基于上下文自编码的船舶行为语义表征

马杰; 何沐蓉; 贾承丰; 李文楷; 张煜

doi:10.19818/j.cnki.1671-1637.2022.04.026

基于上下文自编码的船舶行为语义表征

doi: 10.19818/j.cnki.1671-1637.2022.04.026

马杰^{1, 2, 3,},
何沐蓉¹,
贾承丰¹,
李文楷¹,
张煜^{2, 3, 4}

1.
武汉理工大学航运学院，湖北武汉 430063
2.
武汉理工大学内河航运技术湖北省重点实验室，湖北武汉 430063
3.
武汉理工大学国家水运安全工程技术研究中心，湖北武汉 430063
4.
武汉理工大学交通与物流工程学院，湖北武汉 430063

基金项目:

国家重点研发计划 2021YFB3901504

国家自然科学基金项目 52271366

国家自然科学基金项目 51679182

详细信息

作者简介:
马杰(1978-)，男，湖北武汉人，武汉理工大学教授，工学博士，从事信息融合感知与船舶智能导航研究

中图分类号: U675
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- 被引次数: 0
出版历程
- 收稿日期: 2022-02-09
- 网络出版日期: 2022-10-08
- 刊出日期: 2022-08-25

Semantic representation of ship behavior based on context autoencoder

MA Jie^{1, 2, 3
,},
HE Mu-rong¹,
JIA Cheng-feng¹,
LI Wen-kai¹,
ZHANG Yu^{2, 3, 4}

1.
School of Navigation, Wuhan University of Technology, Wuhan 430063, Hubei, China
2.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, Hubei, China
3.
National Engineering Research Center for Water Transportation Safety, Wuhan University of Technology, Wuhan 430063, Hubei, China
4.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, Hubei, China

Funds:

National Key Research and Development Program of China 2021YFB3901504

National Natural Science Foundation of China 52271366

National Natural Science Foundation of China 51679182

More Information

Author Bio:
MA Jie(1978-), male, professor, PhD, majie@whut.edu.cn

摘要

摘要: 考虑船舶行为的时序相关性，提出了一种基于上下文自编码的船舶行为语义表征(SRCAE)模型；提取船舶经度、纬度、航速、航向等行为特征参量，建立了行为特征序列；借助连续词袋模型将行为特征序列划分为中心船舶行为和上下文船舶行为，利用深度自编码网络构建了船舶上下文行为的语义表征模型，将得到的中心船舶行为编码作为表征向量输出，通过聚类算法构建船舶行为词典；选取长江口南槽交汇水域作为研究对象，利用船舶自动识别系统产生的数据对提出的模型和方法进行了验证。分析结果表明：所提出的SRCAE模型能有效表征船舶行为之间的上下文联系，与传统自编码器和长短期记忆网络自编码器等模型相比SRCAE模型具有更低的表征误差；分别采用k均值(k-Means)、高斯混合模型(GMM)与核k均值(Kernel k-Means)3种聚类算法提取船舶行为词典，与原始数据相比SRCAE模型产生的表征向量更易于区分不同船舶行为模式，其中k-Means效果最优，轮廓系数、卡林斯基-哈拉巴斯指数和戴维森堡丁指数指标分别达到了0.384、18.308、0.531，共产生转向加速、转向减速、直行加速、直行减速等30种复合行为，有效提取了不同行为模式下船舶行为词组合关系。
- 船舶自动识别系统 /
- 船舶行为 /
- 自编码器 /
- 连续词袋模型 /
- 语义表征
Abstract: Considering the temporal correlation of ship behavior, a semantic representation model based on the context autoencoder (SRCAE) was proposed for ship behavior. The behavioral feature parameters, such as the longitude, latitude, speed, as well as the course, were extracted to establish the behavioral feature sequence. The behavioral feature sequence was divided into the center ship behavior and context ship behavior via the continuous bag-of-words (CBOW) model. The deep autoencoder (AE) networks were utilized to construct the semantic representation model of context ship behavior, and the encoded center ship behavior obtained from the model was output as the representation vector. The clustering algorithm was employed to establish the ship behavior dictionary. The South Passage Intersection Water of the Yangtze Estuary was selected as the research object, and the data from the automatic identification system (AIS) for ships were employed for verification of the proposed model and method. Analysis results show that the context relationships between ship behaviors can be effectively represented by the proposed SRCAE model, and the representation error of the SRCAE model is lower than that of the traditional AE model and long short-term memory autoencoder (LSTMAE) model. Three clustering algorithms, namely, k-means, Gaussian mixture model (GMM), and kernel k-means, were used to extract the ship behavior dictionary. Compared with the original data, the representation vectors generated by the SRCAE model are easier to distinguish different ship behavior patterns, among which the effect of k-means is the best, and the Silhouette coefficient (SC), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI) of k-means reach 0.384, 18.308, and 0.531, respectively. A total of 30 types of composite behaviors are generated, such as steering acceleration, steering deceleration, straight-ahead acceleration, straight-ahead deceleration, and so on and the combination relationships of ship behavior words under different behavior patterns are effectively extracted.
- ship automatic identification system /
- ship behavior /
- autoencoder /
- continuous bag-of-words model /
- semantic representation

HTML全文

图 1 SOG语义转换

Figure 1. Semantic transformation of SOG

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图 2 SRCAE模型框架

Figure 2. Framework of SRCAE model

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图 3 中心船舶行为和上下文船舶行为提取

Figure 3. Extraction of center ship behavior and context ship behavior

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图 4 重构全局行为的行为特征参量误差(244010871)

Figure 4. Parameter errors of behavior characteristics for reconstructing global behavior (244010871)

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图 5 重构全局行为的行为特征参量误差(255805790)

Figure 5. Parameter errors of behavior characteristics for reconstructing global behavior (255805790)

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图 6 重构全局行为的行为特征参量误差(255805792)

Figure 6. Parameter errors of behavior characteristics for reconstructing global behavior (255805792)

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图 7 重构全局行为的行为特征参量误差(351453000)

Figure 7. Parameter errors of behavior characteristics for reconstructing global behavior (351453000)

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图 8 船舶行为表征算法的性能对比

Figure 8. Performance comparison of ship behavior representation algorithms

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图 9 三种聚类算法在原始轨迹数据与表征向量中聚类结果

Figure 9. Clustering results of three clustering algorithms on raw trajectory data and representation vector

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图 10 30类聚类中心在经纬度中的分布

Figure 10. Distribution of 30 cluster centers in latitude and longitude

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图 11 船舶行为场景实例

Figure 11. Examples of ship behavior

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表 1 三种聚类算法在表征向量中聚类效果对比

Table 1. Comparison of three clustering algorithms in representation vector

方法	指标
方法	SC	CHI	DBI
k-Means	0.384	18.308	0.531
GMM	0.368	18.665	1.675
核k-Means	0.376	17.874	0.792

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表 2 6类船舶行为词典统计描述

Table 2. Statistical descriptions of six kinds of ship behavior dictionary

类别	行为参数	均值	标准差	上分位数	中位数	下分位数
B₂	经度/(°)	122.561	0.022	122.545	122.549	122.581
	纬度/(°)	31.017	0.022	30.992	31.028	31.038
	对地航速/kn	10.429	2.462	8.300	10.600	12.200
	对地航向/(°)	271.868	115.087	278.730	287.880	357.760
B₃	经度/(°)	122.568	0.024	122.546	122.551	122.592
	纬度/(°)	31.011	0.027	30.989	31.018	31.038
	对地航速/kn	10.173	2.439	8.300	10.100	11.400
	对地航向/(°)	187.562	144.453	2.920	275.320	287.000
B₅	经度/(°)	122.531	0.032	122.543	122.545	122.548
	纬度/(°)	30.949	0.030	30.930	30.935	30.948
	对地航速/kn	7.664	3.477	6.400	7.300	8.270
	对地航向/(°)	292.245	120.646	282.490	356.000	358.180
B₁₄	经度/(°)	122.543	0.008	122.543	122.545	122.548
	纬度/(°)	30.968	0.012	30.958	30.966	30.974
	对地航速/kn	9.049	3.865	7.600	8.820	10.000
	对地航向/(°)	318.687	100.557	353.790	357.000	358.280
B₁₉	经度/(°)	122.524	0.017	122.512	122.520	122.543
	纬度/(°)	30.969	0.016	30.958	30.968	30.986
	对地航速/kn	9.173	3.626	7.950	8.840	9.800
	对地航向/(°)	145.350	97.907	103.840	172.680	180.620
B₂₈	经度/(°)	122.472	0.015	122.462	122.469	122.479
	纬度/(°)	31.004	0.014	30.997	31.010	31.013
	对地航速/kn	10.387	2.686	8.500	9.700	12.800
	对地航向/(°)	231.500	78.093	115.530	279.830	282.250

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