Recurrent Encoder–Decoder Networks for Vessel Trajectory Prediction With Uncertainty Estimation

Evaluation of ship collision risk in ships’ routeing waters: A Gini coefficient approach using AIS data

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

The advancement of Maritime Autonomous Surface Ships (MASS) relies heavily on data-driven knowledge discovery and situational awareness to ensure safe and efficient navigation. This PhD by Published Works comprises five peer-reviewed journal papers, contributing to the integration of Artificial Intelligence (AI), machine learning, deep learning, and Bayesian Network (BN) into key areas of maritime safety and security. The research aims to enhance risk assessment, piracy incident analysis, ship trajectory prediction, and autonomous route planning, forming a cohesive framework for intelligent decision-making in MASS operations. The first contribution focuses on data-driven risk analysis of global maritime accidents, employing a data-driven BN model to quantify accident risks, identify critical factors, and enhance safety management strategies. The second contribution addresses spatio-temporal piracy pattern analysis, utilising AI-driven methodologies to detect high-risk zones and extract patterns of piracy incidents, thereby improving security measures and navigational decision-making for MASS. The third and fourth contributions focus on ship trajectory prediction and route planning, which are crucial for autonomous navigation. A systematic review of machine learning and deep learning-based trajectory prediction methods highlights the effectiveness of data-driven models over traditional approaches. Further, an unsupervised route planning model integrates Automatic Identification System (AIS) data and machine learning techniques, enabling autonomous vessels to adapt dynamically to changing maritime conditions. The final contribution refines AIS-based trajectory prediction frameworks, leveraging machine learning methodologies to improve predictive accuracy and enhance collision avoidance capabilities. By synthesising insights from these studies, this research establishes a holistic framework for data-driven knowledge discovery and situational awareness in MASS. The integration of this research content and outcomes ensures that autonomous vessels can navigate safely, mitigate operational risks, and enhance adaptability to evolving maritime conditions. This study advances AI-driven autonomous navigation by bridging data-driven knowledge discovery with real-time maritime decision-making, providing a resilient and adaptive foundation for next-generation MASS technology.

<span lang="EN-US">Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.</span>

Automatic Identification System (AIS) provides abundant near real-time information of moving vessels in the sea of the whole world and has been widely used in the fields of vessel collision avoidance, Maritime Situation Awareness (MSA) and ocean surveillance. The development of satellite-based AIS further expands the range of AIS and enables a wide converge of AIS data collection, which solves the problem of lacking AIS data in high-seas. At the same time, AIS data provides a rich source for data mining for maritime traffic analysis. In this paper, a typical clustering algorithm called K-means is applied to deal with the Space-based AIS(S-AIS) data received by “TianTuo-3” satellite developed by National University of Defense Technology. We use Elbow Rule to determine the optimal number of clusters and calculate the normalized standard deviation of COG(Course Over Ground) and SOG(Speed Over Ground) of vessels in south Africa area as their features to conduct clustering. This method is supposed to evaluate vessels' sailing stability and used in detection of low-likelihood behaviors or anomalies of vessels.

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the Automatic Identification System, which serves as a communication medium between neighboring ships and the control center. While traditional methods have relied on sequential tracking and physics-based models, the emergence of deep learning has significantly transformed techniques typically used in trajectory prediction, clustering, and anomaly detection. This transformation is largely attributed to the deep learning algorithm’s capability to model complex nonlinear relationships while capturing both the spatial and temporal dynamics of ship movement. Capitalizing on this computational advantage, our study focuses on evaluating different deep learning architectures such as Multi Model Long Short-Term Memory (LSTM), 1D Convolutional-LSTM, and Temporal-Graph Convolutional Neural Networks— in addressing the problem of track association. The performance of these proposed models are compared against different deep learning algorithms specialized in track association tasks using several real-life AIS datasets.

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data.

In order to study the ship traffic flow in LaoTieshan channel, this paper rewrites the rules of NS (Nagel-Schreckenberg) model of Cellular Automata (CA) based on AIS（Automatic Identification System） data. By referring to the idea of synchronous flow in three-phase traffic flow theory and introducing the speed adaptation mechanism into the simulation, the new model reproduces the traffic flow of LaoTieshan channel and gets the relevant fundamental diagrams. Compared with the actual flow-density diagram drawn from AIS data, it is found that the simulated flow generated by the improved model is more in line with the actual condition, and also conforms to the phase transition of traffic flow in LaoTieshan channel. The method and idea of the simulation are helpful to explain and investigate some complex situations in LaoTieshan channel.

Full mission marine training scenarios are essential in training marine trainees with ship handling simulators. In a good training simulation not only the static environment but also the dynamic surrounding ships plays a major role in terms of behaviour realism. The surrounding ships also need to navigate according to COLREG (Convention on International Regulation for Preventing Collisions at Sea) navigational rules. This research presents a novel approach to automate the surrounding vessels in a marine simulation environment with a central controller to control the behaviours of each surrounding vessel in the marine environment. The controller uses positioning data of real ships which are derived from a set of historical Automatic Identification System (AIS) data and map the positioning data with the surrounding ships in virtual environment to obtain the navigation. An Ant Colony Optimization (ACO) algorithm is used to avoid the collisions by generating a new path when both trainee's ship and surrounding ships are in the vicinity of each other. The collision handling mechanism uses COLREG rules. The ACO algorithm generates successful collision avoided paths in all head-on, crossing and over-taking encountering situations. The number of turning points is used to measure the smoothness of the path and number of obstacles in the environment and number of nodes in the graph affects the smoothness of the path. With these results it is revealed that AIS data can be used as an assistant to automate surrounding vessels in a virtual maritime environment together with a proper collision avoidance mechanism.

Maritime Autonomous Surface Ships (MASS) are deemed as the future of maritime transport. Although showing attractiveness in terms of the solutions to emerging challenges such as carbon emission and insufficient labor caused by black swan events such as COVID-19, the applications of MASS have revealed problems in practice, among which MASS navigation safety presents a prioritized concern. To ensure safety, rational route planning for MASS is evident as the most critical step to avoiding any relevant collision accidents. This paper aims to develop a holistic framework for the unsupervised route planning of MASS using machine learning methods based on Automatic Identification System (AIS) data, including the coherent steps of new feature measurement, pattern extraction, and route planning algorithms. Historical AIS data from manned ships are trained to extract and generate movement patterns. The route planning for MASS is derived from the movement patterns according to a dynamic optimization method and a feature extraction algorithm. Numerical experiments are constructed on real AIS data to demonstrate the effectiveness of the proposed method in solving the route planning for different types of MASS.

Recreational boating is an increasing activity in coastal areas and its spatiotemporal overlap with key habitats of marine species pose a risk for negative noise impacts. Yet, recreational vessels are currently unaccounted for in vessel noise models using Automatic Identification System (AIS) data. Here we conduct a case study investigating noise contributions from vessels with and without AIS (non-AIS) in a shallow coastal area within the Inner Danish waters. By tracking vessels with theodolite and AIS, while recording ambient noise levels, we find that non-AIS vessels have a higher occurrence (83%) than AIS vessels, and that motorised recreational vessels can elevate third-octave band noise centred at 0.125, 2 and 16 kHz by 47–51 dB. Accordingly, these vessels dominated the soundscape in the study site due to their high numbers, high speeds and proximity to the coast. Furthermore, recreational vessels caused 49–85% of noise events potentially eliciting behavioural responses in harbour porpoises (AIS vessels caused 5–24%). We therefore conclude that AIS data would poorly predict vessel noise pollution and its impacts in this and other similar marine environments. We suggest to improve vessel noise models and impact assessments by requiring that faster and more powerful recreational vessels carry AIS-transmitters.

Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means and dropout k-median to improve placement robustness to buoy disruption.We simulated the passage of ships in the Gabonese waters near West Africa using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%.

Path planning for large ships in inland waterways considering risk assessment of AIS data

Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities