Trajectory Prediction and Intention Identification of Ships in Confluence Waters
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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。Abstract: A data-driven method is proposed for predicting ship trajectory and identifying sailing intention for typical confluence waters. A CNN+LSTM combined neural network is designed by learning the historical trajectories of the ships in the confluence waters. Using CNN+LSTM as an encoder, the spatio-temporal characteristics of the navigable environment and ship sailing are extracted. The decoder, which is composed of the LSTM and full connection layer, synchronously outputs the trajectory sequence and route selection of the ship in the future period. Moreover, the Dropout layer is introduced to describe the prediction uncertainty of the proposed model. Take the same trajectory sequence as the input, multiple groups of similar prediction results are obtained by randomly disabling several neural units of the CNN+LSTM network. Based on the statistical mean and variance of the prediction results, the prediction uncertainty of ship trajectory can be estimated. A dataset is created based on the open AIS data of confluence water in the coast of the United States. The input conditions are as follows: the input time is 60 min, the sam-pling frequency is 3 min, and the dropout parameter is 0.5. The results of the proposed model show that the error of trajectory prediction is 3.946 n mile for the next 60 min. The recognition accuracy of sailing intention is 87%. And the coverage rate of uncertainty estimation is 85.7%. Compared with other LSTM-based prediction methods, the trajectory prediction error of the proposed model is reduced by 31.6% when the ship's maneuverability changes. Furthermore, the proposed CNN+LSTM model has the ability of identifying ships' sailing intentions and estimating the prediction uncertainties, which is conducive to the development of intelligent navigation and intelligent maritime supervision technology.
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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 
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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
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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
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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值 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
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