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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Population Evacuation in Motion: Harnessing Disaster Evolution for Effective Dynamic Emergency Response</title>
      <link>https://trid.trb.org/View/2659121</link>
      <description><![CDATA[This study introduces a robust Dynamic Population Evacuation (DPE) framework in response to the escalating challenges in wildfire-urban interface regions. The DPE model elevates emergency response strategies by seamlessly integrating traffic simulation, real-time mobility tracking, optimization systems leveraging advanced algorithms, and cloud-based communication. The offline component of the framework focuses on pre-disaster preparation by optimizing the allocation of evacuation shelters and generating initial route plans for impacted populations, incorporating changing on-the-ground hazard conditions and geography. The online phase dynamically reallocates shelters and provides real-time guidance for vehicle navigation. This approach significantly improves the efficiency and safety of evacuation processes by utilizing advanced algorithms and cloud infrastructure. Key innovations include an offline planning phase, optimizing shelter allocation and route plans with a keen eye on evolving hazards and geography. Extensive testing and simulations of the DPE framework, including models of real-world evacuation scenarios such as the Tubbs fire in California, validate the proposed approach and demonstrate significant improvement in efficiency, responsiveness, and safety for populations relative to traditional evacuation planning methods and frameworks. The study further underscores the broader applicability of the DPE model to enhance resilience and outcomes in urban evacuations not only for wildfires but also for other hazards such as floods and earthquakes.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659121</guid>
    </item>
    <item>
      <title>On the Classification of Vehicle Cut-In Scenarios Severity: Empirical Evidence to Validate the Severity Classification of UN R157</title>
      <link>https://trid.trb.org/View/2686157</link>
      <description><![CDATA[The Fuzzy Safety Model (FSM), developed amongst all to support UN Regulation No. 157 on Automated Lane Keeping Systems (ALKS), provided a novel methodology for distinguishing between avoidable and non-avoidable cases of certain test scenarios for Automated Vehicles (AVs). ALKSs, restricted to highway environments, must avoid any reasonably foreseeable and preventable accident. However, beyond this capability, the FSM may also be an optimal tool to classify the difficulty level of the same traffic scenarios. To validate the FSM's ability to classify preventable scenarios according to their difficulty level, a test campaign was conducted focusing on the critical “cut-in” scenario, where another vehicle changes lanes in front of the ALKS, requiring it to decelerate to avoid a collision. The study demonstrates the feasibility of the required tests and the FSM's effectiveness in categorising preventable cases by difficulty level. Results highlight the model's potential to plan, execute, and analyse cut-in scenarios beyond the scope of UN R157. This contribution supports the impartial assessment of AVs while addressing the challenge of representing diverse and challenging traffic conditions with a limited number of tests. The research results underscore the FSM's broader applicability for improving AV safety testing frameworks.]]></description>
      <pubDate>Fri, 03 Apr 2026 12:13:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686157</guid>
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    <item>
      <title>Driving Risk Field Model and Its Application in Trajectory Planning: A New Perspective</title>
      <link>https://trid.trb.org/View/2610670</link>
      <description><![CDATA[Driving risk field (DRF) emerges as an effective way to assess the driving safety of connected and automated vehicles (CAVs). Most existing DRF models are established from the so-called birds-eye-view (BEV), which limits their accuracy for distributed vehicle-level tasks such as trajectory planning since the interactions between ego vehicle (EV) and its surrounding traffic environment have not been fully considered. To fill this research gap, we establish a novel DRF model from ego-vehicle-view (EVV) and apply it in trajectory planning in this paper. Firstly, the collision boundary between EV and its surrounding obstacles is defined by introducing the elliptical model to fully consider the geometry characteristics of vehicles. Secondly, the relative motion influence coefficient is designed to accurately characterize the relative motion between EV and obstacles, instead of using only basic driving state information such as location and velocity. On this basis, the unified DRF is established from EVV for driving safety assessment, which contains vehicle risk field (VRF) and lane marking risk field (LMRF). Based on the established DRF model, we then design a rolling trajectory planning method (RTPM) with a rolling horizon strategy, which not only ensures a long prediction horizon but also effectively reduces the computational complexity. Multiple simulation results under different traffic scenarios jointly verify the accuracy and applicability of the proposed RTPM and DRF model established from this new perspective.]]></description>
      <pubDate>Thu, 26 Mar 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610670</guid>
    </item>
    <item>
      <title>Waterway-BEV: Generate Bird’s Eye View Layouts of a Waterway From a First-Person View Camera Using Cross-View Transformers</title>
      <link>https://trid.trb.org/View/2561863</link>
      <description><![CDATA[In the domain of autonomous ship navigation, the construction of bird’s-eye view (BEV) layouts for waterways has obvious significance. A helmsman can generate the BEV layout of the waterway using his/her eyes only. To simulate this intelligence, a novel neural network-based algorithm named Waterway-BEV is proposed, which enables reconstructing a local map formed by the waterway layout and ship occupancies in the bird’s-eye view given a first person view monocular image only. Waterway-BEV employs an efficient SEResNeXt encoder to extract features from first person view (FPV) monocular images, capturing deep semantic information related to waterways and ships. Due to the variations in information across different perspectives, Waterway-BEV incorporates a Cross-View Transformation Module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. To fully leverage the feature output of the SEResNeXt encoder, Waterway-BEV employs a decoder based on a dedicated lightweight network. This decoder is responsible for decoding the enhanced bird’s-eye view (BEV) feature maps and generating the BEV layout. By employing the Focal Loss as the loss function for model optimization, Waterway-BEV takes into account the quantity and classification difficulty of ship samples during the training process, thereby improving the generation performance and convergence speed. The experiments demonstrated that Waterway-BEV achieved notable performance metrics, with mIOU and mAP rates reaching 97.8% and 98.2%, respectively, in waterway bird’s-eye view layout generation. Waterway-BEV outperformed other state-of-the-art (SOTA) algorithms in generating BEV layouts of waterways. In particular, during specialized scenarios such as crossroads of waterways and tasks involving small target ships, Waterway-BEV consistently generated satisfactory bird’s-eye view layouts, demonstrating robustness and applicability.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561863</guid>
    </item>
    <item>
      <title>Predicting Vehicle Yielding Intentions for Automated Driving Interaction</title>
      <link>https://trid.trb.org/View/2613244</link>
      <description><![CDATA[The growing integration of connected and automated vehicles (CAVs) with human-driven vehicles (HVs) presents challenges in traffic efficiency and safety during cooperative lane-changing (CLC). Accurately predicting HV yielding intentions is crucial but remains difficult due to limited short-term interaction data. To address this, we propose a method leveraging long-distance HV trajectory data from roadside units (RSUs). This method combines driving style calibration and a style-based learning model to predict yielding intentions with high accuracy and reliability. Experimental results demonstrate a 94% prediction accuracy, surpassing traditional methods by 8%, with a computation time of 45.6 ms per vehicle. The proposed framework enables personalized intention prediction, robust adaptability across scenarios, and real-time applicability, contributing to safer and more efficient CLC interactions.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613244</guid>
    </item>
    <item>
      <title>Research on Takeover Behavior in Autonomous Vehicles at Complex Urban Intersections</title>
      <link>https://trid.trb.org/View/2613090</link>
      <description><![CDATA[When driving Level 3 autonomous vehicles, drivers do not need to constantly monitor operations; however, engaging in non-driving-related tasks (NDRTs) during autonomous phases impairs takeover performance, posing safety risks, especially in complex intersection scenarios. This study employs a multi-agent interaction driving simulator to model takeover events. It integrates simulator data with driver physiological responses to analyze the impact of takeover warning time, NDRT types, and interactions with other road users on takeover performance. Four key metrics—reaction time, normalized pupil diameter, average saccadic velocity, and subjective risk perception—are evaluated for their applicability in assessing takeover behavior. The results show that no single metric suits all scenarios, and metric selection should consider scenario-specific characteristics. Findings provide a theoretical foundation for enhancing the safety and reliability of automated vehicle technology.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613090</guid>
    </item>
    <item>
      <title>Towards Safe Human–Machine Interaction in Remotely Controlled Ships: A System-Theoretic Risk Analysis Framework</title>
      <link>https://trid.trb.org/View/2654575</link>
      <description><![CDATA[With the rapid advancement of Maritime Autonomous Surface Ships (MASS), the complexity of onboard automation and remote operations has significantly increased, placing greater demands on the safety and reliability of Human-Machine Interaction (HMI). Ensuring safe navigation under varying levels of autonomy requires a structured and comprehensive assessment of HMI-related risks. This study proposes a novel risk-informed safety framework for HMI in remotely controlled MASS, particularly those operating at Degree of Autonomy 2 (DoA2). By integrating Systems-Theoretic Process Analysis (STPA) with the Human Factors Analysis and Classification System (HFACS), the framework systematically identifies unsafe interactions, causal factors, and control structure vulnerabilities across multiple functional levels. The approach captures both technical failures and human factors, offering a holistic view of HMI safety. A case study of DoA2 ships demonstrates the applicability and effectiveness of the proposed STPA-HFACS framework in visualising unsafe scenarios and tracing their root causes. The findings highlight key areas for risk mitigation through targeted technological improvements and enhanced operator training. This research contributes a structured methodology for MASS HMI safety assessment and provides practical guidance for risk management in semi-autonomous ship operations.]]></description>
      <pubDate>Wed, 28 Jan 2026 14:43:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2654575</guid>
    </item>
    <item>
      <title>Further Application of Pitch Independent Laser Doppler Velocimeter in Land Vehicle Autonomous Navigation</title>
      <link>https://trid.trb.org/View/2651921</link>
      <description><![CDATA[The strapdown inertial navigation system (SINS) and pitch-independent laser Doppler velocimeter (PI-LDV) integration represents a traditional navigation architecture. However, its effectiveness in obtaining precise altitude measurements remains constrained by the PI-LDV's inherent limitation of providing only one-dimensional velocity information. This study addresses this limitation by using the optical path structure of the PI-LDV to construct a frame capable of providing two-dimensional velocity information. To achieve this objective, two innovative integration methods are proposed: a SINS/PI-LDV loosely coupled integration method and a SINS/PI-LDV tightly coupled integration method, both of which consider the influence of potential laser beam fluctuations. Furthermore, a displacement increment measurement model is developed for the SINS/PI-LDV integrated navigation system to maximize the utilization efficiency of PI-LDV measurements while reducing the impact of sensor noise and outliers. The effectiveness of the proposed methods is rigorously validated through a comprehensive series of experimental tests, including: 1) extended-duration, long-distance tests using high-precision inertial measurement units (IMUs); 2) short-duration, limited-range evaluations using high-precision IMUs; and 3) two additional long-distance verification experiments using both high-precision and medium-precision IMUs. Experimental results demonstrate that the proposed method significantly outperforms traditional methods, particularly in height accuracy. Notably, the performance advantages become more pronounced when implementing the SINS/PI-LDV integrated navigation system with medium-precision IMUs, suggesting enhanced practical applicability in cost-sensitive applications.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651921</guid>
    </item>
    <item>
      <title>FEMASF: An SVD-Based Algorithm for Accurately Estimating the Mounting Angle and Scale Factor</title>
      <link>https://trid.trb.org/View/2553307</link>
      <description><![CDATA[Accurately estimating the position and attitude of vehicles is essential for intelligent transportation systems. The GNSS/INS integrated system offers precise navigation information. However, the system’s positioning errors may accumulate rapidly in challenging GNSS signal conditions. Odometer (ODO) and nonholonomic constraints (NHC) are commonly employed to mitigate the rapid accumulation of INS errors. Compensating for the mounting angles of INS and the scale factor of the odometer is necessary to fully exploit the potential of ODO/NHC. However, many studies employ Kalman filters with small mounting angle assumption, which limits their applicability for large mounting angles in practice. To accurately estimate the mounting angle of INS with any installation attitude, we propose a new algorithm called Fast Estimation of Mounting Angle and Scale Factor (FEMASF). FEMASF employs Singular Value Decomposition (SVD) to obtain a closed-form solution for the parameters. It also incorporates an enhanced Sage-Husa scheme, enhancing overall estimation accuracy by reducing the weight of outlier data through a forgetting factor. Extensive simulation experimental results demonstrate that our proposed FEMASF algorithm outperforms filter-based methods in terms of accuracy and convergence speed for large mounting angles. Specifically, for the -90° mounting angle, FEMASF achieves 0.45° angle error, while the velocity-based Kalman filter (VKF) fails to converge and the position-based Kalman filter (PKF) yields about 4° error. Furthermore, neither VKF nor PKF converges for the 120° mounting angle, whereas FEMASF exhibits only about 3.2° estimation error.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553307</guid>
    </item>
    <item>
      <title>Recent advances in fault diagnosis of ship integrated power systems: A review</title>
      <link>https://trid.trb.org/View/2614981</link>
      <description><![CDATA[With the continuous expansion and complexity of Integrated Power Systems (IPS) on ships, even localized faults can trigger cascading failures. Therefore, reliable and real-time fault diagnosis is a critical component for ensuring navigation safety. This review highlights major developments in IPS fault diagnosis over the past decade (since 2016, covering 79 references), with a focus on four representative methodological frameworks: model-based, data-driven, knowledge-based, and hybrid-based. The review also outlines emerging technologies and their current applications. Related research has been preliminarily validated on various full-scale ships, demonstrating promising engineering applicability. In reviewing the evolution of diagnostic methods, the paper identifies three pressing challenges: difficulties in data acquisition and imbalance, limitations in sensing and onboard computing capacity, and reduced diagnostic reliability under coupled fault scenarios. To address these issues, this review outlines future research directions, including enhancing data quality and intelligent preprocessing, leveraging data augmentation, transfer learning, and unsupervised modeling to mitigate sample scarcity and label deficiency, and developing modeling and inference frameworks tailored to coupled fault conditions. This review aims to provide theoretical guidance and practical reference for the design and optimization of fault diagnosis techniques for integrated power systems on ships.]]></description>
      <pubDate>Fri, 05 Dec 2025 14:08:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614981</guid>
    </item>
    <item>
      <title>Real-Time Resilient Tracking Control for Autonomous Vehicles Through Triple Iterative Approximate Dynamic Programming</title>
      <link>https://trid.trb.org/View/2512338</link>
      <description><![CDATA[Enhancing control precision, mitigating external disturbances, and ensuring real-time responsiveness stand as the cornerstone of autonomous vehicle tracking endeavors, each of which intricately interwoven to uphold operational safety. In pursuit of addressing these issues, this paper presents a triple iterative control method inspired by approximate dynamic programming (ADP) tailored for real-time disturbance avoidance. The control framework orchestrates simultaneous iterations of value function, control policy, and disturbance policy, engineered to optimize tracking control amidst external disturbances cast as a zero-sum differential game, tackled adeptly through deep neural networks. Rigorous mathematical proof underpins its triple iteration, coupled with assurances of residual error convergence, solidifying its safety guarantee ability and algorithmic resilience. To validate its effectiveness, both numerical simulations and experiments on a real micro-vehicle platform were conducted. Results underscore the feasibility of this new method, showcasing its energy-saving capability and a four-times acceleration compared to conventional model predictive control (MPC) approaches when confronted with lateral disturbances. Notably, the single-step calculation time of this method on the Raspberry Pi is only 1.44ms, affirming its practical viability and real-world applicability.]]></description>
      <pubDate>Fri, 17 Oct 2025 16:49:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512338</guid>
    </item>
    <item>
      <title>Progress towards multidimensionally scalable assisted and/or automated ship navigation and control - part I: reliable and autonomous guidance - a contradiction?</title>
      <link>https://trid.trb.org/View/2582942</link>
      <description><![CDATA[The automation of vessels is becoming increasingly important, developing from human dominated command structures through partial automation to the long-term goal of fully autonomous ship command and control. If fully autonomous vessels are realised, the guarantee of at least equally reliable and safe ship guidance is crucial. Part I of the paper addresses key questions: How can the safety and reliability of autonomous navigation be improved through online accessible methods? Can established risk assessment methods be extended to maintain functionality without compromising safety? New ideas for improved guidance are proposed, using a recently published method for assessing reliability based on probability of detection given the basis for developing a reliable guidance unit that balances adaptive behaviour with safety goals. Also addressed are the limitations of object detection approaches, especially in terms of practical applicability. The solution idea presented includes a reliability- and task-based functional degradation to guarantee safety despite functional restrictions. Overall, the contribution demonstrates improvements showing new methods in the development of highly automated ship guidance systems, which have the potential to be widely used through certified capabilities, while part II addresses progress in integrating human capabilities into automated and assisted systems.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582942</guid>
    </item>
    <item>
      <title>Control of Autonomous Aerial Vehicles: Advances in Autopilot Design for Civilian UAVs</title>
      <link>https://trid.trb.org/View/2579156</link>
      <description><![CDATA[Control of Autonomous Aerial Vehicles is an edited book that provides a single-volume snapshot on the state of the art in the field of control theory applied to the design of autonomous unmanned aerial vehicles (UAVs), aka “drones”, employed in a variety of applications. The homogeneous structure allows the reader to transition seamlessly through results in guidance, navigation, and control of UAVs, according to the canonical classification of the main components of a UAV’s autopilot. Each chapter has been written to assist graduate students and practitioners in the fields of aerospace engineering and control theory. The contributing authors duly present detailed literature reviews, conveying their arguments in a systematic way with the help of diagrams, plots, and algorithms. They showcase the applicability of their results by means of flight tests and numerical simulations, the results of which are discussed in detail. Control of Autonomous Aerial Vehicles will interest readers who are researchers, practitioners or graduate students in control theory, autonomous systems or robotics, or in aerospace, mechanical or electrical engineering.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579156</guid>
    </item>
    <item>
      <title>Development of a Modular ROS-Enabled Pedestrian Intention Prediction Architecture for AVs Maneuvering Control</title>
      <link>https://trid.trb.org/View/2512322</link>
      <description><![CDATA[In this work, the problem of predicting a pedestrian’s intention to cross the road is addressed using visual data from a camera. The proposed ROS-based modular architecture consists of four modules: Visual-Perception, Intention Prediction, and Planning and Control Modules. The Visual Perception module is divided into three sub-modules. The pedestrian detection is responsible for detecting the pedestrian and analyzing his motion and looking states. The lane detection is responsible for analyzing the structured environment which helps in the road state classifiers. The third sub-module aims to extract some curvilinear localization states that are essential for the vehicle’s motion planning and control. The intention prediction module captures the pedestrian’s intention to cross the road. A comparative study is conducted between three different data-driven sequential models. Each model is trained on the JAAD dataset and different extracted features from the visual perception module. The proposed GRU model obtained an 86% average f1-score, anticipating the pedestrian’s intention two seconds in advance when the pedestrian is standing, and three seconds in advance when the pedestrian is walking to the crossing area. To control the maneuver of the vehicle, longitudinal velocity and lateral controllers are implemented to control the motion of the vehicle while avoid collision with the pedestrian based on the intention prediction. Finally, this work is verified on a 1:4 scaled real vehicle to ensure the applicability of implementing this work in real hardware.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:55:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512322</guid>
    </item>
    <item>
      <title>Scalable link cost learning for real-time route guidance systems</title>
      <link>https://trid.trb.org/View/2572094</link>
      <description><![CDATA[Real-time Route Guidance Systems (RGS) are a critical component of Intelligent Transportation Systems (ITS), designed to provide users with optimal routes by responding to real-time traffic data. Central to RGS is the link cost function, which quantifies the generalized cost of traversing road network links. Existing methods primarily rely on travel time and geometry of links, overlooking latent factors that significantly influence user experience. Additionally, processing millions of links in large-scale networks to account for these factors poses substantial computational challenges. As a solution, the authors employ a scalable, learning-based framework to estimate link costs by learning latent factors from user responses with multi-armed bandit (MAB). The latent factors are modeled as weights representing the general inconvenience of traversing the links. User non-compliance with guided routes serves as key evidence of a discrepancy between RGS’ information and the real-world’s latent factor. Real-time GPS data is used to estimate travel times, which are adjusted by the link weights to compute link costs to provide real-time routes to users. The authors' methodology was implemented in Kakao Mobility’s real-time RGS, a leading mobility platform in South Korea, and demonstrated across a national-scale network. Online evaluation proved the methodology’s scalability and effectiveness, processing millions of sample trips while enhancing multiple metrics to evaluate the RGS performance. Particularly, for 11.59 % of cases where route guidance differed from the baseline, the compliance rate increased from 64.22 % to 70.87 %. Moreover, the authors' method integrates with custom adjustments commonly applied by RGS operators, ensuring compatibility with existing RGS and empirical applicability.]]></description>
      <pubDate>Thu, 28 Aug 2025 17:16:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572094</guid>
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