Ship Trajectory Prediction Method of Gated Recurrent Unit Based on Spatial-temporal Attention Mechanism
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摘要: 船舶轨迹预测的精度关系到船舶智能航行水平。针对门控循环单元（gated recurrent unit, GRU）提取船舶时空信息数据能力不足，导致轨迹预测精度不佳的问题，研究了基于时空注意力机制的GRU船舶轨迹预测方法（spatial-temporal attention mechanism-gated recurrent unit, STA-GRU）。将传统GRU中的激活函数改进为加权激活函数组，以保留更完整的船舶轨迹数据；引入空间注意力机制模块提取船舶空间位置信息的特征，以船舶经纬度及相对经纬度数据作为输入序列，计算对应的空间权重注意力因子，获得空间特征向量；再引入时间注意力机制模块挖掘观测时段内历史轨迹特征向量的时空依赖性，以历史轨迹数据中的航速、航向拼接空间特征向量作为输入序列，计算时空权重注意力因子，将获得的时空特征向量作为STA-GRU模型的训练数据集，用于船舶轨迹预测。采用青岛港AIS数据开展实验验证，以输入时长20 min，采样频率2 min作为输入条件，构建船舶航行轨迹数据集，结果表明：对比LSTM、AT-GRU、Bi-GRU算法，STA-GRU模型不仅在训练过程中收敛速度更快，而且在均方根误差、平均绝对误差、最终位移误差指标中均有大幅下降，预测轨迹时各项指标平均降低了50.2%，38.7%，48.3%；预测经度时各项指标平均降低了43.8%，50.5%，49.5%；预测纬度时各项指标平均降低了52.4%，48.4%，50.5%。因此，所提船舶轨迹预测STA-GRU模型的精度有显著提升，并能满足轨迹预测的实时性需求。Abstract: The accuracy of ship trajectory prediction is crucial for the intelligence level of ship's navigation. Addressing the insufficient capability of the gated recurrent unit (GRU) in capturing spatial-temporal information from ship data, which leads to poor accuracy in trajectory prediction, a method of GRU ship trajectory prediction based on spatial-temporal attention mechanism (STA-GRU) is investigated. The traditional activation function in GRU is improved by a weighted activation function set to retain more comprehensive ship trajectory data. A spatial attention mechanism module is introduced to extract spatial location features of ships using latitude, longitude, relative latitude, and relative longitude as input sequences. This module computes spatial-temporal weight attention factors to obtain spatial feature vectors. The resulting vectors serve as the training dataset for the STA-GRU model used for ship trajectory prediction. Experimental validation is conducted using AIS data from Qingdao Port, with an input duration of 20 minutes and a sampling frequency of 2 min. A ship navigation trajectory dataset is constructed under these conditions. Results indicate that, compared to LSTM, AT-GRU, and Bi-GRU algorithms, the STA-GRU model not only converges faster during training but also significantly reduces the root mean square error, mean absolute error, and final displacement error. The average reductions of the aforementioned indexes for trajectory prediction are 50.2%, 38.7%, and 48.3%, respectively. For longitude prediction, the average reductions are 43.8%, 50.5%, and 49.5%, respectively. For latitude prediction, the average reductions are 52.4%, 48.4%, and 50.5%, respectively. Therefore, the proposed STA-GRU model exhibits significantly improved accuracy in ship trajectory prediction and meets the real-time requirements for trajectory prediction.
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图 2 改进激活函数GRU结构原理图
Figure 2. Improved activation function GRU structure schematic diagram
图 7 STA-GRU与不同模型轨迹预测结果及对比
Figure 7. Comparison between STA-GRU and other models trajectory prediction
图 8 STA-GRU与不同模型轨迹预测误差箱型图
Figure 8. Box plots of STA-GRU with other models trajectory prediction error
表 1 STA-GRU与不同模型预测误差对比
Table 1. Comparison between STA-GRU and other models prediction error
预测指标 模型 RMSE MAE FDE 轨迹 LSTM 0.185 6 0.214 8 0.085 1×10-2 AT-GRU 0.116 9 0.105 2 0.051 9×10-2 Bi-GRU 0.128 2 0.119 4 0.068 0×10-2 STA-GRU 0.068 7 0.081 6 0.034 0×10-2 经度 LSTM 0.139 5×10-2 1.099 9×10-3 0.107 6×10-2 AT-GRU 0.096 5×10-2 0.836 5×10-3 0.074 7×10-2 Bi-GRU 0.107 4×10-2 0.963 7×10-3 0.085 6×10-2 STA-GRU 0.062 8×10-2 0.473 0×10-3 0.044 1×10-2 纬度 LSTM 0.118 5×10-2 0.903 3×10-3 0.391 7×10-3 AT-GRU 0.074 6×10-2 0.474 7×10-3 0.237 3×10-3 Bi-GRU 0.079 1×10-2 0.541 7×10-3 0.256 1×10-3 STA-GRU 0.041 4×10-2 0.305 3×10-3 0.139 1×10-3 
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表 2 模型实时性分析
Table 2. Analysis of model real-time
模型 运行时间/s LSTM 2.356 2 GRU 1.578 6 Bi-GRU 2.102 5 AT-GRU 1.896 9 STA-GRU 1.648 3
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