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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>
    <image>
      <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>Situation Awareness in Fast Rescue Crafts Operators—A Simulator Study</title>
      <link>https://trid.trb.org/View/2658641</link>
      <description><![CDATA[This study investigated whether experience in maritime operations contributed to situation awareness (SA) and confidence among Fast Rescue Craft (FRC) operators during simulated maritime search and rescue (SAR) missions. A total of 20 novice and 20 experienced Canadian Coast Guard personnel were presented with collision avoidance scenarios of various difficulty levels on a desktop FRC simulator. A goal-directed task analysis (GDTA) was conducted to identify the critical goals, decisions, and information requirements underpinning FRC operations, providing a structured basis for scenario design and SA measurement. The results indicated that experienced operators had significantly higher Total SA scores. These differences were primarily attributable to stronger performance on Level 3 SA across all scenarios and Level 2 SA in head-on scenarios. Experienced participants also reported higher confidence in Level 1 and Level 2 SA, although no differences were found in Level 3 or Total SA confidence. Experienced operators’ navigation decisions were influenced by informal decision-making cues, especially when interpreting collision-avoidance regulations. The absence of significant differences in Level 3 SA confidence and Total SA confidence between experienced and novice operators suggests that the latter may be overconfident in predicting future events in complex maritime environments. To better prepare novice operators for real-world SAR operations, these findings suggest the potential value of training interventions that focus on specific SA components, particularly projection, and support the development of decision-making strategies under uncertainty.]]></description>
      <pubDate>Tue, 21 Apr 2026 08:28:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658641</guid>
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    <item>
      <title>Perception’s Role for Mental Model Formation in Automated Driving: Insights From Four Studies</title>
      <link>https://trid.trb.org/View/2611055</link>
      <description><![CDATA[The rapid development of driving automation systems (DAS) in the automotive industry aims to support drivers by automating longitudinal and lateral vehicle control. As vehicle complexity increases, it is crucial that drivers comprehend their responsibilities and the limitations of these systems. This work investigates the role of the driver’s perception for the understanding of DAS by cross-analysing four empirical studies. Study I investigated DAS usage across different driving contexts via an online survey conducted in Germany, Spain, China, and the United States. Study II explored contextual DAS usage and the factors influencing drivers’ understanding through a Naturalistic Driving Study (NDS), followed by in-depth interviews. Study III employed a Wizard-of-Oz on-road driving study to simulate a vehicle offering Level 2 and Level 4 DAS, paired with pre- and post-driving interviews. Study IV following up used a Wizard-of-Oz on-road driving study to simulate Level 2 and Level 3 DAS and subsequent in-depth interviews. The findings from these studies allowed the identification of aspects constituting a driver’s understanding and factors influencing their perception of DAS. The identified aspects and factors were consolidated into a unified conceptual model, describing the process of how perception shapes the driver’s mental model of a driving automation system.]]></description>
      <pubDate>Wed, 31 Dec 2025 15:48:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611055</guid>
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    <item>
      <title>Bridging the Gap on the Bridge: Seafarers’ Tasks and Decision-Making With DSS in Energy-Efficient Route Planning</title>
      <link>https://trid.trb.org/View/2577064</link>
      <description><![CDATA[Closing energy efficiency gaps in shipping requires understanding seafarers’ operations in route planning and their preferences for assistive technology. The objective of this research was to systematically examine seafarers’ decision-making to inform human-centered decision support systems (DSSs). The authors conducted a hierarchical task analysis based on guidelines and expert interviews (Study 1, N = 3) and assessed key tasks using seafarers’ expectancy-value-cost ratings (S2, N = 65) via online surveys. Tidal and weather routing tasks were rated highest for energy efficiency, despite associated costs. The authors further examined psychological need satisfaction and preferences for human versus automated control, finding autonomy satisfaction rated significantly lower than other needs; seafarers preferred automated information processing but retained human control over decisions. Finally, post-route planning interviews using the critical decision method (S3, N = 22) highlighted the complexity of balancing goals, particularly safety, and emphasized practical experience as key in route planning and system use. All studies underscored the need for high transparency and controllability in system information and functions. The research emphasizes understanding seafarers’ perceptions of energy-efficient operations and integrating automated support into current processes.]]></description>
      <pubDate>Fri, 29 Aug 2025 16:51:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577064</guid>
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    <item>
      <title>Explaining Automated Vehicle Behavior at an Appropriate Abstraction Level and Timescale to Maintain Common Ground</title>
      <link>https://trid.trb.org/View/2536245</link>
      <description><![CDATA[Automation is becoming increasingly complex, playing a larger role in driving and expanding its operational design domain to dynamic urban roads. Explainable AI (XAI) research in computer science aims to craft explanations of automation that help people understand the behavior of complex algorithms. However, many XAI approaches rely on fixed-format explanations, which may not effectively support drivers with varying levels of automation knowledge and tasks with different timescales. Maintaining common ground is a multilevel process, in which individuals and automation must adjust communication format and abstraction based on knowledge and time constraints. The authors first draw on existing research to suggest that common ground is a shared understanding between drivers and automation that requires constant maintenance. They applied the abstraction hierarchy (AH) modeling method, which describes complex systems across multiple abstraction levels to match drivers’ cognitive capacity. The authors modified it to translate vehicle and traffic data into multilevel explanations of automation behavior. They expanded the model into the abstraction–decomposition space, naming it the Driver–Automation Teaming model, designed to generate explanations that account for task timescale. With this modified model, they developed three human–machine interface concepts to demonstrate how it can improve XAI’s support for driver–automation collaboration.]]></description>
      <pubDate>Tue, 29 Apr 2025 13:35:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2536245</guid>
    </item>
    <item>
      <title>Human-Automation Trust Development as a Function of Automation Exposure, Familiarity, and Perceived Risk: A High-Fidelity Remotely Operated Aircraft Simulation</title>
      <link>https://trid.trb.org/View/2536244</link>
      <description><![CDATA[Trust development will play a critical role in remote vehicle operations transitioning from automated (e.g., requiring human oversight) to autonomous systems. Factors that affect trust development were collected during a high-fidelity remote uncrewed aerial system (UAS) simulation. Six UAS operators participated in this study, which consisted of 17 trials across two days per participant. Trust in two highly automated systems were measured pre- and post-study. Perceived risk and familiarity with the systems were measured before the study. Main effects showed performance-based trust and purpose-based trust increased between the pre- and post-study measurements. System familiarity predicted process-based trust. An interaction indicated that operators who rated the systems as riskier showed an increase in a single-item trust scale between the pre- and post-study measurement, whereas participants that rated the systems as less risky maintained a higher trust rating. Individual differences showed operators adapted to why the automation was being used, and trust improved between measurements. Qualitative analysis of open-ended responses revealed themes related to behavioral responses of the aircraft and transparency issues with the automated systems. Results can be used to support training interventions and design recommendations for appropriate trust in increasingly autonomous remote operations, as well as guide future research.]]></description>
      <pubDate>Tue, 29 Apr 2025 13:35:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2536244</guid>
    </item>
    <item>
      <title>Failures in Driving Automation Systems: Definitions, Taxonomy, and Prevention Mechanisms</title>
      <link>https://trid.trb.org/View/2452709</link>
      <description><![CDATA[Skraaning and Jamieson’s (2023) article defines, provides examples, and offers a taxonomy for automation failures. They also invite others to apply their concepts to other domains. Driving automation systems along with their myriad of failures provide the perfect test case. This article defines, characterizes, and discusses prevention mechanisms for driving automation system failures. By combining Skraaning and Jamieson’s (2023) original taxonomy with characterizations and prevention mechanisms of driving automation system failures, their work is extended and substantiated.]]></description>
      <pubDate>Thu, 16 Jan 2025 09:09:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452709</guid>
    </item>
    <item>
      <title>How Are Automation Failures Characterized in the Driving Domain? Insights From a Scoping Review</title>
      <link>https://trid.trb.org/View/2452708</link>
      <description><![CDATA[The authors agree with Skraaning and Jamieson’s assertion that the “failure” construct is not always clearly defined in human-automation interaction research. They applied the proposed taxonomy to explore how failures have been characterized in driving automation research based on a recent scoping review they conducted. The authors discuss the insights gained and the challenges of using the taxonomy to characterize driving automation failures: (1) Utilizing the taxonomy confirmed that driving automation research is limited in failure scenarios tested. (2) Applying the taxonomy to empirical studies on driving automation is challenging due to limited information on underlying failure mechanisms. (3) Failures can be difficult to classify due to the complexity of technology and environmental factors. (4) Researchers and designers should know failure mechanisms, but drivers will not.]]></description>
      <pubDate>Thu, 16 Jan 2025 09:09:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452708</guid>
    </item>
    <item>
      <title>A Taxonomy for AI Hazard Analysis</title>
      <link>https://trid.trb.org/View/2452704</link>
      <description><![CDATA[With the rise of artificial intelligence in safety-critical systems like surface transportation, there is a commensurate need for new hazard analysis approaches to determine if and how AI contributes to accidents, which are also increasing in number and severity. The original Swiss Cheese model widely used for hazard analyses focuses uniquely on human activities that lead to accidents, but cannot address accidents where AI is a possible causal factor. To this end, the Taxonomy for AI Hazard Analysis (TAIHA) is proposed that introduces layers focusing on the oversight, design, maintenance, and testing of AI. TAIHA is illustrated with real-world accidents. TAIHA does not replace the traditional Swiss Cheese model, which should be used in concert when a joint human-AI system exists, such as when people are driving a car with AI-based advanced driving assist features.]]></description>
      <pubDate>Thu, 16 Jan 2025 09:09:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452704</guid>
    </item>
    <item>
      <title>Cognitive Skills for Flight Path Management</title>
      <link>https://trid.trb.org/View/2417227</link>
      <description><![CDATA[This research provides a current benchmark of the cognitive skills and cognitive processes needed for flight path management (FPM) in current commercial air transportation flight operations. While some cognitive skills for aviation have been identified, it remains unclear which skills are most pertinent for different phases of flight, for different tasks, across different aircraft types, and during operational complexity. Further, there is concern that flight deck automation may contribute to cognitive skill degradation. Two expert pilots participated in cognitive walkthroughs to establish a current benchmark of the cognitive skills and cognitive processes needed for FPM. The tasks involved seven different phases of flight and two different aircraft; the results from two phases are reported—Preflight Briefing and Initial Climb. The findings indicate nineteen cognitive skills, and three metacognitive skills are used by pilots for FPM. In addition, the cognitive process models needed for FPM are all very similar, regardless of the aircraft type, task, phase of flight, or increased operational complexity. These results provide a foundation for future efforts on cognitive skill degradation, training of FPM cognitive skills, and may be used to inform the design of new automated systems to support pilot cognition.]]></description>
      <pubDate>Thu, 12 Sep 2024 09:17:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417227</guid>
    </item>
    <item>
      <title>The Influence of Agent Transparency and Complexity on Situation Awareness, Mental Workload, and Task Performance</title>
      <link>https://trid.trb.org/View/2372916</link>
      <description><![CDATA[Transparency is a design principle intended to make the inner workings of autonomous agents visible to end-users such that humans can evaluate the reasoning behind its decisions and actions. To test the effect of agent transparency on situation awareness (SA), mental workload, and task performance, an experiment was performed where 34 nautical navigators were tasked with interpreting the information provided by an autonomous collision and grounding avoidance system. Sixteen traffic situations were created with two levels of complexity. Four levels of transparency varied the amount and type of information in terms of the system’s decisions, planned actions, reasoning, and input parameters. The results show that increased transparency improves SA without increasing mental workload. However, the time to comprehend the system’s decisions and planned actions increased when its reasoning was depicted. Traffic complexity impaired SA, mental workload, and time-to-comprehension regardless of transparency level. However, for level 2 SA, transparency was found to negate the influence of complexity, resulting in improved comprehension of the agent’s reasoning despite high traffic complexity. These outcomes demonstrate the merits of agent transparency as a design principle in supporting human supervision of autonomous agents. However, developers should take care when extending these principles to time-critical applications.]]></description>
      <pubDate>Wed, 26 Jun 2024 14:12:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2372916</guid>
    </item>
    <item>
      <title>Three Player Interactions in Urban Settings: Design Challenges for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2222706</link>
      <description><![CDATA[An observational analysis of crossing episodes between two intersecting vehicles, in which a third road user clearly affected its evolution, was conducted in an attempt to identify (i) recurring patterns of informal coordination among road users and (ii) traffic situational invariances that may inform AV prediction algorithms. The term BLOCK-EXPLOITING is introduced to describe a driver’s exploitation of situational opportunities to gain priority often contrary to regulatory provisions, but favouring overall traffic efficiency. Video-data from an urban stop-controlled intersection were analysed through the lens of joint systems theory using a phenomenological framework developed in this study. Four generic types of BLOCK-EXPLOITING were identified (i.e. covering, ghost-covering, piggybacking, sneaking). Covering and ghost-covering led to minimal or no delays while piggybacking and sneaking, although abusive to other drivers, still only resulted in 1.99 to 3.33 sec delay. It is advocated that BLOCK-EXPLOITING can be socially acceptable. Proposed design challenges for AVs in mixed traffic include the ability to (i) distinguish BLOCK-EXPLOITING from errant driving, (ii) recognise to whom a ‘space-offering’ is addressed, and (iii) assess the appropriateness or abusiveness of a BLOCK-EXPLOITING action. Finally, this study brings to fore very short-time span joint-activity coordination requirements among diverse agents unknown to each other]]></description>
      <pubDate>Thu, 21 Sep 2023 08:58:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2222706</guid>
    </item>
    <item>
      <title>Where Failures May Occur in Automated Driving: A Fault Tree Analysis Approach</title>
      <link>https://trid.trb.org/View/2162283</link>
      <description><![CDATA[There will be circumstances where partial or conditionally automated vehicles fail to drive safely and require human interventions. Within the human factors community, the taxonomies surrounding control transitions have primarily focused on characterizing the stages and sequences of the transition between the automated driving system (ADS) and the human driver. Recognizing the variance in operational design domains (ODDs) across vehicles equipped with ADS and how variable the takeover situations may be, the authors describe a simple taxonomy of takeover situations to aid the identification and discussions of takeover scenarios in future takeover studies. By considering the ODD structure and the human information processing stages, the authors constructed a fault tree analysis (FTA) aimed to identify potential failure sources that would prevent successful control transitions. The FTA was applied in analyzing two real-world accidents involving ADS failures, illustrating how this approach can help identify areas for improvements in the system, interface, or training design to support drivers in level 2 and level 3 automated driving.]]></description>
      <pubDate>Thu, 29 Jun 2023 09:12:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2162283</guid>
    </item>
    <item>
      <title>Teaming with Your Car: Redefining the Driver–Automation Relationship in Highly Automated Vehicles</title>
      <link>https://trid.trb.org/View/2108252</link>
      <description><![CDATA[Advances in automated driving systems (ADSs) have shifted the primary responsibility of controlling a vehicle from human drivers to automation. Framing driving a highly automated vehicle as teamwork can reveal practical requirements and design considerations to support the dynamic driver–ADS relationship. However, human–automation teaming is a relatively new concept in ADS research and requires further exploration. The authors conducted two literature reviews to identify concepts related to teaming and to define the driver–ADS relationship, requirements, and design considerations. The first literature review identified coordination, cooperation, and collaboration (3Cs) as core concepts to define driver–ADS teaming. Based on these findings, the authors propose the panarchy framework of 3Cs to understand drivers’ roles and relationships with automation in driver–ADS teaming. The second literature review identified main challenges for designing driver–ADS teams. The challenges include supporting mutual communication, enhancing observability and directability, developing a responsive ADS, and identifying and supporting the interdependent relationship between the driver and ADS. This study suggests that the teaming concept can promote a better understanding of the driver–ADS team where the driver and automation require interplay. Eventually, the driver–ADS teaming frame will lead to adequate expectations and mental models of partially automated vehicles.]]></description>
      <pubDate>Thu, 09 Feb 2023 09:22:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2108252</guid>
    </item>
    <item>
      <title>Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign</title>
      <link>https://trid.trb.org/View/2050344</link>
      <description><![CDATA[Automated Driving Systems (ADS), like many other systems people use today, depend on successful Artificial Intelligence (AI) for safe roadway operations. In ADS, an essential function completed by AI is the computer vision techniques for detecting roadway signs by vehicles. The AI, though, is not always reliable and sometimes requires the human’s intelligence to complete a task. For the human to collaborate with the AI, it is critical to understand the human’s perception of AI. In the present study, we investigated how human drivers perceive the AI’s capabilities in a driving context where a stop sign is compromised and how knowledge, experience, and trust related to AI play a role. We found that participants with more knowledge of AI tended to trust AI more, and those who reported more experience with AI had a greater understanding of AI. Participants correctly deduced that a maliciously manipulated stop sign would be more difficult for AI to identify. Nevertheless, participants still overestimated the AI’s ability to recognize the malicious stop sign. Our findings suggest that the public do not yet have a sufficiently accurate understanding of specific AI systems, which leads them to over-trust the AI in certain conditions.]]></description>
      <pubDate>Mon, 28 Nov 2022 11:07:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2050344</guid>
    </item>
    <item>
      <title>Using Human–Machine Interfaces to Convey Feedback in Automated Driving</title>
      <link>https://trid.trb.org/View/1937346</link>
      <description><![CDATA[The next decade will see a rapid increase in the prevalence of partial vehicle automation, specifically conditional automation (i.e., SAE level 3; SAE, 2018). In conditional automation, the expectation is that the user is still receptive to takeover and can disengage while the automation is active, but as the automation approaches its operational limits, or the end of its operational design domain, it issues a request to intervene and the user is expected to retake control. A human–machine interface (HMI) that can safely and effectively transition control is therefore very important. This simulator study investigated how features of the HMI design, specifically feedback about the confidence (i.e., current capability) of the automation influenced transition of control. Participants were assigned to one of three conditions, which received varying amounts of visual and auditory feedback regarding the automation’s confidence. Findings suggest 3-stage auditory-visual feedback about the automation’s confidence may improve subsequent takeover performance compared to 3-stage visual and a control group without feedback. This research demonstrates the potential value of providing more insight into automated feature performance in conditional automation.]]></description>
      <pubDate>Tue, 24 May 2022 10:08:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1937346</guid>
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