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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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      <title>Skills and competencies for operating maritime autonomous surface ships (MASS): a systematic review and bibliometric analysis</title>
      <link>https://trid.trb.org/View/2647033</link>
      <description><![CDATA[The emergence of autonomous ships brings a significant transformation, offering operational efficiencies and environmental sustainability through the implementation of intelligent systems. This transition, however, introduces intricate challenges, particularly related to human-machine interaction, highlighting the crucial role of maritime education and training institutions in equipping the future maritime workforce with appropriate skills and competencies. This paper presents a systematic review and bibliometric analysis to offer a comprehensive exploration of the skills and competencies required for the future of autonomous shipping. The skills framework derived from the literature provides a holistic view of the competencies required for the successful operation of maritime autonomous surface ships, encompassing technical and digital proficiency, operational and managerial competencies, higher-order thinking skills, and interpersonal skills. The findings of this study have significant implications for the future of maritime education and training, workforce development, and policymaking. The identified skills framework serves as a foundation for curriculum development and training program design, emphasising the need for a hybrid approach that balances traditional seafaring skills with emerging competencies. The study underscores the importance of lifelong learning, adaptability, and collaboration as key enablers for navigating the complexities of autonomous shipping.]]></description>
      <pubDate>Wed, 28 Jan 2026 08:51:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647033</guid>
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    <item>
      <title>A Hierarchical Control Scheme for Trajectory Tracking USVs in Environments With Disturbances and Dynamic Obstacles</title>
      <link>https://trid.trb.org/View/2591646</link>
      <description><![CDATA[In this paper, a hierarchical control scheme for trajectory-tracking unmanned surface vehicles (USVs) is proposed and tested. The proposed control scheme is capable of avoiding dynamic obstacles under wind and sea current disturbances. In the controller hierarchy, a waypoint guidance law integrated with a runtime obstacle avoidance algorithm, a next-point generation algorithm for the waypoint guidance law, and a linear quadratic regulator controller together with a yaw autopilot are designed. The performance of the proposed hierarchical controller scheme is evaluated in environments including static and dynamic obstacles of variable sizes and speeds. Furthermore, the tests include the wind and sea current disturbances. The paper aims to address the gap in the literature on runtime dynamic obstacle avoidance of USVs in mild environmental conditions. The superiority of the proposed controller design is demonstrated in various scenarios, where the vehicle avoids the obstacles and reaches the final waypoint with satisfactory path tracking performance under the disturbances and dynamic characteristics of the obstacles.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591646</guid>
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    <item>
      <title>Autopilot simulation of a waterjet-propelled trimaran in waves</title>
      <link>https://trid.trb.org/View/2602512</link>
      <description><![CDATA[Aiming toward a better understanding of the navigation and hydrodynamic performance in real-like complex marine environments, systematic investigations of autopilot path-following of a waterjet-propelled trimaran in regular and short-crested waves were conducted. By using the overset grids for hull motions and waterjet discharge nozzle deflection, aligned with the PID (Proportional Integral Derivative) control algorithm for propeller revolution and the cascade controller for nozzle deflection control, direct simulations of the free-running waterjet-propelled CFD (Computational Fluid Dynamics) model under different wave conditions were performed. The fifth-order Stokes wave was utilized to model the regular wave, while the equi-energy-division and double summation-based method was applied to model the short-crested wave. Simulated results of the path-following with different controller parameters, wavelengths, wave heights, and short-crested waves were analyzed. The results demonstrated that in stern oblique waves, to balance the dramatic fluctuation of the trimaran's trajectory due to the wave-induced instability and large motions, a significant dynamic deflection of nozzle discharge with the maximum angle had reached the limitation, along with the emergence of side hulls and waterjets. The resultant steering effectiveness of the waterjet nozzle varied greatly, directly leading to the difference in the control feedback and trajectories. The research can provide more comprehensive guidance for the direct prediction and evaluation of surface vessel autopilot seaworthiness performance and ship stability in complex wave circumstances.]]></description>
      <pubDate>Tue, 30 Sep 2025 10:23:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602512</guid>
    </item>
    <item>
      <title>A survey on Swedish maritime pilots’ trust, training, understanding, and use of the portable pilot unit’s predictor automation</title>
      <link>https://trid.trb.org/View/2573020</link>
      <description><![CDATA[Technological advances such as electronic charts and course prediction systems provide invaluable support to navigation officers and maritime pilots in navigating confined waters. However, recent maritime accidents have been attributed to operators lacking a clear understanding of how the automation works and how to use it, leading to both misuse and disuse. Two concerns emerge: inadequate training and poor automation design, making it difficult and complex to use. To investigate challenges related to understanding, use, and trust in automation, we surveyed Swedish maritime pilots to investigate their experiences with the course predictor automation tool on their portable pilot units. This technology predicts ship trajectories and is commonly used in modern bridge systems. This paper contributes empirical evidence on how maritime pilots trust the predictor currently used, providing insight into their perceptions and experiences of training, level of understanding, and patterns of usage. The results of 69 respondents revealed limited formal training in the predictor, with knowledge acquired primarily from self-learning and practical experience. Although pilots value the predictor and use it frequently, they struggle with sensor error detection and understanding how it works. The trust in the predictor was inversely correlated with age and experience, with lower age and experience associated with higher trust, more frequent use, greater perceived importance, better understanding, and fewer unexplained behaviours encountered. Based on these findings, recommendations are proposed to improve predictor training and improve its transparency through design.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573020</guid>
    </item>
    <item>
      <title>Sensor data based system identification and modular autopilot control instrumentation of marine craft</title>
      <link>https://trid.trb.org/View/2495734</link>
      <description><![CDATA[This research explores the transformation of non-autonomous leisure marine crafts using a novel modular autopilot control instrumentation. It focuses on leveraging sensor data-based models for understanding the motion dynamics of marine crafts and the strategic integration of PID controllers for heading angle control. The paper addresses the inherent challenges in the marine environment, presenting a comprehensive approach that includes the design, implementation, and validation in a real-world sea trial with the Beneteau Antares 780 Motorboat. The study demonstrates the system’s practical viability through testing on a Vehicle In the Loop setup, showcasing its efficiency and potential for enhancing recreational marine navigation.]]></description>
      <pubDate>Fri, 20 Jun 2025 17:03:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2495734</guid>
    </item>
    <item>
      <title>Manual steering and robust adaptive constrained control of ship autopilot</title>
      <link>https://trid.trb.org/View/2434763</link>
      <description><![CDATA[This study proposes two schemes for ship autopilot: the first is a follow-up mode, applying manual dynamics to the state-space-based underactuated large merchant ship (ULMS) model, using a steering wheel and joystick to emulate manual operation; the second is a Nav mode developed by path-following control. In this mode, a novel path-following control strategy is developed for ULMS with input saturation and delay, two novel auxiliary systems are employed to stabilize the plant with input constraints. The input delay auxiliary system (IDAS) effectively tackles the design challenges posed by input delay, while the input saturation auxiliary system (ISAS) is dedicated to generating actual control inputs under the constraint of saturation. Furthermore, the dynamic surface control (DSC) technique and the roust neural damping technique are introduced to mitigate the computational burden and simplify the controller structure, thereby enhancing the efficiency and performance of the entire control system. Lyapunov stability analysis proves the ship motion system is semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of these two schemes is verified through turning experiment, comparative simulations and a semi-physical simulation platform that integrates physical models with computer simulation technology.]]></description>
      <pubDate>Mon, 07 Oct 2024 16:55:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2434763</guid>
    </item>
    <item>
      <title>Wave Height Prediction in Maritime Transportation Using Decomposition Based Learning</title>
      <link>https://trid.trb.org/View/2389558</link>
      <description><![CDATA[Automation in the area of ship navigation and course planning is adversely affected by oceanic conditions. It leads to deviation from the set course, damages the ship structure, and reduces overall efficiency. Autopilot in ships can keep the ship on course but is unable to choose an efficient path in real-time. Varying wave height is one of the most prominent causes that lead to this inefficiency in the ship’s autopilot system. Current state-of-the-art methods in the domain include building machine and deep learning-based models to estimate wave height. However, the existing systems have several limitations, such as difficulty in handling abrupt non-linear and chaotic variations present in the data, low generalizability, noise sensitivity, and many more. To resolve this issue, the current study proposes a hybrid approach involving a combination of Variable Mode Decomposition and Bidirectional Long Short-Term Memory model (VMD - BiLSTM) and its integration to the ship’s autopilot system. The VMD-based data decomposition enables deep learning models to smoothly and accurately capture observed variational components present in the data, contributing to improved accuracy. Performance comparison with state-of-the-art prediction models validates the efficiency and reliability of the proposed prediction approach.]]></description>
      <pubDate>Thu, 05 Sep 2024 16:26:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389558</guid>
    </item>
    <item>
      <title>Designing an optimal ship course-keeping system for an unknown object model via adaptive dynamic programming approach</title>
      <link>https://trid.trb.org/View/2307750</link>
      <description><![CDATA[The paper discusses the problem of ship autopilot design based on the adaptive (approximate) dynamic programming approach (ADP). The main objective of this paper is to propose the design of a ship's course controller within the framework of computational intelligence techniques, i.e. (in our case) based on model-free optimal regulation. This method is attractive in that it does not require knowledge of the system model to generate optimal process control. The key idea is to iteratively solve the Hamilton-Jacobi-Bellman (HJB) equation based on a simple value function approximator structure. In this way, by introducing such basic elements as evaluation and improvement, one is able to carry out an intelligent optimization process based only on the data received online. Thus, this is a kind of data-driven control methodology. The simulations of the ship course change process confirmed the high efficiency of the proposed controller. An important feature of this approach is also the elimination of the tuning problem.]]></description>
      <pubDate>Sat, 30 Dec 2023 11:28:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2307750</guid>
    </item>
    <item>
      <title>Self-tuning ship autopilot based on the neural network concept: An empirical approach</title>
      <link>https://trid.trb.org/View/2169775</link>
      <description><![CDATA[Ship autopilots are one of the key factors that guarantee the safety and economical efficiency of marine transportation. In this paper, a self tuning ship autopilot based on neural network concept is introduced. A multilayered feed forward neural network with a fixed part and a tunable part is used for tuning the feedback coefficients of a conventional controller. The connections of tunable part are updated without gradient calculation and iteration. No prior information about controlled object parameters is required. The proposed controller is applied to mathematical models of real ships. The designed autopilot performance is validated for operation cases in calm water and sea wave. The comparison to PID controller and some other control techniques is conducted. The results show that proposed concept may be used for designing ship autopilots and successfully applied for course control of real ships.]]></description>
      <pubDate>Wed, 28 Jun 2023 16:57:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2169775</guid>
    </item>
    <item>
      <title>On rudder-roll stabilization autopilot based on response models</title>
      <link>https://trid.trb.org/View/2151066</link>
      <description><![CDATA[This study proposes a rudder-roll stabilization (RRS) autopilot by using novel response models developed by Yasukawa and Yoshimura (YY models) since they include the roll-coupling effect. The statistical optimal control theory which used Akaike information criterion (AIC) was used. To verify the proposed RRS autopilot, numerical simulations based were also performed on the onboard monitoring data of a fishery research vessel; the results confirmed its effectiveness for course keeping and roll reduction.]]></description>
      <pubDate>Fri, 21 Apr 2023 09:49:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2151066</guid>
    </item>
    <item>
      <title>Prediction of Ocean Wave Height Suitable for Ship Autopilot</title>
      <link>https://trid.trb.org/View/2082457</link>
      <description><![CDATA[Ships are usually disturbed by waves when they are traveling at sea. When the waves are large, it is not conducive to driving safety, comfort and economy. Therefore, this paper proposed a new type of automatic driving scheme, which links the wave height prediction with ship driving. By studying the accurate prediction of wave height, ships can adjust their course in real time to ensure that they always travel in the area with the lowest wave height. According to the different driving conditions of ships in the open sea and the offshore sea, the authors designed two wave height prediction models based on LSTM, which are suitable for the above two types of sea areas. In particular, when they created the open sea model, they selected the data of the location points other than the predicted point as the training data. After comparative testing, the two types of models have reached satisfactory accuracy, which provided support for the ship automatic driving scheme proposed in this paper.]]></description>
      <pubDate>Wed, 25 Jan 2023 09:23:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2082457</guid>
    </item>
    <item>
      <title>Design of ship course-keeping system via adaptive control techniques</title>
      <link>https://trid.trb.org/View/2050771</link>
      <description><![CDATA[The paper discusses the problem of ship autopilot design based on the input-output feedback linearization method combined with the adaptive control techniques. At first, the nonlinear ship model of the Norrbin type is linearized with the use of simple system nonlinearity cancellation. Since the exact values of the model parameters are not known to overcome the resulting inaccuracies, these parameters are tuned in such a way as to guarantee the asymptotic stability of the system. Some novelty is that by treating the load disturbance as an additional, unknown parameter of the system, the authors do not need to introduce an extra integral action to deal with the appearing steady-state error. The simulations of the ship course change process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental disturbances. An important feature of this approach is also the elimination of the tuning problem.]]></description>
      <pubDate>Fri, 23 Dec 2022 10:04:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2050771</guid>
    </item>
    <item>
      <title>Parameter identification and application of ship maneuvering model based on TO-CSA</title>
      <link>https://trid.trb.org/View/2064034</link>
      <description><![CDATA[In this study, the crow search algorithm is used for the first time to address the parameter identification problem of the ship motion model in the autonomous navigation of ships. A target-oriented crow search algorithm (TO-CSA) is developed to use in this new search technique to identify model parameters, which greatly improves the speed of convergence, accuracy, and stability of the identification results. The main advantages of this approach over previous research are its improved computational accuracy and precise identification of ship parameters with fewer data or under specific sea conditions, which are in line with actual sailing scenarios. Additionally, the feasibility and efficiency of the created algorithm are evaluated by simulations and actual ship trials, and the engineering applicability of this research is shown based on the effectiveness of ship direction autopilot control.]]></description>
      <pubDate>Wed, 23 Nov 2022 15:47:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2064034</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Grounding and Sinking of Fishing Vessel Sage Catherine Lane, June 9, 2021</title>
      <link>https://trid.trb.org/View/1953273</link>
      <description><![CDATA[​​On June 9, 2021, about 0915 local time, the fishing vessel Sage Catherine Lane was transiting outbound on the St. Marys River, south of Cumberland Island, Georgia, when the vessel grounded on the north jetty of the St. Marys Entrance and, shortly afterward, began to flood. The crew of three abandoned the vessel and were rescued by the crew of a nearby Good Samaritan vessel. The Sage Catherine Lane later sank; about 2,300 gallons of fuel, engine oil, and hydraulic oil were on board, with roughly 800 gallons recovered. A sheen was observed following the breakup of the vessel, 3 days later. One minor injury to a crewmember was reported. Loss of the vessel was estimated at $1 million. The National Transportation Safety Board ​determined that the probable cause of the grounding of the fishing vessel Sage Catherine Lane was the captain’s decision to leave the wheelhouse unattended as the vessel transited the St. Marys Entrance on autopilot, leaving insufficient time to respond when the autopilot failed and caused the vessel to go off the set course.]]></description>
      <pubDate>Fri, 20 May 2022 09:30:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1953273</guid>
    </item>
    <item>
      <title>A Neural Network Based Heading and Position Control System of a Ship</title>
      <link>https://trid.trb.org/View/1939803</link>
      <description><![CDATA[Heading and position control system of ships has remained a challenging control problem. It is a nonlinear multiple input multiple output system. Moreover, the dynamics of the system vary with operating as well as environmental conditions. Conventionally, simple Proportional Integral Derivative controller is used which has well known limitations. Other conventional control techniques have also been investigated but they require an accurate mathematical model of a ship. Unfortunately, accuracy of mathematical models is very difficult to achieve. During the past few decades computational intelligence techniques such as artificial neural networks have been very successful when an accurate mathematical model is not available. Therefore, in this paper, an artificial neural network controller is proposed for heading and position control system. For simulation purposes, a mathematical model with four effective thrusters has been chosen to test the performance of the proposed controller. The final closed loop system has been analysed and tested through simulation studies. The results are very encouraging.]]></description>
      <pubDate>Tue, 19 Apr 2022 13:06:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1939803</guid>
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