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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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    <item>
      <title>Aircraft Catastrophic Failure Prevention Research Program</title>
      <link>https://trid.trb.org/View/2784626</link>
      <description><![CDATA[At least eight tragic civil transport aircraft accidents worldwide since 1979 that resulted in over 1,100 fatalities, prompted Congress to enact the legislation which directed the Federal Aviation Administration (FAA) to establish the Aircraft Catastrophic Failure Prevention Research Program. The legislators were motivated by the fact that in each accident the devastating consequence may have been prevented. Specifically, the Program contained in the Omnibus Reconciliation Act of 1990 (Public Law 101-508) stated that improved methodology and advanced technology be applied to assess and prevent catastrophic failures that can result in civil aircraft accidents. The Program further expanded the FAA's research role which had just recently been broadened under the Aviation Safety Research Act of 1988 (Public Law 100-591). The goal of this program is to prevent catastrophic failures and if prevention is not feasible, to mitigate the effects of such failures to allow for continued controlled flight to a safe landing. This research program is inclusive and addresses all components of a civil aircraft, e.g., turbine engines, airframes, flight control systems, and man-machine interfaces. Safety of flight depends on each component functioning properly and all functioning together as they were designed to do. Thus, a systems approach is followed in the analysis of each component to address cascading of failures by related components. A failure risk assessment is performed for each aircraft system to define threats and vulnerabilities. This analysis is the basic tool used to identify and evaluate deterrent technologies. When technology is not effective in deterring a threat, mitigation measures are applied to avoid a catastrophic event. The program plan is presented in four sections. Section One provides background information and a technical overview of the program. Section Two deals with the prevention of catastrophic turbine engine failures and/or the isolation of flight critical systems and occupants from such failures. Section Three concentrates on airframe design, manufacturing, and maintenance technologies which can prevent or mitigate the effect of structural failures. Section four address the prevention of loss of critical flight control system functions after a failure and determines possible design concepts that will provide alternate means of control. The human interface is also addressed within the context of flight control system research.]]></description>
      <pubDate>Mon, 12 Oct 2026 12:00:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784626</guid>
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    <item>
      <title>When Driving Automation Exceeds Human Ability: Authoritative Control Interventions and their Implications for Human-Machine Interaction</title>
      <link>https://trid.trb.org/View/2736402</link>
      <description><![CDATA[Advances in artificial intelligence and driving automation are fostering a paradigm shift in which technical systems exhibit cognitive and operational capabilities that surpass human performance. These developments have prompted proposals to grant automated systems greater control and authority, including allowing the automation to act authoritatively, fully blocking or taking away control from human users. While such delegation may promise improvements in safety and efficiency, it also carries the risk of disempowering humans if not carefully managed. This tension underscores a central challenge for human–machine interaction (HMI) design: maintaining operator agency and trust when interacting with automated systems that demonstrate ability beyond that of humans. The concept of Control Management, originating from aerospace, offers a pathway for balancing authority between human and automation by defining system policies that guide its architecture, mechanisms, and HMI design. In this conceptual work, we examine the theoretical foundations of human-automation collaboration and describe emerging challenges for driver interaction with authoritative automation. This work contributes: (1) a conceptual classification of authoritative control interventions across automation levels and control authority transition types, (2) Control Management-informed HMI guidelines spanning Avoid, Alert, Inform, Recover, and Return intervention states, (3) ethical implications for preserving driver agency, and (4) research directions for system development, policymakers, and HMI designers.]]></description>
      <pubDate>Thu, 08 Oct 2026 09:06:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736402</guid>
    </item>
    <item>
      <title>Large language model based interactive decision-making for autonomous driving</title>
      <link>https://trid.trb.org/View/2785004</link>
      <description><![CDATA[In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly conservative behaviors, lack proactive interaction, and consequently suffer from limited public acceptance. To mitigate intent misunderstandings and decision failures, we present a large language model based interactive decision-making framework that augments scene understanding and intent-aware interaction to jointly improve safety and efficiency. The approach uses Object-Process Methodology to semantically model complex multi-vehicle scenes, abstracting low-level perceptual data into objects, processes, and relations, thereby streamlining reasoning over latent causal structure. Building on this representation, the large language model parses both explicit and implicit intents of surrounding agents and, under jointly enforced safety and efficiency constraints, selects candidate maneuvers. We further generate perturbed trajectory candidates via Monte Carlo sampling and evaluate them to obtain an optimized executable trajectory. To foster transparency and coordination with nearby road users, the final decision is translated by the large language model into concise natural-language messages and broadcast through an external human-machine interface, completing a closed loop from scene understanding to action to language. Experiments in a cluster driving simulator demonstrate that the proposed method outperforms traditional baselines across safety, comfort, and efficiency metrics, while a turing-test-style evaluation indicates a high degree of human-likeness in decision making. Besides, these results suggest that coupling semantic scene abstraction with large language model mediated intent reasoning and language-based eHMI communication offers a practical pathway toward interactive, trustworthy autonomous driving in dense mixed traffic.]]></description>
      <pubDate>Tue, 06 Oct 2026 15:52:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2785004</guid>
    </item>
    <item>
      <title>Human-in-the-loop COLREGs-compliant platoon formation control of maritime autonomous surface ships via finite state machine</title>
      <link>https://trid.trb.org/View/2782470</link>
      <description><![CDATA[This paper investigates the human-in-the-loop platoon formation control problem of maritime autonomous surface ships (MASSs) under the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). A human-in-the-loop virtual leader is introduced to incorporate supervisory human commands into the platoon navigation, such that human operators can indirectly regulate the sailing speed of the entire MASS platoon through speed inputs. To achieve path following and coordinated platoon formation, a cooperative path-following guidance law based on the line-of-sight method is developed for multiple MASSs along a parameterized path. To ensure safe navigation in compliance with COLREGs, a finite state machine is constructed to identify encounter situations, and safety constraints are formulated using control barrier functions. By solving a quadratic optimization problem, the optimal collision-avoidance surge velocity and yaw rate are obtained, enabling effective obstacle avoidance and collision prevention while preserving platoon navigation performance. Simulation results demonstrate the effectiveness of the proposed approach in achieving human-supervised platoon path following and COLREGs-compliant collision avoidance for MASSs.]]></description>
      <pubDate>Thu, 01 Oct 2026 11:44:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782470</guid>
    </item>
    <item>
      <title>Human–Technology Risk Interactions in Maritime Accidents: Tree-Augmented Naïve Bayes-Based Assessment for Maritime Autonomous Surface Ships in Japan’s Short-Sea Shipping</title>
      <link>https://trid.trb.org/View/2784379</link>
      <description><![CDATA[Maritime autonomous surface ships are increasingly considered to address labour shortages and minimise human error in short-sea shipping. However, quantitative evidence on accident risk structures reflecting human–technology interactions remains limited. This study analysed Japan Coast Guard accident investigation forms for domestic cargo ships and tankers in Japanese coastal waters (2009–2024), focusing on collision and grounding cases. Risk-influencing factors including environmental conditions, vessel characteristics, equipment, operator attributes, steering mode, lookout method, and human-factor categories were modelled using a tree-augmented naïve Bayes network. Sensitivity analysis based on mutual information and evidence-setting inference identified factors associated with accident severity. Collision severity was primarily associated with human-factor categories, with damaging substances, communication errors, maintenance, and leadership failures linked to higher probabilities of restricted or disabled navigation, whereas the navigation-error category is more commonly associated with minor outcomes. Grounding accidents more often resulted in severe outcomes, and AIS carriage was associated with a lower probability of disabled navigation. These findings suggest that autonomous or decision-support technologies may reduce some high-incidence, low-severity risks by complementing navigational decision-making and lookout-related functions. Preventing severe outcomes also requires organizational safety management, shore-side information-sharing and warning/intervention arrangements, supporting cautious and phased definition of the operational design domain.]]></description>
      <pubDate>Mon, 28 Sep 2026 16:45:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784379</guid>
    </item>
    <item>
      <title>Determining What’s Next: Visual Analytics for Evaluating Human-Automation Coordination</title>
      <link>https://trid.trb.org/View/2754344</link>
      <description><![CDATA[As advanced automated systems become more complex, there is a greater need to analyze the difficulty of human-automation coordination. We developed a novel visual analytics tool to evaluate human-automation coordination by examining the relationship between projection, explanation (retrojection), and the understanding of current events through time. The What’s Next Diagram visualizes demands on human sensemaking as operators answer Wiener’s key automation surprise question: what will automation do next. The tool can assess whether operators can answer the question directly and quickly given the design and architecture of human-automation systems. It can be used as a model of multi-agent coordination, as a tool for post-mortem analysis, or an aid for design. We illustrate how the tool can be used to identify system requirements for anomaly response in the aviation domain.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2754344</guid>
    </item>
    <item>
      <title>The Use of Computational Modeling and Simulation to Design and Evaluate a Distributed Work System</title>
      <link>https://trid.trb.org/View/2754342</link>
      <description><![CDATA[Designing integrated systems of humans and automation involves envisioning how work will be performed, how it is affected by technological capabilities, and what will be helpful to support system performance. This is challenging as interactions and dynamics can create emergent effects that are difficult to predict. This demonstration aims to demonstrate the power of computational simulation in designing, verifying, and validating integrated systems of humans and automation. The demo will include an introduction to the simulation framework Work Models that Compute (WMC) used for our ongoing research, how to develop a work model in WMC, a demo of the simulation, and a demo of postprocessing visualization to analyze the simulation results. The approach can be applied in all stages of system design. With an example in aviation, we show how we use computational modeling and simulation to evaluate the robustness of envisioned operations of humans working with automated systems.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2754342</guid>
    </item>
    <item>
      <title>An Integrated Display of Fluid Human Systems Interactions</title>
      <link>https://trid.trb.org/View/2581149</link>
      <description><![CDATA[Supporting drivers in different levels of automation was one of the key goals in the HADRIAN project. Following the approach of a “fluid interface”, i.e., an interface that considers the state of the driver, vehicle, and environment, and which uses different modalities to support the driver, a human–machine interface (HMI) was developed and compared to a baseline HMI in a simulator study (n = 39). The integrated fluid HMI aimed at supporting the driver in automated driving in SAE level 2 and 3 by providing mode relevant information, supporting the driver during take-over requests by the system, and supporting engagement in non-driving related tasks when allowed. The fluid HMI consisted of several components (head-up display, LEDs, haptic icons, sound, tablet) and featured driver monitoring and adaptive tutoring. Study results did not show significant differences between the HMIs regarding subjective measures such as user experience, usability, acceptance, or safety feeling. The various factors contributing to this conclusion are thoroughly discussed. However, objective measures in terms of eye movements and a safety analysis including driving data showed a significant benefit of the integrated fluid HMI over the baseline HMI. Participants had better mode awareness and a higher safety score with the integrated fluid HMI. Furthermore, valuable insights on how to further improve the HMI could be gained during the study.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:36:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581149</guid>
    </item>
    <item>
      <title>Control logic verification of safety of the intended functionality for maritime autonomous surface ships based on Event-B: A case of autonomous berthing scenario</title>
      <link>https://trid.trb.org/View/2728291</link>
      <description><![CDATA[Despite rapid advancements in intelligent shipping technologies, the commercial operation of Maritime Autonomous Surface Ships (MASS) is still on the way due to the great concerns about the safety and reliability of the designed functional logic. Specifically, as MASS evolves towards remote control or autonomous operation, safety issues are shifting from component failures to logical flaws of Safety of the Intended Functionality (SOTIF) at the system level. To this end, this study tries to propose a technical tool to verify functional logic involved in the designed plans by means of integrating System-Theoretic Process Analysis (STPA) with Event-B, a formal method based on set theory and stepwise refinement. The autonomous berthing scenario of MASS is exampled in this study, where STPA is employed to qualitatively derive hierarchical safety constraints from the identified unsafe control actions. Then these constraints are formalized in Event-B via hierarchical refinement to rigorously verify the functional logic. As a result, the proposed tool can effectively expose logical flaws in the autonomous berthing control procedure, which continuously guides the optimization of the functional logic of MASS berthing by considering various possible berthing operational conditions until all the SOTIF requirements are satisfied. Possessing inherent generalizability, this tool is extensible to other complex MASS operational scenarios, such as obstacle avoidance and autonomous departure.]]></description>
      <pubDate>Fri, 25 Sep 2026 10:51:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728291</guid>
    </item>
    <item>
      <title>A Virtual Reality-Based Evaluation of Human-in-the-Loop Connected Cruise Control (hC3)</title>
      <link>https://trid.trb.org/View/2775487</link>
      <description><![CDATA[This study investigated the effectiveness of a Human-in-the-Loop Cooperative Cruise Control (hC3) system in improving vehicle driving performance. Twenty participants experienced both manual and hC3 driving modes in realistic traffic situations by using a virtual reality (VR) based simulation framework. The study measured and evaluated different driving performance metrics, such as energy consumption, safety, and subjective user feedback under all of the experimental conditions. Findings showed the hC3 mode greatly improved the longitudinal stability. Under hC3 operation, root mean square (RMS) acceleration was reduced by 28%, average time gap by 17.6% and fuel consumption by 26.1% over those of manual driving. Participants mentioned that the immersive VR environment contributed to the realism and accuracy of the assessment of human-machine interactions. Overall, the results indicated the superiority of hC3 over human driving in terms of driving consistency, energy efficiency, and comfort, as well as the feasibility of VR-based simulation in promoting the research of intelligent vehicle systems.]]></description>
      <pubDate>Thu, 24 Sep 2026 17:08:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775487</guid>
    </item>
    <item>
      <title>Trust-Building in L4 Automation by Intent Communication: Using HMI Design to Examine Passengers' Trust and Acceptance of Automated Vehicles [supporting dataset]</title>
      <link>https://trid.trb.org/View/2775411</link>
      <description><![CDATA[The rapid advancement of automated vehicle (AV) technology requires effective communication between AVs and their passengers to foster trust, perceived safety, and acceptance. This project explores how intent communication via Human-Machine Interfaces (HMIs) affects passenger trust in SAE Level 4 AVs. Using a driving simulator, two interface designs will be tested for their ability to convey vehicle sensing, navigation, and control intent. Outcomes include quantitative and qualitative data on passenger trust, usability, and technology acceptance, with the potential to inform HMI development for broader AV deployment. The findings aim to enhance AV adoption, improve passenger experience, and support safer integration of automated systems into transportation networks.]]></description>
      <pubDate>Thu, 24 Sep 2026 17:08:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775411</guid>
    </item>
    <item>
      <title>Trust-Building in Level 4 Automation by Intent Communication: In-Vehicle HMI Design, Passenger Trust, and Visual Attention</title>
      <link>https://trid.trb.org/View/2775410</link>
      <description><![CDATA[At SAE Level 4 automation the vehicle occupant becomes a passenger with no driving role, no fallback obligation, and no expectation of takeover. The passenger cannot intervene with the driving and thus must rely on trust. Here, it is the in-vehicle human-machine interface (HMI) that is the primary channel through which the vehicle can make its perception and intent legible. Passenger-facing HMI is sparsely researched relative to external interfaces and to drivers at lower automation levels. This project addressed that gap in three tasks. A systematic review following PRISMA searched seven databases, screened 3,364 records, and synthesized 37 studies on how in-vehicle HMI modality, content, and quality shape passenger trust. Guided by that synthesis, three in-vehicle HMI conditions (auditory, visual, and multimodal) were built in Unity and integrated with a high-fidelity driving simulator, that communicated the vehicle's perception and intent five seconds before every maneuver. This was experimentally evaluated with thirty-six participants who rode as rear-seat passengers through a seven-minute Level 4 drive containing eight scenarios, with survey and head-mounted eye-tracking measures. Reported trust increased significantly from pre-drive to post-drive in all three conditions. The multimodal interface produced significantly higher trust and perceived system transparency than the visual interface, with no penalty in workload or usability. HMI modality shifted visual attention only toward the in-vehicle display, and trust was negatively correlated with time spent monitoring that display. Passenger scanning remained anchored on the forward roadway and varied with the driving scenario rather than with the interface. Communicating intent builds passenger trust irrespective of channel, and redundant multimodal delivery builds the most trust.]]></description>
      <pubDate>Thu, 24 Sep 2026 17:08:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775410</guid>
    </item>
    <item>
      <title>Eliciting operational requirements for transparent adaptive automation strategies in air traffic control</title>
      <link>https://trid.trb.org/View/2719269</link>
      <description><![CDATA[Adaptive automation is increasingly explored in air traffic control as a means to manage variability in operational demand while preserving controller engagement, situation awareness, and authority. This paper reports the COntroller adaptive Digital Assistant (CODA) contribution on making adaptive task delegation governable in supervisory work. We specify an adaptive automation strategy for non-critical air traffic control support tasks that combines explicit mode logic, authority contracts, and interface mechanisms that render delegation inspectable at the point of action. Adaptation is formalised as a closed-loop policy driven by three trigger classes—traffic demand, task/load demand, and an operator-readiness dimension—computed over both current and short-horizon predicted states to anticipate demand peaks and to support timely, bounded transitions. The strategy operationalises three graduated proactivity modes with explicit authority management: Manual (monitor-and-inform), Proactive-Light (recommend-and-confirm), and Proactive-Strong (initiate-and-notify with veto), designed to support appropriate reliance and maintain continuous legibility of task ownership. We detail delegation rules that constrain automation to supportive functions, and we present a human–machine interface that externalises automation state, proposed actions, and ownership while incorporating concise, actionable rationale cues intended to support controllability with minimal overhead. Finally, structured workshops with expert controllers are synthesised to derive operational acceptability constraints on delegation boundaries, transition timing, preview horizons, and explanation selectivity, grounding the design in the realities of air traffic control work.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:56:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2719269</guid>
    </item>
    <item>
      <title>Can you fly it? Flight performance of novices and system designers with a simplified vehicle operations concept</title>
      <link>https://trid.trb.org/View/2714418</link>
      <description><![CDATA[The concept of Simplified Vehicle Operations (SVO) is proposed to reduce input complexity and training time for future Advanced Air Mobility (AAM) pilots. So far, flight controllers and input concepts for SVO are often evaluated using computational simulations or in small-scale experiments with professional test pilots. This paper presents findings from a quantitative user study on the flight performance of n = 31 novices and n = 7 system designers in the execution of standardized Mission Task Element (MTEs) in a flight simulator. These MTEs include complex transition phases with a switch from rotary-wing to fixed-wing flight and vice versa. Absolute values from multiple subjective, performance, and input-based parameters indicate the general adequacy of the tested SVO concept, even for beginners. At the same time, multiple parameters also show significant differences between the two groups. The paper discusses these findings and SVO concepts in general to improve the future design of SVO concepts for minimally trained pilots.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:56:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714418</guid>
    </item>
    <item>
      <title>A Hierarchical Shared-Control Framework for Distributed Drive Electric Vehicles Considering Driving Ability and Energy Efficiency</title>
      <link>https://trid.trb.org/View/2734964</link>
      <description><![CDATA[To address the problem of personalized and ability-aware shared control for distributed drive electric vehicles (DDEVs), this article proposes a hierarchical shared-control framework that simultaneously accounts for driving ability and energy efficiency. First, a driving-ability metric is constructed by jointly considering path-tracking accuracy and handling stability, and a long short-term memory (LSTM) network-based short-horizon predictor is developed to capture its rapid variations under dynamic conditions. Then, an adaptive model predictive control (MPC) scheme is designed to coordinate the driver, active front-wheel steering (AFS), and direct yaw moment control (DYC) within a multiagent formulation, dynamically adjusting multiobjective weights and authority allocation according to the predicted ability. At the lower layer, an energy-aware Nash-game torque vector allocation (TVA) strategy is introduced, where each in-wheel motor minimizes its own energy loss while satisfying global longitudinal and yaw-moment demands. Driver-in-the-loop experiments demonstrate that the proposed strategy significantly improves the level of human-machine cooperation, achieving approximately a 14.5% improvement in tracking accuracy and a 4.6% improvement in energy efficiency while maintaining vehicle stability.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:56:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2734964</guid>
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