Complex Navigation Research Articles

The Impact of Offshore Wind Farm Construction on Maritime Traffic Complexity: An Empirical Analysis of the Yangtze River Estuary

The rapid growth of offshore wind farms (OWFs) as renewable energy sources has heightened concerns about maritime traffic safety and management in high-density traffic zones. These areas, characterized by complex interactions among diverse ship types and spatial constraints, require advanced situational awareness to prevent collisions and ensure efficient operations. Traditional maritime traffic systems often lack the granularity to assess the multifaceted risks around OWFs. Existing research has explored local traffic patterns and collision risks but lacks comprehensive frameworks for evaluating traffic complexity at both micro and macro levels. This study proposes a new complexity assessment model tailored to OWF areas, integrating micro-level ship interactions and macro-level traffic flow conditions to capture a holistic view of traffic dynamics. Using extensive historical AIS data from the Yangtze River Estuary, the model evaluates the impact of the proposed OWF on existing traffic complexity. The results demonstrate that OWFs increase navigational complexity, particularly in route congestion, course adjustments, and encounter rates between ships. Different ship types and sizes were also found to experience varying levels of impact, with larger ships and tankers facing greater challenges. By providing a quantitative framework for assessing traffic complexity, this research advances the field’s ability to understand and manage the risks associated with OWFs. The findings offer actionable insights for maritime authorities and OWF operators, supporting more effective traffic management strategies that prioritize safety and operational efficiency in high-density maritime areas.

Robust Nonlinear Model Predictive Control for the Trajectory Tracking of Skid-Steer Mobile Manipulators with Wheel–Ground Interactions

This paper presents a robust control strategy for trajectory-tracking control of Skid-Steer Mobile Manipulators (SSMMs) using a Robust Nonlinear Model Predictive Control (R-NMPC) approach that minimises trajectory-tracking errors while overcoming model uncertainties and terra-mechanical disturbances. The proposed strategy is aimed at counteracting the effects of disturbances caused by the slip phenomena through the wheel–terrain contact and bidirectional interactions propagated by mechanical coupling between the SSMM base and arm. These interactions are modelled using a coupled nonlinear dynamic framework that integrates bounded uncertainties for the mobile base and arm joints. The model is developed based on principles of full-body energy balance and link torques. Then, a centralized control architecture integrates a nominal NMPC (disturbance-free) and ancillary controller based on Active Disturbance-Rejection Control (ADRC) to strengthen control robustness, operating the full system dynamics as a single robotic body. While the NMPC strategy is responsible for the trajectory-tracking control task, the ADRC leverages an Extended State Observer (ESO) to quantify the impact of external disturbances. Then, the ADRC is devoted to compensating for external disturbances and uncertainties stemming from the model mismatch between the nominal representation and the actual system response. Simulation and field experiments conducted on an assembled Pioneer 3P-AT base and Katana 6M180 robotic arm under terrain constraints demonstrate the effectiveness of the proposed method. Compared to non-robust controllers, the R-NMPC approach significantly reduced trajectory-tracking errors by 79.5% for mobile bases and 42.3% for robot arms. These results highlight the potential to enhance robust performance and resource efficiency in complex navigation conditions.

Leveraging network analysis to improve navigability of design standards

The utility of building design standards is a growing concern amongst construction professionals. Design standards continue to swell with updates, which makes ensuring all requirements are satisfied increasingly complex for users. Few tools exist for authors of standards to improve navigation for users. This study investigates the use of network analysis to understand the relationship between the organization of a standard and navigational complexity. A case study is presented of the reorganization of American Concrete Institute’s (ACI) flagship design document, ACI 318. The standard’s networks before (ACI 318-11) and after (ACI 318-14) the reorganization are developed via rule-based text extraction. Networks are analyzed assuming that ACI 318-14 is the structurally superior document. Indicators of complexity are identified from each networks' characteristic features, centrality metrics, clustering tendencies, recurring motifs, and geodesic paths. Network analysis is found to be useful for identifying, understanding, and mitigating navigational complexity within a building design standard.

Interpreting the space characteristics of everyday heritage gardens of Suzhou, China, through a space syntax approach

ABSTRACT China’s everyday heritage gardens, epitomizing social and spatial logic through classical layouts, offer a window into the nexus between Chinese culture and spatial design. Using space syntax and statistical methods, we examined 27 such gardens in Suzhou, categorizing them into four groups by spatial attributes. We first quantified building spatial traits, then delved into their syntactical relationships, guiding the final clustering. Cluster A, with 17 gardens, marries architectural diversity with spatial harmony, ensuring navigability. Cluster B’s seven gardens prioritize open landscapes, minimizing buildings for clarity. Cluster C, comprising two expansive gardens, blends architectural variety with spatial coherence, albeit with some navigational complexity. Unique, Cluster D, solely Yi Garden, presents a rich spatial and architectural tapestry, demanding deeper visitor engagement. This work sheds light on the intertwining of garden design and spatial structures, underscoring influences like ownership and cultural milieu, suggesting broader applicability to heritage gardens.

Literary Realism, Speculative Fiction, and Queer African Futures in Akwaeke Emezi's Freshwater

ABSTRACT: This article positions Akwaeke Emezi's novel Freshwater (2018) as a text that challenges definitions of multiple genres, namely literary realism and speculative fiction. I argue that Freshwater 's seemingly fantastical elements are in fact iterations of Igbo cosmology, which recognizes coterminous human and spiritual realms as part of material reality. By positioning Igbo ontology as the grounding principle of earthly existence, Freshwater contests the division of realism into multiple subsets (magical, animist, literary). Furthmore, I argue that the novel playfully refigures ogbanje identities and Igbo cosmological structures to envision new horizons for future kinship. Rather than speculating about futures that respond to African histories of European colonization, Freshwater reconfigures uniquely Igbo concepts such as ogbanje and iyi-uwa into means for queer agential re-destination. Thus, the novel shifts the speculative focus away from postcolonial efforts to "write back," and instead locates the basis for speculative futures within Igbo ontological structures. Freshwater 's complex navigation of not only multiple realisms and speculative fiction, but also tradition and change, defies easy categorization and instead offers open-ended avenues for queer Igbo and other African futurities.

Research on Navigation Risk Assessment of Unmanned Ship Under Complex Navigation Conditions

With the research on unmanned ships conducted by the International Maritime Organization, the European Union and China, the navigation risk of unmanned ships has become a hot topic around the world. Based on the research and development history and current situation of unmanned ships at home and abroad, focusing on the three key elements of “Unmanned Ship, shore-based control personnel, external navigation environment”, this study establishes a navigation risk index system for unmanned ships under complex navigation conditions, considering the particularity of the unmanned ship, its perception ability, environmental understanding, situation judgment, communication ability and other indicators. The analytic hierarchy process is used to solve the weight of all kinds of navigation risk factors, and a consistency test of the established index system is carried out. Through expert investigation, the fuzzy membership set of risk indexes is established, and a fuzzy comprehensive evaluation model is established to evaluate the navigation risk of unmanned ships under complex navigation conditions. To avoid the situation of single-factor information being inundated, this study adopts the method of fuzzy comprehensive evaluation from a low level to a high level, and it verifies the algorithm through cases, which proves the validity and rationality of the proposed risk assessment method.

Development of a method for predicting hazardous ship trajectories under uncertainty of navigator actions

The object of the research is the automation processes in maritime navigation to ensure the safety of ship movement by predicting their trajectories in complex aquatic areas, such as narrow passages, straits, and ports. The research applied six key stages to create a comprehensive method for clustering and predicting ship trajectories based on ECDIS data. In the first stage, ship movement trajectories were constructed according to risk categories, using the LCSS and DTW algorithms to compare planned and actual trajectories. This allowed for the accurate identification of course deviations and the determination of potentially dangerous sections of the trajectory. The second stage implemented clustering using the DBSCAN and GMM algorithms. DBSCAN was used to identify the density of points in space, and GMM provided modeling of cluster probabilities, allowing for better risk zone determination. The third stage applied the Douglas-Peucker compression algorithm to reduce the number of points in the trajectories, which preserved key characteristics and optimized data processing. In the fourth stage, ship movement stability was assessed using the Fourier transform, which allowed the detection of high-frequency oscillations that may indicate movement instability caused by changes in course or speed. The fifth stage included fuzzy clustering of trajectories using the Gaussian Mixture Model (GMM), which allowed modeling the probabilities of dangerous trajectories, considering the uncertainty of navigational parameters. At the final stage, a multilayer neural network (MLP) was used to predict future points of ship trajectories. The model accurately predicted the ship's coordinates, enabling timely trajectory adjustments. Experimental results showed that the developed method increased the accuracy of ship trajectory prediction to 72–81 % and also significantly reduced the final error, ensuring effective risk management during complex navigation.

Revisiting resistance: student diaries and educational aspirations in Colonial Korea (1920–1945)

ABSTRACT This article examines the intricate interplay of educational enthusiasm, conformity, and resistance amongst Korean students during the Japanese colonial period (1920–1945). It explores how Japan’s governance shift in the 1920s, which extended schooling years and introduced co-education, elicited mixed responses from Korean students, ranging from compliance to various forms of resistance. By examining student diaries, this study unravels the complexities of educational aspirations and resistance and challenges traditional binary views of colonial educational history. The research highlights the heightened educational zeal in the 1920s and 1930s, characterised by increased school enrolment and fierce competition for admissions. The diaries provide insight into students’ reactions to colonial policies, blending aspirations for advancement with subtle forms of resistance. In analysing diary entries, this paper uncovers how increases in student enrolment and rising educational aspirations reflect both conformity with and resistance to colonial policies. Explicit resistance, more prominent in the 1920s, was replaced by implicit forms as militarism intensified in the 1930s. This study categorises resistance into organised (overt) and disorganised (covert) forms, with the latter including subtler forms of opposition, such as disquieting, indirect, and silent resistances. The diaries examined in this study reflect the dual life experiences of students, who adhered to public norms while nurturing private dissent. Manifestations of resistance ranged from disquieting actions to metaphorical defiance and strategic silence, which illustrates students’ complex navigation between external conformity and internal rebellion. This understanding of compliance and resistance, encompassing overt acts and subtle unspoken forms, underscores the need for a comprehensive view of educational aspirations during this historical period.

External Steering of Vine Robots via Magnetic Actuation.

This article explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25-mm-diameter vine robot with an integrated magnetic tip capsule, including 6 degrees of freedom (DOF) localization system and camera, and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation.

An enhanced stochastic error modeling using multi-Gauss–Markov processes for GNSS/INS integration system

Angular random walk (ARW), rate random walk (RRW), and bias instability (BI) are the main noise types in inertial measurement units (IMUs) and thus determine the navigation performance of IMUs. BI is the flicker noise, which determines the noise level of an inertial sensor. The traditional error modeling approach involves modeling the ARW and BI processes as RRW or Gauss–Markov (GM) processes, and this approach is applied as a suboptimal filter in the global navigation satellite system (GNSS)/inertial navigation system (INS) extended Kalman filter (EKF). In this paper, the random error identification processes for white noise and colored noise for inertial sensors are separated using the Allan variance and power spectral density methods and the equivalence of the stochastic process differential equations of bias instability and a combination of multiple first-order GM processes are derived. A colored noise compensation method is proposed based on the enhanced EKF model. Experimental results demonstrate that, compared to traditional error models, our proposed model reduces positional drift error in dynamic testing from 195 to 49 m, enhancing positional accuracy by 40.2%. These findings confirm the potential and superiority of our method in complex navigation environments.

Complexity Analysis Using Graph Models for Conflict Resolution for Autonomous Ships in Complex Situations

Abstract Maritime autonomous surface ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verifications to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; artificial intelligence-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers decision-making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multicriteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. This paper conducts a comprehensive analysis of DM and navigational complexity; we develop novel complexity estimation scores, tools for complexity monitoring for human intervention, and spatial analysis of traffic complexity for geo-intelligent MASSs deployment and operation planning. The presented work is validated on a database of historical scenarios extracted from multiple data sources.

Optimization of Sailing Speed for Inland Electric Ships Based on an Improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm

Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions.

Role of Ship Guidance in Reducing Accident Risks in Tanjung Perak’s Mandatory Waters

This study investigates the effectiveness of ship guidance in minimizing accident risks within the mandatory guidance waters of Tanjung Perak Surabaya. Employing qualitative research methods and descriptive analysis, the research evaluates ship pilot performance based on communication effectiveness, maneuvering precision, regulatory knowledge, emergency response, and professionalism. The findings reveal that effective pilotage significantly enhances navigational safety, contributing to a 20% reduction in accident rates. Despite generally high performance, challenges such as communication barriers, navigational complexity, and regulatory constraints persist. Recommendations include investing in advanced navigational technologies, improving communication protocols, updating regulations for better operational flexibility, and ensuring regular equipment maintenance. The study highlights the critical role of ship pilots in ensuring safe maritime operations and provides actionable insights for optimizing pilotage practices to further enhance safety in complex navigation environments

Parallel vector memories in the brain of a bee as foundation for flexible navigation

Insects rely on path integration (vector-based navigation) and landmark guidance to perform sophisticated navigational feats, rivaling those seen in mammals. Bees in particular exhibit complex navigation behaviors including creating optimal routes and novel shortcuts between locations, an ability historically indicative of the presence of a cognitive map. A mammalian cognitive map has been widely accepted. However, in insects, the existence of a centralized cognitive map is highly contentious. Using a controlled laboratory assay that condenses foraging behaviors to short distances in walking bumblebees, we reveal that vectors learned during path integration can be transferred to long-term memory, that multiple such vectors can be stored in parallel, and that these vectors can be recalled at a familiar location and used for homeward navigation. These findings demonstrate that bees meet the two fundamental requirements of a vector-based analog of a decentralized cognitive map: Home vectors need to be stored in long-term memory and need to be recalled from remembered locations. Thus, our data demonstrate that bees possess the foundational elements for a vector-based map. By utilizing this relatively simple strategy for spatial organization, insects may achieve high-level navigation behaviors seen in vertebrates with the limited number of neurons in their brains, circumventing the computational requirements associated with the cognitive maps of mammals.

Cautious Gait during Navigational Tasks in People with Hemiparesis: An Observational Study.

Locomotor and balance disorders are major limitations for subjects with hemiparesis. The Timed Up and Go (TUG) test is a complex navigational task involving oriented walking and obstacle circumvention. We hypothesized that subjects with hemiparesis adopt a cautious gait during complex locomotor tasks. The primary aim was to compare spatio-temporal gait parameters, indicators of cautious gait, between the locomotor subtasks of the TUG (Go, Turn, Return) and a Straight-line walk in people with hemiparesis. Our secondary aim was to analyze the relationships between TUG performance and balance measures, compare spatio-temporal gait parameters between fallers and non-fallers, and identify the biomechanical determinants of TUG performance. Biomechanical parameters during the TUG and Straight-line walk were analyzed using a motion capture system. A repeated measures ANOVA and two stepwise ascending multiple regressions (with performance variables and biomechanical variables) were conducted. Gait speed, step length, and % single support phase (SSP) of the 29 participants were reduced during Turn compared to Go and Return and the Straight-line walk, and step width and % double support phase were increased. TUG performance was related to several balance measures. Turn performance (R2 = 63%) and Turn trajectory deviation followed by % SSP on the paretic side and the vertical center of mass velocity during Go (R2 = 71%) determined TUG performance time. People with hemiparesis adopt a cautious gait during complex navigation at the expense of performance.

Hippocampal-dependent navigation in head-fixed mice using a floating real-world environment

Head-fixation of mice enables high-resolution monitoring of neuronal activity coupled with precise control of environmental stimuli. Virtual reality can be used to emulate the visual experience of movement during head fixation, but a low inertia floating real-world environment (mobile homecage, MHC) has the potential to engage more sensory modalities and provide a richer experimental environment for complex behavioral tasks. However, it is not known whether mice react to this adapted environment in a similar manner to real environments, or whether the MHC can be used to implement validated, maze-based behavioral tasks. Here, we show that hippocampal place cell representations are intact in the MHC and that the system allows relatively long (20 min) whole-cell patch clamp recordings from dorsal CA1 pyramidal neurons, revealing sub-threshold membrane potential dynamics. Furthermore, mice learn the location of a liquid reward within an adapted T-maze guided by 2-dimensional spatial navigation cues and relearn the location when spatial contingencies are reversed. Bilateral infusions of scopolamine show that this learning is hippocampus-dependent and requires intact cholinergic signalling. Therefore, we characterize the MHC system as an experimental tool to study sub-threshold membrane potential dynamics that underpin complex navigation behaviors.

Environmental enrichment: a systematic review on the effect of a changing spatial complexity on hippocampal neurogenesis and plasticity in rodents, with considerations for translation to urban and built environments for humans.

Hippocampal neurogenesis is critical for improving learning, memory, and spatial navigation. Inhabiting and navigating spatial complexity is key to stimulating adult hippocampal neurogenesis (AHN) in rodents because they share similar hippocampal neuroplasticity characteristics with humans. AHN in humans has recently been found to persist until the tenth decade of life, but it declines with aging and is influenced by environmental enrichment. This systematic review investigated the impact of spatial complexity on neurogenesis and hippocampal plasticity in rodents, and discussed the translatability of these findings to human interventions. Comprehensive searches were conducted on three databases in English: PubMed, Web of Science, and Scopus. All literature published until December 2023 was screened and assessed for eligibility. A total of 32 studies with original data were included, and the process is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement and checklist. The studies evaluated various models of spatial complexity in rodents, including environmental enrichment, changes to in-cage elements, complex layouts, and navigational mazes featuring novelty and intermittent complexity. A regression equation was formulated to synthesize key factors influencing neurogenesis, such as duration, physical activity, frequency of changes, diversity of complexity, age, living space size, and temperature. Findings underscore the cognitive benefits of spatial complexity interventions and inform future translational research from rodents to humans. Home-cage enrichment and models like the Hamlet complex maze and the Marlau cage offer insight into how architectural design and urban navigational complexity can impact neurogenesis in humans. In-space changing complexity, with and without physical activity, is effective for stimulating neurogenesis. While evidence on intermittent spatial complexity in humans is limited, data from the COVID-19 pandemic lockdowns provide preliminary evidence. Existing equations relating rodent and human ages may allow for the translation of enrichment protocol durations from rodents to humans.

SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments

Semantic reasoning and dynamic planning capabilities are crucial for an autonomous agent to perform complex navigation tasks in unknown environments. It requires a large amount of common-sense knowledge, that humans possess, to succeed in these tasks. We present SayNav, a new approach that leverages human knowledge from Large Language Models (LLMs) for efficient generalization to complex navigation tasks in unknown large-scale environments. SayNav uses a novel grounding mechanism, that incrementally builds a 3D scene graph of the explored environment as inputs to LLMs, for generating feasible and contextually appropriate high-level plans for navigation. The LLM-generated plan is then executed by a pre-trained low-level planner, that treats each planned step as a short-distance point-goal navigation sub-task. SayNav dynamically generates step-by-step instructions during navigation and continuously refines future steps based on newly perceived information. We evaluate SayNav on multi-object navigation (MultiON) task, that requires the agent to utilize a massive amount of human knowledge to efficiently search multiple different objects in an unknown environment. We also introduce a benchmark dataset for MultiON task employing ProcTHOR framework that provides large photo-realistic indoor environments with variety of objects. SayNav achieves state-of-the-art results and even outperforms an oracle based baseline with strong ground-truth assumptions by more than 8% in terms of success rate, highlighting its ability to generate dynamic plans for successfully locating objects in large-scale new environments. The code, benchmark dataset and demonstration videos are accessible at https://www.sri.com/ics/computer-vision/saynav.

Numerical simulation study on ship manoeuvrability in mountainous rivers: Comprehensively considering effects of nonuniform flow, shallow water, narrow banks, winds and waves

Ships navigating inland waterways, especially mountainous waterways, may encounter complex navigation environments. Under these circumstances, the force of the ship is extremely complex, the attitude of the ship may be unstable, and the control performance may decrease, possibly resulting in marine accidents. In this study, a modular-type manoeuvring model (MMG) is used to develop a ship manoeuvring model that comprehensively considers multiple factors. Based on this model, numerical simulations are conducted on the ship Seiun Maru, and Esso Osaka; the simulation results are found to be in good agreement with the results of real ship motions. Then, numerical simulations are conducted on the navigation of a 500-ton ship in a typical mountainous waterway, the Suoluo Rapids in the Lancang River, and the impact of the complex environment in this area on the safety of ship navigation is explored. The results reveal that ignoring the influence of environmental factors may put ships in dangerous situations when navigating mountain waterways. In addition, the model can effectively simulate the manoeuvring motion of a ship in a typical mountain waterway, and the calculation cost is negligible. This study provides valuable suggestions for long-distance ship manoeuvring in complex inland river environments.

Artificial Intelligence in Ship Trajectory Prediction

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research.