

  • 图  1  交汇水域船舶航行轨迹

    Figure  1.  Shiptrajectories in confluence waters

    图  2  基于CNN+LSTM组合网络的船舶轨迹预测与航行意图识别模型

    Figure  2.  Ship trajectory prediction and intention recognition model based on CNN+LSTM combined network

    图  3  船舶轨迹预测与航行意图识别方法流程

    Figure  3.  Theflow of the ship trajectory prediction and intention recognition method

    图  4  以前60 min时长为输入,预测未来60 min时段内的船舶轨迹

    Figure  4.  Predicting the ship trajectories of next 60 min with the input of previous 60 min

    图  5  LSTM+CNN、LSTM、运动预测方法对比

    Figure  5.  Comparison between LSTM+CNN、LSTM、motion-based predictions

    图  6  船舶轨迹预测不确定性估计(Dropout = 0.5)

    Figure  6.  Uncertainty estimation of ship trajectory prediction(Dropout = 0.5)

    表  1  不同采样频率输入条件下的轨迹预测误差

    Table  1.   Trajectory prediction errors using different sampling frequenciesas inputs

    采样频率/min t1时刻误差/n mile t2时刻误差/n mile t3时刻误差/n mile t4时刻误差/n mile t5时刻误差/n mile 预测误差/n mile
    1 0.463 1 0.759 8 1.129 3 1.595 3 2.196 5 6.640 5
    3 0.262 0 0.453 6 0.669 7 0.918 2 1.208 8 3.946 0
    6 0.539 7 0.863 3 1.232 9 1.604 7 2.244 3 7.034 0
    12 0.813 5 1.107 3 1.412 0 2.187 5 2.937 0 8.560 0
    下载: 导出CSV

    表  2  不同时长输入条件下的轨迹预测误差

    Table  2.   Trajectory prediction errors using different durations as inputs

    输入时长/min t1时刻误差/n mile t2时刻误差/n mile t3时刻误差/n mile t4时刻误差/n mile t5时刻误差/n mile 预测误差/n mile
    15 1.105 2 1.576 8 2.228 4 3.258 1 4.539 6 15.534
    30 0.813 6 1.328 4 1.933 2 2.638 8 3.481 2 12.978
    45 0.565 2 0.878 4 1.326 7 1.976 4 2.746 8 8.946
    60 0.262 0 0.453 6 0.669 7 0.918 2 1.208 8 3.946
    下载: 导出CSV

    表  3  交汇水域船舶航行意图识别混淆矩阵

    Table  3.   Confusion matrix of intention identification for ships in confluence waters

    实际 预测 总计
    直线航行 右转航行
    直线航行 49 8 57
    右转航行 5 38 43
    总计 54 36 100
    下载: 导出CSV

    表  4  不同Dropout值条件下的轨迹预测不确定性估计及覆盖率

    Table  4.   Uncertaintyestimation and coverage rate of trajectory prediction with different Dropout values

    Dropout t1时刻范围/n mile2 t2时刻范围/n mile2 t3时刻范围/n mile2 t4时刻范围/n mile2 t5时刻范围/n mile2 不确定性范围/n mile2 覆盖率/%
    0.3 0.390 4 0.5981 0.954 5 1.595 5 2.923 7 1.292 4 75.4
    0.4 0.598 8 0.809 9 1.430 2 3.203 9 4.632 8 2.755 1 80.1
    0.5 0.965 1 1.449 7 2.351 3 4.027 6 7.408 8 3.940 5 85.7
    0.6 1.800 1 2.517 7 3.633 0 6.205 9 9.390 3 5.069 4 79.6
    0.7 2.300 4 3.322 3 4.878 9 7.029 5 10.762 3 6.658 7 71.7
    下载: 导出CSV
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  • 收稿日期:  2022-02-24
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