The prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e.g. route planning, collision warning, and traffic regulation. For maritime intelligence, Automatic Identification System (AIS) plays an important role because it recently has been made compulsory for large international commercial vessels and is able to provide nearly real-time information of the vessel. Therefore AIS data based vessel path prediction is a promising way in future maritime intelligence. However, real-world AIS data collected online are just highly irregular trajectory segments (AIS message sequences) from different types of vessels and geographical regions, with possibly very low data quality. So even there are some works studying how to build a path prediction model using historical AIS data, but still, it is a very challenging problem. In this paper, we propose a comprehensive framework to model massive historical AIS trajectory segments for accurate vessel path prediction. Experimental comparisons with existing popular methods are made to validate the proposed approach and results show that our approach could outperform the baseline methods by a wide margin.
XLAB Research（XLAB研究公司）；University of Ljubljana（卢布尔雅那大学）；Piraeus Port Authority（比雷埃夫斯港务局）
Automatic Identification System (AIS) data represents a rich source of information about maritime traffic and offers a great potential for data analytics and predictive modeling solutions, which can help optimizing logistic chains and to reduce environmental impacts. In this work, we address the main limitations of the validity of AIS navigational data fields, by proposing a machine learning-based data-driven methodology to detect and (to the possible extent) also correct erroneous data. Additionally, we propose a metric that can be used by vessel operators and ports to express numerically the…
Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
Institute for Big Data Analytics --- Dalhousie University, Halifax - NS -- Canada [.05cm]；Department of Computer Science --- Memorial University of Newfoundland, St. John's - NL -- Canada [.05cm]；Institute of Computer Science --- Polish Academy of Sciences, Warsaw -- Poland
WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory
School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea（工业与管理工程学院，韩国大学，首尔02841，大韩民国）；SeaVantage, Seoul 06120, Republic of Korea（SeaVantage，首尔06120，大韩民国）
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to as GeoTrackNet -- for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks and a contrario detection to detect abnormal events. The neural network provides a new means to capture complex and heterogeneous patterns in vessels' behaviours, while the \textit{a contrario} detector takes into account the fact tha…
AIS Data-Driven Maritime Monitoring Based on Transformer: A Comprehensive Review
School of Information Science and Technology, Dalian Maritime University（信息科学与技术学院，大连海事大学）；R&D Innovation Center, COSCO Shipping Technology Co., Ltd（中远海运科技有限公司研发创新中心）
CmIVTP: Cross-modal Interaction-based Vessel Trajectory Prediction for Maritime Intelligence
Department of Logistics and Maritime Studies, the Hong Kong Polytechnic University（物流及海运研究系，香港理工大学）；Research Centre for ESG Advancement (RCESGA), the Hong Kong Polytechnic University（ESG进步研究中心（RCESGA），香港理工大学）；School of Navigation, Wuhan University of Technology（航海学院，武汉理工大学）
Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting
organization= Institute for Big Data Analytics, Dalhousie University , addressline= Halifax -- NS, Canada；organization= Industrial Engineering Department, Dalhousie University, Halifax -- NS, Canada；organization= Department of Comp. Sci.；Media Tech., Linnaeus University , addressline= Vaxjo, Sweden；organization= Institute of Computer Science, Polish Academy of Sciences , addressline= Warsaw, Poland；organization= DHI Water \& Environment Inc. , addressline= Ottawa, Canada
Institute of Informatics & Telecommunications, NCSR Demokritos（希腊国家科学研究中心德谟克利特信息学与电信研究所）；Information Management Systems Institute, Athena Research Center（雅典娜研究中心信息管理系统研究所）；University of Piraeus（比雷埃夫斯大学）
In previous work we introduced a trajectory detection module that can provide summarized representations of vessel trajectories by consuming AIS positional messages online. This methodology can provide reliable trajectory synopses with little deviations from the original course by discarding at least 70% of the raw data as redundant. However, such trajectory compression is very sensitive to parametrization. In this paper, our goal is to fine-tune the selection of these parameter values. We take into account the type of each vessel in order to provide a suitable configuration that can yield imp…
Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment
Faculty of Computer Science, Dalhousie University, Halifax, Canada（计算机科学学院，达尔豪斯大学，加拿大哈利法克斯）；Computer Science Department, University of São Paulo, São Carlos, Brazil（计算机科学系，圣保罗大学，巴西萨昂卡尔）
Universidade Federal de Santa Catarina (UFSC)（圣卡塔琳娜联邦大学（UFSC））；Dalhousie University（达尔豪斯大学）
The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and event detection, considering several constraints imposed by real-time data streams and the maritime monitoring domain. As a starting point, we investigate how unsupervised and semi-supervised change detection methods may be employed for identifying shifts in vessel behavior, aimi…
VISTA: Knowledge-Driven Vessel Trajectory Imputation with Repair Provenance
Department of Computer Science, Aalborg University, Denmark（计算机科学系，奥胡斯大学，丹麦）；School of Computer Science and Engineering, Northeastern University, Shenyang, China（计算机科学与工程学院，东北大学，沈阳，中国）
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
Institute of High Performance Computing (IHPC), A*STAR, Singapore（新加坡科技研究局高性能计算研究所）；The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学道路与交通工程教育部重点实验室）；Meituan Inc., Shenzhen, China（美团（深圳））；Centre for Frontier AI Research (CFAR), A*STAR, Singapore（新加坡科技研究局前沿人工智能研究中心）；Nankai University（南开大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
Norwegian University of Science and Technology（挪威科技大学）；Oklahoma State University（俄克拉荷马州立大学）
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of Deep Reinforcement Learning (DRL) in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy fro…
SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）数据科学学院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）科学与工程学院）；School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）人工智能学院）；COSCO SHIPPING Advanced Technology Institute, Shanghai, China（中远海运技术研究院）
We propose a novel framework to classify large-scale time series data with long duration. Long time seriesclassification (L-TSC) is a challenging problem because the dataoften contains a large amount of irrelevant information to theclassification target. The irrelevant period degrades the classifica-tion performance while the relevance is unknown to the system.This paper proposes an uncertainty-aware multiple instancelearning (MIL) framework to identify the most relevant periodautomatically. The predictive uncertainty enables designing anattention mechanism that forces the MIL model to learn f…
Agency for Science, Technology and Research（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）
Given the trend of digitization and increasing number of maritime transport, prediction of vessel berth stay has been triggered for requirements of operation research and scheduling optimization problem in the era of maritime big data, which takes a significant part in port efficiency and maritime logistics enhancement. This study proposes a systematic and dynamic approach of predicting berth stay for tanker terminals. The approach covers three innovative aspects: 1) Data source employed is multi-faceted, including cargo operation data from tanker terminals, time-series data from automatic ide…
