Collision Avoidance Behavior Mining Model Considering Encounter Scenarios

The exploration of ship collision avoidance behavior characteristics can provide a theoretical basis for ship collision risk assessment and collision avoidance decision-making, which is significant for ensuring maritime navigation safety and the development of intelligent ships. In order to scientifically and effectively analyze the characteristics of ship collision-avoidance behavior and to seek the intrinsic connections among ship collision-avoidance behavior feature parameters(CABFPS), this study proposes a method that combines the Apriori algorithm and complex network theory to mine ship collision-avoidance behavior characteristics from massive AIS spatiotemporal data. Based on obtaining ship encounter samples and CABFPS from AIS data, the Apriori algorithm is used to mine the association rules of motion parameters, and the maximum mutual information coefficient is employed to represent the correlation between parameters. Complex networks of CABFPS for different encounter situations are constructed, and network topological indicators are analyzed. Mutual information theory is applied to identify key parameters affecting ship collision- avoidance behavior under different situations. The analysis using actual AIS data indicates that during navigation, the relationships among various parameters are closely linked and prone to mutual influence. The impact of CABFPS on ship collision-avoidance actions varies under different encounter scenarios, with relative distance and DCPA having the greatest influence on ship collision-avoidance actions. This method can comprehensively and accurately mine the correlations between CABFPS and the influence mechanism of parameters on collision-avoidance actions, providing a reference for intelligent ship navigation and the formulation of collision-avoidance decisions.

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

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.

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.

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

Ship collision avoidance behaviour recognition and analysis based on 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.

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.

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.

Artificial Force Fields for Multi-agent Simulations of Maritime Traffic: A Case Study of Chinese Waterway

In China’s inland waterway transport, some private cargo ships are illegally and nominally registered under the names of large shipping companies for benefit, as a result of the hidden agreements between these companies and private shipowners. These are known as “guakao” ships. To reduce costs, private shipowners often employ insufficient and underqualified crew members, making the fraudulently registered guakao ships substandard in safety management and leading to numerous accidents. To address the challenge of identifying guakao ships, this paper presents a data-driven identification approach based on background knowledge of ship operations. Firstly, the automatic identification system (AIS) data are collected and analyzed to derive ship behavior characteristics by data mining and community detection algorithms. Subsequently, machine learning models are formulated and trained to predict ships’ authentic owner types from the input behavior features, thereby identifying the potential guakao ships with fraudulent owner information. Finally, the effectiveness of our identification approach is validated via a case study on a typical bulk shipping company in China. Furthermore, the optimal trade-off between identification accuracy and timeliness is identified by examining the impacts of the length of the AIS dataset on identification results. The proposed approach can help authorities better detect fraudulent registrations of ships and improve the allocation of inspection resources.

Inland waterway transport is an essential element of integrated transport systems, and the inland waterway freight volume accounts for about 50% of the total waterway freight volume in China. During the navigation, anchoring, and operation of ships, various water pollutants are generated, and the pollution generated by sewage is receiving more and more attraction. To prevent and control pollution from ships, it is important to estimate the amount of sewage and pollutants involved. In this study, the data preparation process is established to generate the Degree of Ship Activity (DSA) data pool after cleaning and thinning the massive original Automatic identification System (AIS) data, and then the data fusion method of a fine GIS grid is established to integrate the DSA data into each grid. The total DSA in the lower reaches of the Yangtze River is 37.14 million h/a. The sewage and pollutant generation inventories for the lower reaches of Yangtze River are estimated and analyzed spatiotemporally. It is estimated that the generations of sewage are 1,768,600 t/a in total. After spatial analysis, it is revealed that the water areas with a relatively large amount of pollutant generation are mainly related to ports distributed along the channel and the DSA density. Finally, based on the spatial distribution characteristics of the estimated inventories, the countermeasures of “zero discharge” for inland ships, the receiving facility system improving, and prevention and control at the river basin level are proposed.

A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty

Identifying illegal fishing activities from Automatic Identification System (AIS) data is difficult since AIS messages are broadcast cooperatively, the ship’s master controls the timing, and the content of the transmission and the activities of interest usually occur far away from the shore. This paper presents our work to predict ship types using AIS data from satellites: in such data, there is a pronounced imbalance between the data for different types of ships, the refresh rate is relatively low, and there is a misreporting of information. To mitigate these issues, our prediction algorithm only uses the sequence of ports the ships visited, as inferred from the positions reported in AIS messages. Experiments involving multiple machine learning algorithms showed that such port visits are informative features when inferring ship type. In particular, this was shown to be the case for the fishing vessels, which is the focus of this paper. We then applied a KD-tree to efficiently identify pairs of ships that are close to one another. As this activity is usually dangerous, multiple occurrences of such encounters that are linked to one ship sensibly motivate extra attention. As a result of applying the analysis approach to a month of AIS data related to a large area in Southeast Asia, we identified 17 cases of potentially illegal behaviours.