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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Trajectory Tracking Control for UUVs Based on Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2777837</link>
      <description><![CDATA[Unmanned Underwater Vehicles (UUVs) operate in complex and uncertain environments, which require a suitable controller. While traditional PID controllers are widely used, they often have slow response speed and inadequate disturbance rejection, particularly under complex and uncertain conditions. To overcome these shortcomings, this paper introduces the DDPG-DLPID, an adaptive motion controller, including a Deep Deterministic Policy Gradient (DDPG) reinforcement learning that can acquire the parameters of PID controllers. In this paper, we design two loops: the inner loop handles velocity regulation, and the outer loop handles position and attitude. By using DDPG, the system can efficiently adjust the PID parameters of both loops in real time, allowing it to effectively adapt to environmental changes and achieve optimized requirements. To evaluate the controller, we design the following scenarios, including straight-line and complex path-following tasks. Compared with single-loop PID and dual-loop PID controllers, the proposed DDPG-DLPID approach achieves faster response and higher tracking accuracy, while substantially reducing tracking errors under interference conditions. Physical experiments under three conditions - straight-line voyage, attitude maintaining, and depth control - were further carried out to validate the strategy’s real-world applicability. Experimental data confirm that DDPG-DLPID has better performance when compared with both traditional PID and dual-loop PID controllers across all test scenarios.]]></description>
      <pubDate>Sun, 04 Oct 2026 19:22:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2777837</guid>
    </item>
    <item>
      <title>Axial vibration reduction and energy harvesting for marine thrust bearings based on BNES-EMS considering bistable behavior</title>
      <link>https://trid.trb.org/View/2782394</link>
      <description><![CDATA[The marine propulsion shaft experiences continuous axial vibration induced by the non-uniform wake field of the propeller. Although this vibration affects navigation safety, it may potentially serve as a source of vibration energy. A bistable nonlinear energy sink system integrated with an electromagnetic energy harvester (BNES-EMS) is designed to achieve synergistic vibration reduction and energy harvesting. A three-mass dynamic model incorporating negative stiffness and cubic nonlinear stiffness is established to accurately capture the bistable behavior and reveal the targeted energy transfer. Numerical simulations are conducted to evaluate the vibration mitigation and energy harvesting performance of the proposed integrated BNES-EMS using measured vibration excitation. The results demonstrate that the BNES-EMS effectively redirects vibrational energy from the primary system to the absorber, generates substantial output voltage, and achieves superior displacement amplitude reduction. Parametric discussions indicate that increasing absorber mass enhances both vibration suppression and energy harvesting, while coil turns has an optimum for power output. Furthermore, bistable topology coupling analysis elucidates the operational adaptability of low-barrier narrow-well versus high-barrier wide-well configurations under varying operating conditions. This study provides a theoretical basis for the parameter optimization and practical application of the integrated vibration reduction and energy harvesting device for marine thrust bearings.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782394</guid>
    </item>
    <item>
      <title>Dynamic analysis and response experimental study of a 6-UPU-RRP serial-parallel wave compensation mechanism under micro-disturbance with 3-DOF excitations</title>
      <link>https://trid.trb.org/View/2782409</link>
      <description><![CDATA[This study addresses end-payload offset in offshore operations caused by a ship's pitch, yaw, and heave motions under low sea states (sea state 4 and below). a scaled 6-UPU-RRP serial-parallel wave compensation mechanism. Forward and inverse kinematics models are established, with small-amplitude nonlinear solutions simplified via equivalent infinitesimal substitution. A Newton-Euler-based dynamic model of the compensation chain is built to clarify correlations among excitation variables, compensation variables, and component accelerations. Scaled multi-parameter kinematic analysis explores the effects of excitation parameters on end-effector motion and compensation variables. An integrated test bench enables co-simulations and hardware-in-the-loop experiments under harmonic excitations representative of sea state 4, analyzing dynamic responses and joint bending moments, with PID parameters tuned to enhance performance. Quantitative experiments yield a normalized sensitivity ranking of pitch joint driving torque as β (1.00) > f (0.68) > Δz₀ (0.45) > γ (0.32). Under the tested harmonic excitations, the mechanism ensures high end-payload stability and precision, with position scatters within approximately ±15 mm, providing a validated experimental reference for scaled wave compensation under low-sea-state equivalent excitations. Extension of these findings to irregular wave conditions would require further investigation with realistic wave spectra.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782409</guid>
    </item>
    <item>
      <title>Adaptive Region Distillation: A lightweight knowledge distillation method for ship fuel consumption prediction</title>
      <link>https://trid.trb.org/View/2782414</link>
      <description><![CDATA[Accurate ship fuel consumption prediction is essential for energy efficiency optimization, route planning, and emission reduction. However, interactions among operational and environmental factors produce local fluctuations and heteroscedasticity across operating conditions, making it difficult for lightweight onboard models to preserve predictive structures learned by high-capacity models.Existing regression knowledge distillation methods mainly align point predictions or global predictive distributions, providing limited representation of local and relative structure in continuous output spaces. To address this limitation, this paper proposes Adaptive Region Distillation (ARD), a knowledge distillation framework with region decoupling for ship fuel consumption regression. ARD constructs adaptive proxy distributions around observed targets and decouples knowledge transfer into target-neighborhood and complementary-neighborhood objectives. ARD was evaluated on operational records from two container ships and one cargo ship using MLP and LSTM backbones. Relative to the standalone Student, ARD increased mean R² by 0.0308–0.0870 for MLP and 0.0070–0.0954 for LSTM. Relative MAPE reductions were 16.2%–33.5% for MLP, while absolute reductions for LSTM were 0.7256–2.1721 percentage points. Ablation and sensitivity analyses characterized component contributions and hyperparameter responses. ARD introduces no additional parameters or branches at inference time, retaining parameter compression ratios of 167.67× and 252.17× for the MLP and LSTM students, respectively.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782414</guid>
    </item>
    <item>
      <title>Numerical study of unsteady aerodynamic interference in dual-helicopter/ship flow fields</title>
      <link>https://trid.trb.org/View/2782418</link>
      <description><![CDATA[To clarify the unsteady aerodynamic interference in simultaneous shipboard dual-helicopter operations, a high-fidelity Computational Fluid Dynamics (CFD) model based on Improved Delayed Detached-Eddy Simulation (IDDES) was established for a Wasp-class ship under headwind, Green 30° wind, and Red 30° wind conditions. The flow evolution, aerodynamic-force histories, spectral characteristics, and coherence of the coupled flow fields were systematically analyzed. The results show that the coupled flow is governed by the interaction of the bow vortex, deck-edge vortex, superstructure wake vortex, and rotor-induced vortex, while wind direction changes their relative importance. Under headwind conditions, the fore-aft asymmetry is most evident, and the maximum rotor-lift difference reaches approximately 25 kN, corresponding to a 17.6% reduction for the trailing helicopter due to the leading-helicopter wake and downwash. Spectral analysis shows dominant low-frequency components from 0.4 to 1.2 Hz, together with a persistent blade-passing frequency peak near 23.87 Hz. Under Green 30° and Red 30° winds, the dominant interference shifts toward the deck-edge vortex and leading-helicopter wake, producing more discrete multi-band spectral and coherence characteristics. Overall, inflow direction and deck position significantly affect dual-helicopter aerodynamic interference and should be considered in deck scheduling and safe operating limit assessment.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782418</guid>
    </item>
    <item>
      <title>A hierarchical spatio-temporal reinforcement learning framework for cooperative multi-USV search and interception</title>
      <link>https://trid.trb.org/View/2782404</link>
      <description><![CDATA[Multi-USV cooperative search and target interception holds value in maritime security, yet existing methods face three challenges: multi-timescale decision coupling, insufficient heterogeneous observation fusion, and inefficient exploration under sparse rewards. This paper proposes a multi-USV decision-making framework based on Hierarchical Spatio-Temporal Proximal Policy Optimization (HST-PPO) with three contributions. A three-layer spatio-temporal decision architecture spanning strategic, tactical, and execution layers is coupled via soft goal passing and trained end-to-end. A Hierarchical Causal Observation Fusion Network (HCOF-Net) uses causal mask attention and temporal decay encoding to adaptively fuse four categories of observations. A Distributed Random Network Distillation exploration mechanism (DRND-NovelD) combines ensemble novelty estimation with a multi-agent diversity reward to accelerate convergence under sparse rewards. HST-PPO is validated across Python simulation, Unity high-fidelity simulation, and real-world lake trials, and benchmarked against ten baselines spanning six non-hierarchical MARL methods and four hierarchical RL methods adapted to the same setting. HST-PPO achieves a Search Coverage Rate of 82.3% and Interception Success Rate of 80.0%, surpassing the strongest baseline by 1.7 and 1.1 percentage points and outperforming every hierarchical baseline. In real-world trials, HST-PPO retains 96.2% and 81.3% of its simulated Search Coverage Rate and Interception Success Rate, validating its zero-shot sim-to-real transfer capability.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782404</guid>
    </item>
    <item>
      <title>Optimal kite trajectory generation for ship traction: A comparison of numerical optimal control methods and a case study</title>
      <link>https://trid.trb.org/View/2782403</link>
      <description><![CDATA[This paper presents an overview of optimal trajectory generation for kite systems applied to ship traction, along with an illustrative case study. Motivated by the growing need for sustainable maritime propulsion, this paper begins by reviewing the fundamental principles of kite-assisted systems, their components, and the main challenges associated with their modeling and control. A special focus is placed on the optimal control formulation of the trajectory generation problem, highlighting general optimization strategies and transcription methods. Four major numerical approaches, GPOPS-II, ICLOCS2, CasADi, and MUSCOD, are critically reviewed, emphasizing their suitability for kite systems. A detailed case study is then conducted using CasADi combined with multiple shooting and IPOPT, with justification provided for this methodological choice. The obtained simulation results are analyzed, with particular attention to the trade-off between traction performance and energy consumption. The optimized figure-eight trajectory yields an average traction force of 980.35kN for a 500 m² kite, corresponding to a wing loading of 1.96kN/m², with winch power consumption representing approximately 6% of the average mechanical power generated. A sensitivity analysis with respect to the true wind angle further confirms the robustness of the proposed approach. Finally, the paper identifies key research gaps and outlines future directions to improve the efficiency and robustness of kite-based ship traction systems.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782403</guid>
    </item>
    <item>
      <title>Hand position-based L1-model predictive control for robust trajectory tracking of underactuated surface vehicles</title>
      <link>https://trid.trb.org/View/2782393</link>
      <description><![CDATA[This paper proposes a hand position-based L₁-MPC framework for robust trajectory tracking of underactuated unmanned surface vehicles under environmental disturbances and model uncertainties. In maritime missions such as hydrographic surveys, environmental monitoring, docking, and dynamic positioning, accurate regulation is often required at a mission-relevant point, such as a sensor location or the bow tip, rather than at the center of gravity (CoG). Moreover, because underactuated surface vehicles have no direct sway control input, CoG-based dynamics contain unmatched sway uncertainty, limiting direct disturbance compensation. To address both aspects, this work adopts the hand position, an established output-redefinition concept defining a virtual point ahead of the CoG along the vehicle centerline, as the control reference. Expressing the dynamics at the hand position converts the sway disturbance unmatched at the CoG into matched uncertainty at the control point, enabling effective L1 adaptive compensation despite the vehicle underactuation. This enables the proposed framework to combine constraint-aware MPC with L₁ adaptive augmentation. MPC regulates the hand position while handling vehicle dynamics and input constraints, whereas the L1 controller compensates for disturbances and model uncertainties in real time. Simulation and field experiments demonstrate improved trajectory-tracking performance over the evaluated baseline methods.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782393</guid>
    </item>
    <item>
      <title>LARL: LOS-guided Attention-Residual Learning for robust trajectory tracking of underactuated USVs</title>
      <link>https://trid.trb.org/View/2782383</link>
      <description><![CDATA[Accurate trajectory tracking of underactuated unmanned surface vehicles (USVs) is challenging because of nonlinear coupled dynamics, unavailable lateral actuation, model uncertainty, and environmental disturbances. This paper proposes a LOS-guided Attention-Residual Learning (LARL) framework that integrates line-of-sight (LOS) guidance with deep reinforcement learning for robust USV trajectory tracking. LOS-generated desired-heading and tracking-error information is incorporated into a structured state–reward formulation, while the 13-dimensional observation is organized into five physically meaningful tokens and processed by a multi-head attention-residual actor–critic controller based on deep deterministic policy gradient (DDPG). The framework is evaluated in simulation under disturbance-free, wind–wave–current, dynamic-parameter-perturbation, and actuator-dynamics conditions, with PID, sliding mode control, DDPG, and TD3 as baselines. Across 12 trajectory–scenario combinations, LARL achieves the lowest RMSE in 10 cases and the lowest MAE in 11 cases, and shows consistent performance across five independent training seeds. Additional tests on unseen ellipse, racetrack, and Gerono trajectories further demonstrate generalization to new path geometries. Field experiments on a self-developed USV verify practical deployability: in rectangular trajectory tracking, LARL reduces MAE by 14.21% and mean absolute rudder rate by 26.55% relative to PID, while maintaining comparable overall tracking performance. These results demonstrate the effectiveness and practical potential of the proposed structured LOS-informed attention-residual learning framework.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782383</guid>
    </item>
    <item>
      <title>Spectrum-regularized deep learning with joint wave parameter prediction for sea state estimation from ship motions</title>
      <link>https://trid.trb.org/View/2782381</link>
      <description><![CDATA[Real-time in-situ sea state estimation is crucial for ship operations. However, existing data-driven methods often struggle with physical inconsistency, limited continuous parameter estimation, and poor robustness under sensor degradation. To address these challenges, this paper proposes a deep learning method that integrates spectrum-based self-consistency regularization to jointly estimate significant wave height (Hₛ), wave direction (Dir), and peak period (Tₚ) from ship motion data, while providing an auxiliary sea state class (Class) output. The proposed model predicts Hs, Tₚ, and the spectrum in parallel using a shared dual-domain encoder and three cross-attention branches, where spectral relations couple the outputs. During training, spectral algebraic relation serves as auxiliary self-consistency losses, with a delayed introduction strategy to balance spectral regularization and data-driven objectives. Six ablation experiments examine the effects of the main components and training choices. Based on privately owned high-fidelity simulated data, the obtained results show that compared with the data-driven baseline, the proposed method reduces Hₛ MAE by 15.53% and improves auxiliary classification accuracy by 5.61pp. It also shows lower sampling-rate sensitivity to downsampling, with 1.3% degradation at 1 Hz, compared with 30.2% for the baseline. Ablation results further show that spectral regularization mainly improves Hₛ estimation, delayed introduction improves the balance among the prediction targets, and soft labels improve the auxiliary classification output.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782381</guid>
    </item>
    <item>
      <title>A radar–vision fusion approach for ship monitoring in bridge waterways using weakly supervised cross-modal representation learning</title>
      <link>https://trid.trb.org/View/2782369</link>
      <description><![CDATA[Frequent ship–bridge collisions pose serious risks to infrastructure, public safety, and economic assets. Reliable ship perception is critical for timely risk identification in bridge waterways. Radar–vision fusion enables complementary ship sensing capabilities beyond single-sensor monitoring using cameras or radars alone. However, existing rule-based fusion methods exhibit limited applicability in continuous waterway monitoring due to poor generalisation and labour-intensive calibration. This study proposes a weakly supervised representation learning framework that achieves embedding-level alignment with reduced manual supervision to improve the robustness of radar–vision association-based ship monitoring. A time window-based pseudo-labelling strategy is introduced to guide a Transformer-based dual encoder for learning shared radar–vision representations, with a novel ordinal loss incorporated into contrastive learning to enforce physical consistency. The framework is validated on a real-world bridge waterway using a field-deployed radar–camera system, achieving an F1-score of 95.7% and outperforming state-of-the-art learning-based fusion methods in the traffic sensing domain. The proposed approach can provide reliable and automated situational awareness of bridge waterway traffic to support decision-making in navigational safety management systems.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782369</guid>
    </item>
    <item>
      <title>MBSE-based development and verification of function-oriented ODDs for autonomous ships</title>
      <link>https://trid.trb.org/View/2782367</link>
      <description><![CDATA[Safe development and operation of autonomous ship functions depend on whether their operating conditions can be explicitly defined, quantified, verified, and monitored. This study proposes a model-based systems engineering (MBSE) method for developing and verifying function-oriented operational design domains (ODDs). Regulatory requirements, mission requirements, and operational risks are transformed into observable and decidable ODD conditions, with traceability established to functional architecture, physical deployment, verification scenarios, function-activation logic, and safe modes. The generic method comprises an ODD metamodel and an integrated design-time, test-time, and runtime verification process. Autonomous collision avoidance is used as a case study to instantiate the method. Its ODD is decomposed into environmental, positioning and perception reliability, communication, manoeuvring capability, collision-decision, and adaptive-safety conditions. These conditions are formalised as Object Constraint Language (OCL)-like constraints and Boolean ODD eligibility-gate (ODD_Gate) rules. Static model checking, deployment-consistency checking, integrated risk analysis, and representative scenario simulations are conducted to assess ODD formalisation, architecture mapping, function-activation control, and safe-mode switching. The results support the consistency of ODD-condition binding, deployment relationships, eligibility-gate evaluation, and safe-mode selection within the prescribed case-study inputs. The evidence is limited to model-level verification and does not establish the operational validity of the thresholds or collision-avoidance safety in real maritime traffic.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782367</guid>
    </item>
    <item>
      <title>Autonomous safety control of unmanned surface vessels formation driven by prescribed performance</title>
      <link>https://trid.trb.org/View/2782365</link>
      <description><![CDATA[Aiming at the problem of high-precision cooperative tracking safety control of multiple unmanned surface vessels (USVs) in complex marine environments, a formation control framework that balances formation tracking performance, anti-interference capability, and safety is proposed. In this framework, a prescribed performance control method with monotonically adjustable prescribed performance boundaries is used to generate the desired control signals, enabling the USVs to track the desired trajectory of a virtual leader while satisfying transient and steady-state error constraints during tracking. These monotonic boundary constraints reduce overshoot in formation position tracking errors, thereby minimizing collisions among USVs in the initial stage. Then, considering model uncertainty and external environment disturbances, an integral disturbance observer is designed to achieve synchronous estimation of compound disturbances and is embedded into a backstepping control framework to construct a cooperative controller with stronger performance assurance capabilities. Finally, the control obstacle function (CBF) is used to describe the safety state set of the USVs, and the corresponding quadratic programming problem is established. The closed-form solution of the minimum intervention safety controller is derived through the Karush-Kuhn-Tucker condition. Based on the closed-form solution, an auxiliary system is constructed to realize the adaptive update of performance function. Theoretical analysis shows that the algorithm can ensure the system security under the condition of conflict between formation tracking and security, and realize formation tracking under performance constraints when there is no conflict. The simulation results demonstrate that compared with other prescribed performance function strategies, this control scheme has obvious advantages in response speed, immunity, input smoothness, and safety, and possesses good engineering application value.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782365</guid>
    </item>
    <item>
      <title>Parametric inversion of equivalent sea-ice bending-strength based on shipboard measurements and discrete element simulations</title>
      <link>https://trid.trb.org/View/2782364</link>
      <description><![CDATA[Sea-ice bending strength is a key parameter in ship–ice interaction analysis and ice-load prediction, but the strength corresponding to full-scale measured load cases cannot be obtained simultaneously during shipboard monitoring. Besides, mechanical properties of natural sea ice have strong randomness. Therefore, identification of the equivalent sea-ice bending-strength corresponding to the measured ice loads is of practical importance. A parameter inversion framework combining shipboard measurements and discrete element simulations is proposed. The compressive-to-bending strength ratio constraint is used to reduce the dimensionality of the inversion, while the Progressive Refinement Search (PRS) strategy is employed to organize candidate parameters. The equivalent parameter corresponding to the measured load condition is inverted using the error between simulated and measured ice loads. Numerical validation shows that the maximum error is less than 4%. Full-scale validation based on Xuelong ice-load data shows that the inverted bending strength is 0.35 MPa, which lies within the reference range estimated from the empirical ice temperature–strength relationship (0.24–0.42 MPa). The average relative error of the three load-characteristic indicators is 13.4%. The proposed method provides a feasible approach for inverting equivalent sea-ice bending strength from measured ice loads.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782364</guid>
    </item>
    <item>
      <title>FuelEU maritime compliance in a tramper dry cargo fleet: An annual multi-voyage assessment</title>
      <link>https://trid.trb.org/View/2782360</link>
      <description><![CDATA[The FuelEU Maritime regulation introduces a well-to-wake (WtW) greenhouse gas (GHG) intensity limit for energy used onboard ships, reshaping compliance strategies in European maritime transport. This study evaluates 2025 compliance for a five-vessel tramper dry cargo fleet using disaggregated voyage-level MRV records and separately reported port-stay fuel consumption. The dataset covers 61 voyages and 143,903 nautical miles, with voyage-specific geographic scope weighting and fuel-specific WtW emission factors. All four vessels with non-zero FuelEU-scoped energy exceed the 2025 GHG intensity limit under their observed fossil-fuel mix, while vessel-level scoped-energy exposure ranges from 0% to 57.28%. The fleet compliance deficit is −81.97 tCO2eq, corresponding to a FuelEU penalty of €52,620. Counterfactual HVO blending shows that a fleet-wide B3 share is sufficient for 2025 compliance across the tested waste-based pathways. Fuel-mix-preserving demand reduction leaves attained FuelEU GHG intensity unchanged but reduces the absolute compliance deficit, FuelEU penalty exposure, and EU ETS allowance requirements. Combined HVO and demand-reduction cases show complementary regulatory effects, while alternative pooling configurations demonstrate the importance of surplus allocation. The results are case-specific but provide empirical evidence on how voyage geography, fuel mix, and regulatory scope shape FuelEU compliance in heterogeneous tramper operations.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782360</guid>
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