Vessel trajectory prediction is a critical component for ensuring maritime traffic safety and avoiding collisions. Due to the inherent uncertainty in vessel behavior, trajectory prediction systems must adopt a multimodal approach to accurately model potential future motion states. However, existing vessel trajectory prediction methods lack the ability to comprehensively model behavioral multi-modality. To better capture multimodal behavior in interactive scenarios, we propose modeling interactions as dynamic graphs, replacing traditional aggregation-based techniques that rely on vessel states. By leveraging the natural multimodal capabilities of diffusion models, we frame the trajectory prediction task as an inverse process of motion uncertainty diffusion, wherein uncertainties across potential navigational areas are progressively eliminated until the desired trajectories is produced. In summary, we pioneer the integration of Spatio-Temporal Graph (STG) with diffusion models in ship trajectory prediction. Extensive experiments on real Automatic Identification System (AIS) data validate the superiority of our approach.
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）
Wuhan University of Technology（武汉理工大学）；Institute of Geographic Sciences and Natural Resources Research, CAS（中国科学院地理科学与资源研究所）；Institute of High Performance Computing, A*Star (Agency for Science, Technology and Research)（新加坡科技研究局高性能计算研究所）
To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape- and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method…
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…
Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）
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）
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）
University of Ottawa（渥太华大学）；Bradley University（布拉德利大学）
Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces. In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide the optimal rudder and thrust control signals. This enforces several difficult-to-solve constraints that are associated with the error dynamical equations using classical optimal tracking and adaptive control approaches. An online machine learning mechanism based on integral reinforcement learning is proposed to find a solution for a class of nonlinear tracki…
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, …
Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Nanyang Technological University（南洋理工大学）
In this study, a probability density-based approach for constructing trajectories is proposed and validated through an typical use-case application: Estimated Time of Arrival (ETA) prediction given origin-destination pairs. The ETA prediction is based on physics and mathematical laws given by the extracted information of probability density-based trajectories constructed. The overall ETA prediction errors are about 0.106 days (i.e. 2.544 hours) on average with 0.549 days (i.e. 13.176 hours) standard deviation, and the proposed approach has an accuracy of 92.08% with 0.959 R-Squared value for o…
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）
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）
SSP-DMGTimeNet: Physics-Constrained Learning for Spatiotemporal Trajectory Prediction of Vehicle Platoons
Korea Advanced Institute of Science & Technology (KAIST)（韩国科学技术院）；Chinese Academy of Sciences（中国科学院）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）
Several algorithms have been proposed for discovering patterns from trajectories of moving objects, but only a few have concentrated on outlier detection. Existing approaches, in general, discover spatial outliers, and do not provide any further analysis of the patterns. In this paper we introduce semantic spatial and spatio-temporal outliers and propose a new algorithm for trajectory outlier detection. Semantic outliers are computed between regions of interest, where objects have similar movement intention, and there exist standard paths which connect the regions. We show with experiments on …
This paper considers the problem of multi-modal future trajectory forecast with ranking. Here, multi-modality and ranking refer to the multiple plausible path predictions and the confidence in those predictions, respectively. We propose Social-STAGE, Social interaction-aware Spatio-Temporal multi-Attention Graph convolution network with novel Evaluation for multi-modality. Our main contributions include analysis and formulation of multi-modality with ranking using interaction and multi-attention, and introduction of new metrics to evaluate the diversity and associated confidence of multi-modal…
Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Shanghai Key Laboratory of Data Science, Fudan University（复旦大学数据科学重点实验室）；Shanghai Institute of Intelligent Electronics & Systems（上海智能电子与系统研究所）；School of Computing and Information Systems, Singapore Management University（新加坡国立大学计算与信息系统学院）
Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks. Moreover, previous models barely leverage spatial dependencies or only consider them at the grid cell level, ign…
Trajectory-Aware Adaptive Inference in Object Detection Models
Department of Electrical and Computer Engineering, National Technical University of Athens, Greece（电子与计算机工程系，国家技术大学亚历山大学院，希腊）；Department of Product and Systems Design Engineering, University of the Aegean, Syros, Greece（产品与系统设计工程系，爱琴海大学，西罗斯，希腊）
NICTA VRL（澳大利亚国家信息与通信技术研究院视觉研究实验室）；The University of Melbourne（墨尔本大学）；UNSW（新南威尔士大学）
Nowadays large amounts of GPS trajectory data is being continuously collected by GPS-enabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the POIs in the process of SVL generation. NNQ in some scenarios is not accurate enough. To…
Simula Research Laboratory（Simula研究实验室）；Navtor AS（Navtor公司）
Vessel navigation is influenced by various factors, such as dynamic environmental factors that change over time or static features such as vessel type or depth of the ocean. These dynamic and static navigational factors impose limitations on vessels, such as long waiting times in regions outside the actual ports, and we call these waiting regions gateway ports. Identifying gateway ports and their associated features such as congestion and available utilities can enhance vessel navigation by planning on fuel optimization or saving time in cargo operation. In this paper, we propose a novel tempo…
STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data
State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和计算机与信息科学系，澳门大学）；Beijing Normal University-Hong Kong Baptist University United International College（北京师范大学-香港 Baptist大学联合国际学院）
Prognostication of vehicle trajectories in unknown environments is intrinsically a challenging and difficult problem to solve. The behavior of such vehicles is highly influenced by surrounding traffic, road conditions, and rogue participants present in the environment. Moreover, the presence of pedestrians, traffic lights, stop signs, etc., makes it much harder to infer the behavior of various traffic agents. This paper attempts to solve the problem of Spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network. The Memory Neuron Net…
DJI Technology Co., Ltd.（大疆创新科技有限公司）；Hong Kong University of Science and Technology（香港科技大学）
Planning safe trajectories for autonomous vehicles in complex urban environments is challenging since there are numerous semantic elements (such as dynamic agents, traffic lights and speed limits) to consider. These semantic elements may have different mathematical descriptions such as obstacle, constraint and cost. It is non-trivial to tune the effects from different combinations of semantic elements for a stable and generalizable behavior. In this paper, we propose a novel unified spatio-temporal semantic corridor (SSC) structure, which provides a level of abstraction for different types of …
University of Washington（华盛顿大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）
The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical gridding and model temporal relation with explicit time intervals, while some vital questions remain unsolved. Non-adjacent locations and non-consecutive visits provide non-trivial correlations for understanding a user's behavior but were rarely considered. To aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations, here we propose a Spatio-Temporal Att…
DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction
School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；Second Research Institute of Civil Aviation Administration of China（中国民用航空局第二研究所）
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
GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction
School of Information Technology, Monash University Malaysia（墨尔本大学马来西亚分校信息科技学院）；School of Engineering, Monash University Malaysia（墨尔本大学马来西亚分校工程学院）；Perunding Atur Trafik Sdn Bhd, Malaysia（马来西亚交通自动化有限公司）
Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
Department Hydraulic Engineering in Inland Areas（内陆水域水利工程部门）；Federal Waterways Engineering and Research Institute（联邦水道工程与研究机构）；Chair of Dynamics and Control（动力学与控制教授职位）；University of Duisburg-Essen（杜伊斯堡- Essen大学）；Department of Technology (Computer Science)（技术部门（计算机科学））；Baden-Wuerttemberg Cooperative State University（巴登-符腾堡州立合作大学）；Institute for Sustainable and Autonomous Maritime Systems（可持续与自主水运系统研究所）
Royal Melbourne Institute of Technology（皇家墨尔本理工大学）；DATA61 / CSIRO（澳大利亚联邦科学与工业研究组织数据61研究所）；Australian National University（澳大利亚国立大学）
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achi…
