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    <title>Transport Research International Documentation (TRID)</title>
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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>
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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>A Queuing Model Based Rapid Evaluation Method for Automotive Control Design</title>
      <link>https://trid.trb.org/View/2675779</link>
      <description><![CDATA[This paper evaluates the ergonomics performance of automotive driving systems through a new computational model, aiming to enhance vehicle control design more cost-effectively than traditional experimental human factors research in the automotive field. Parameters such as spatial coordinates and control dimensions were measured for different driver interaction controls (e.g., hazard light switches, steering wheel buttons) across three typical passenger vehicles. These parameters were integrated into the QN-MHP-U to simulate driver operational behaviors and predict task performance. A computational method was introduced to assess the ergonomic scores of automotive control designs based on the modeling results. The QN-MHP-U provides a systematic and universally applicable solution for evaluating and comparing vehicle control designs within automotive driving systems. This allows automotive designers to assess and improve vehicle control designs from an ergonomic perspective more efficiently in terms of time and economic costs.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675779</guid>
    </item>
    <item>
      <title>Toward the Design of an Ultra-Light Car Seat With a Reclining Back Rest</title>
      <link>https://trid.trb.org/View/2675767</link>
      <description><![CDATA[To reduce the weight of a seat of a city car (A-segment), a car seat was developed. First, a questionnaire was used to define the main functions the occupants prefer to have in the seat. The reclining and forward afterwards movement of the seat were seen as most important. Therefore, a reclining mechanism was built into the lightweight chair. This seat was tested by 39 participants and compared with a benchmark seat of the Toyota Aygo. It was clear that comfort testing is difficult comparing a full developed car seat with a prototype, which still needs development. Nevertheless, the comfort was comparable between the two seats and the shoulder space of the new seat was appreciated. The conclusion is that it is possible to develop a lighter weight car seat, which has the main features preferred by occupants, but further development is needed.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675767</guid>
    </item>
    <item>
      <title>Co-designing with Experts: Exploring Scenarios for Haptic-Enhanced Virtual Reality in Automotive HMI Design</title>
      <link>https://trid.trb.org/View/2580272</link>
      <description><![CDATA[This study investigates how virtual reality (VR) and haptic technologies can enhance the efficiency and sustainability of automotive HMI design through a co-design focus group with seven expert designers, exploring their expectations, attitudes, and concerns. A user story and five potential use cases were developed based on literature findings and used as discussion materials. VR was recognized as a promising tool for usability testing, enabling early-stage assessment of ergonomics and interaction design, thereby reducing reliance on physical prototypes and minimizing environmental and economic costs. Despite the potential of haptic feedback, participants expressed skepticism regarding its technological maturity, highlighting the need for further development to ensure its effectiveness in design testing.  As highlighted by the co-design focus group results, future development efforts should focus on enhancing VR realism, improving haptic feedback, and optimizing HMI prototype management, while also addressing VR context customization, data collection for usability testing, and backend challenges.]]></description>
      <pubDate>Tue, 24 Mar 2026 13:08:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580272</guid>
    </item>
    <item>
      <title>Enhancing Takeover Performance in Autonomous Vehicles Through Augmented Highlighting Displays</title>
      <link>https://trid.trb.org/View/2646131</link>
      <description><![CDATA[Objective: This study aims to introduce a novel augmented display technology that enhances visibility of forward vehicles by projecting critical highlighting information onto the windshield, and to validate its effectiveness in improving occupants’ reaction, acceptance, and workload. Background: The rapid advancements in autonomous driving technology have brought significant changes to the automotive landscape; however, trust and safety concerns remain major barriers to widespread acceptance. To address these issues, enhancing occupants’ reaction efficiency with workload and acceptance in autonomous vehicle operations is critical. Method: Utilizing two distinct highlighting display methods—surface and outline—within a virtual reality simulation, the research examines their effects on occupants’ acceptance including perception of safety through AVAM (Autonomous vehicle acceptance model), and workload through NASA-TLX to dynamic road scenarios during autonomous driving. Results: The findings reveal that highlighting display significantly enhances acceptance and workload with reaction time, but their effectiveness varies. Surface highlighting was found to better reduce anxiety and increase perceived safety, while outline highlighting more effectively reduced mental demand. Conclusion: These results offer valuable insights into the dynamic interaction between advanced display technologies and autonomous vehicle operations, highlighting the potential benefits and challenges in their implementation to foster broader acceptance of autonomous vehicles. Application: By intuitively projecting critical information during takeover scenarios, this technology addresses trust and safety barriers in autonomous driving, potentially enhancing prompt responses, accelerating autonomous vehicle integration, and improving the overall driving experience.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646131</guid>
    </item>
    <item>
      <title>Ergonomic evaluation of a new truck seat design: A field study</title>
      <link>https://trid.trb.org/View/2661761</link>
      <description><![CDATA[A postural evaluation of commercial licensed truck drivers was conducted to determine the ergonomic benefits of a truck seat prototype in comparison with an industry standard seat. Twenty commercially licensed truck drivers were recruited to perform a 90-min driving task. Postures were assessed using accelerometers and a backrest and seat pan pressure mapping system. Subjective discomfort measurements were monitored using two questionnaires: ratings of perceived discomfort (RPD) and the automotive seating discomfort questionnaire (ASDQ). Participants reported significantly higher discomfort scores when sitting in the industry standard seat. Participants sat with more lumbar lordosis and assumed a more extended thoracic posture when seated in the prototype. Pairing the gluteal backrest panel with the adjustable seat pan also helped reduce the average sitting pressure on both the seat pan and the backrest. The prototype provided several postural benefits for commercially certified truck drivers, as it did for a young and healthy population.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661761</guid>
    </item>
    <item>
      <title>A Two-Loop Coupled Interaction System Design for Autonomous Driving Scenarios</title>
      <link>https://trid.trb.org/View/2580287</link>
      <description><![CDATA[The development of automated driving technology gradually detaches the driver from the driving task and allows more interaction in the intelligent cockpit. Problems ensue, as redundancy of interaction information, enhanced cognitive load, and imbalance of resource allocation become increasingly prominent, reducing driver attention and operational efficiency and leading to an imbalance between interactivity and safety. The automotive industry urgently needs to redefine the human-vehicle interaction relationship. In this paper, the authors construct an interaction design set concept, sort out the interaction-related factors of autonomous driving scenarios, and establish a human-centered intelligent cockpit ring interaction set and a vehicle-centered autonomous driving ring interaction set. The relationship between driving tasks, cognitive contention, user experience and human-machine ergonomics is explored. Visualization through the sets redefines the design methodology and evaluation methodology of interaction systems for present and future autonomous driving scenarios, providing guidance to designers.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580287</guid>
    </item>
    <item>
      <title>What drives the effective integration of lift assists in automotive assembly? Perspectives from operators, ergonomists, and manufacturers</title>
      <link>https://trid.trb.org/View/2640992</link>
      <description><![CDATA[Automotive assembly workers experience elevated risks of work-related musculoskeletal disorders due to frequent material handling. Lift assists (LAs) can reduce these risks by offsetting payload weights. However, integrating LAs into complex workflows can be challenging, and workers may choose not to use LAs to achieve other objectives. We interviewed 16 operators, nine ergonomists, and six LA manufacturers to capture diverse viewpoints. Content analysis revealed perspectives on LA usability, design, implementation, and operational concerns. Operators noted physical demands in initiating, turning, or stopping LAs, and emphasized lightweight designs, simplified controls, and structured training. Ergonomists reported retrofitting LAs into workflows not designed for LAs, creating integration challenges. LA manufacturers described balancing ergonomic goals with operational demands and evolving requirements, emphasizing the need for better design feedback. Our findings suggest that heavy equipment, complex controls, and limited training hinder successful LA implementation; we offer recommendations to improve future LA design and implementation.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640992</guid>
    </item>
    <item>
      <title>Human posture and motion prediction for automotive ergonomics design : enhancing functionality and accuracy in digital human modelling tools</title>
      <link>https://trid.trb.org/View/2666546</link>
      <description><![CDATA[Product development (PD) increasingly relies on digital tools to support the process of exploring, generating, and evaluating product design proposals. Ergonomics plays a critical role in ensuring that product designs align with human capabilities and needs. Digital human modelling (DHM) tools can simulate human-product interactions and assess ergonomics virtually, before physical prototypes exist. In vehicle design, DHM tools are frequently applied in occupant packaging activities, supporting the design of vehicle interiors that accommodate a diverse user population. Still, although commonly used in industry, DHM tools have various limitations. One challenge is their limited ability to predict human postures and motions with sufficient accuracy. This inaccuracy is the result of current simulation procedures and the prediction models used. To compensate for this, DHM tool users often require significant manual adjustments to produce realistic postures, making the process time-consuming, subjective, and difficult to reproduce. Moreover, the simulation procedures themselves can be complex and inefficient, reducing their accessibility and usefulness in iterative design work. These limitations often lead to costly and time-consuming validation activities involving real users. This thesis addresses these challenges by developing and evaluating methods and models to enhance the functionality and accuracy of posture and motion predictions in DHM tools.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666546</guid>
    </item>
    <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>
    </item>
    <item>
      <title>Enhancing Tractor Operator Comfort and Safety Through Advanced Drive Assistance Features</title>
      <link>https://trid.trb.org/View/2623983</link>
      <description><![CDATA[Operating tractors on inclined & uneven terrains for prolonged operations presents safety and ergonomic challenges. Applications such as shuttle operations, loader use, or long-duration implement usage prove to be highly critical based on field observations across Mahindra tractor platforms and it requires skill & experience for maneuvering at ease across usage. We identified the need to offload these repeatable tasks from the operator to improve control & offer comfort. This paper explains the role of Advanced drive assistance features developed for Mahindra tractors suited for all prime mover types – ICE, Alternate Fuels including electric. These features include Hill Hold, Electronic parking brake, Cruise control & Creep mode. Each feature is designed to offload frequent manual tasks from the operator and ensure smoother, safer operation. Hill hold and electronic parking brake work in tandem to offer unparalleled safety by eliminating the fear of tractor roll back in uneven terrain and surfaces both in launch and normal operational scenarios. Cruise and Creep control in a combination have been designed to reduce operator fatigue and increase productivity.]]></description>
      <pubDate>Thu, 13 Nov 2025 16:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623983</guid>
    </item>
    <item>
      <title>Work performance measurement of visual inspectors in the automotive industry by considering ergonomic factors</title>
      <link>https://trid.trb.org/View/2611435</link>
      <description><![CDATA[BACKGROUND: Visual inspection workers are always performing under various ergonomic factors and are more vulnerable to the effects of physical and organizational aspects, since their work deals highly with cognitive functions and the impact of ergonomic factors has to be identified in automotive industries to improve work performance., OBJECTIVE: To study the combined effect of ergonomic factors that may have an impact on the performance of visual inspection workers in the automotive industry., METHODS: In this experimental study combined factors such as postures (standing, sitting, Sit-stand) and work shifts (A shift, B shift, C Shift) have been studied at three levels. The study was conducted among selected employes (n = 10) in the automotive manufacturing industry in 2023. During the study, the visual inspectors' work performance was measured using the error study, and the cognitive functions of visual inspectors' were evaluated by taking the Digit Symbol Substitution Test (DSST)., RESULTS: The study established that postures significantly impact work performance at 40.08% and cognitive functions at 36.25%. Work shifts significantly impact work performance with 18.18% and cognitive functions with 26.62% of visual inspectors' in the automotive industry. The combined effect of postures and work shifts has significantly impacted the visual inspectors' performance with 13.29% on work performance and 10.12% on cognitive functions., CONCLUSIONS: This study draws the inference that individual and combined factors (Posture and Work shift) both possess a significant impact on the work performance and cognitive functions of visual inspectors' in the automotive manufacturing industry.]]></description>
      <pubDate>Fri, 24 Oct 2025 08:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611435</guid>
    </item>
    <item>
      <title>Optimizing heated driver seat design: Thermal comfort in cold weather</title>
      <link>https://trid.trb.org/View/2587399</link>
      <description><![CDATA[Heated seats are increasingly used in vehicles to improve thermal comfort, yet preferred temperatures across different seat zones remain underexplored. This study examined seat surface temperature preferences across six seatback and cushion zones, considering the effects of weather conditions and user demographics. A total of 102 participants—diverse in sex, age, body size, and ethnicity—participated in a controlled experiment simulating -8 °C to 12 °C and 35 %–75 % humidity. Results indicated that colder conditions led to higher preferred temperatures at four seat zones, with female participants generally favoring warmer settings than males. The findings support the need for seat heating systems with individual controls for multiple zones. Twelve predictive models—six each for basic and advanced seat designs—were developed to estimate optimal seat temperatures. These models can help automotive manufacturers improve seat heating systems by integrating them into AI-assisted technologies for adaptive thermal regulation, ultimately enhancing occupant comfort and satisfaction.]]></description>
      <pubDate>Mon, 22 Sep 2025 08:48:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587399</guid>
    </item>
    <item>
      <title>Analysis of adaptive systems based on Driver's workload</title>
      <link>https://trid.trb.org/View/2569860</link>
      <description><![CDATA[This study examined workload classification models and their application in adaptive in-vehicle systems. A meta-analysis of 31 studies assessed how predictor types (e.g., physiological data), experimental settings (simulator vs. on-road), and device types (wearable vs. remote) influence model accuracy. Results indicated that incorporating physiological data improved model accuracy, although ensuring generalizability remains a challenge. Random Forest models demonstrated the highest average accuracy for binary classification, while Neural Networks showed promise for multi-class models. Adaptive systems leveraging multi-input models were found effective in dynamically adjusting to workload, enhancing safety and user experience. However, challenges such as system over-reliance and limited system implementation persist. Additionally, this study analyzed the existing adaptive systems in the automotive market and proposed design guidelines and a framework for workload-based adaptive systems. Future research should focus on developing robust, context-aware systems tailored to occupational and real-world driving demands, ensuring reliability and widespread applicability.]]></description>
      <pubDate>Fri, 29 Aug 2025 10:03:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569860</guid>
    </item>
    <item>
      <title>The human-factors’ challenges of (tele)drivers of Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2549004</link>
      <description><![CDATA[Autonomous capabilities, including Autonomous Vehicle (AV) technology, aim to reduce human effort, extend capabilities, and enhance safety. While AVs offer societal benefits, human intervention remains necessary, especially in complex situations. As communication technology advances, human intervention is possible from remote sites. In such remote locations, highly skilled tele-drivers (TEDs) are ready to face situations too complicated for the AV. However, current work still needs a comprehensive mapping of the challenges that TEDs would face. Some of these challenges are shared with IVDs but may have stronger or weaker effects on the remote driver’s ability to maintain safety. Other challenges, such as limited situational awareness of the road scene, the indirect experience of vehicle motion, and communication latency, are unique to TEDs. The authors assess the challenges, comparing their impact on TEDs versus IVDs, and explore technological countermeasures aimed at mitigating specific challenges encountered by TEDs. Lastly, the authors identified knowledge gaps and areas lacking understanding in the literature, highlighting avenues for future research and practical implications for practitioners.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2549004</guid>
    </item>
    <item>
      <title>Effect of Takeover Request Time and Warning Modality on Trust in L3 Automated Driving</title>
      <link>https://trid.trb.org/View/2539945</link>
      <description><![CDATA[Objective: This study investigated the effects of four takeover request (TOR) times and seven warning modalities on performance and trust in automated driving on a mildly congested urban road scenario, as well as the relationship between takeover performance and trust. Background: Takeover is crucial in L3 automated driving, where human–machine codriving is employed. Establishing trust in takeover scenarios among drivers can enhance the acceptance of autonomous vehicles, thereby promoting their widespread adoption. Method: Using a driving simulator, data from 28 participants, including collision counts, takeover time (ToT), electrodermal activity (EDA) data, and self-reported trust scores, were collected and analyzed primarily using Generalized Linear Mixed Models (GLMM). Results: Collisions during the takeover undermined participants’ trust in the autonomous driving system. As TOR time increased, participants’ trust improved, and the longer TOR time did not lead to participant confusion. There was no significant relationship between warning modality and trust. Furthermore, the combination of three warning modalities did not exhibit a notable advantage over the combination of two modalities. Conclusion: The study examined the effects of TOR time and warning modality on trust, as well as preliminarily explored the potential association between takeover performance, including collisions and ToT, and trust in autonomous driving takeovers. Application: Researchers and designers of automotive interactions were given referenceable TOR time and warning modality by this study, which extended the autonomous driving takeover scenarios. These findings contributed to boosting drivers’ confidence in transferring control to the automated system.]]></description>
      <pubDate>Thu, 05 Jun 2025 13:59:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539945</guid>
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