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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>Pilot response to somatogravic illusion in a simulated environment: Implications for early instrument flight training</title>
      <link>https://trid.trb.org/View/2643951</link>
      <description><![CDATA[The somatogravic illusion, a vestibular misperception caused by linear acceleration in the absence of visual cues, poses a significant safety risk during flight, particularly under instrument meteorological conditions. Despite its operational relevance, current pilot training programs emphasize theoretical instruction and lack practical exposure to such illusions. This study aimed to assess the behavioral effects of the somatogravic illusion in a controlled simulator environment and to evaluate the potential for adaptation through repeated exposure. A total of 114 pilots were assigned to four groups based on IFR experience. Each participant completed two simulator sessions one week apart, each comprising flights with and without induced somatogravic illusions. Illusion induction was achieved using cabin pitch motion within a fixed-base disorientation trainer. Altitude trajectories during the illusion interval were extracted, L2-normalized, and analyzed using principal component analysis and hierarchical clustering. Cluster transitions were evaluated to identify adaptation patterns. Post-exposure questionnaires assessed perceptual awareness and training utility. Illusion exposure caused systematic suppression of climb performance, independent of IFR experience. Unsupervised clustering revealed two dominant trajectory patterns corresponding to affected and unaffected responses. In the second session, 32% of previously affected pilots transitioned to the unaffected cluster, indicating behavioral adaptation. Perceptual awareness of the illusion remained low (23%–29%), yet 95.6% of participants endorsed the inclusion of vestibular illusion scenarios in IFR training. Controlled simulator exposure to the somatogravic illusion elicits measurable disruptions in altitude control that are not mitigated by experience alone but can improve with brief, repeated exposure. The findings support the integration of illusion-focused modules into early instrument training to enhance resilience to spatial disorientation. The use of fixed-base simulators for such training is feasible and well-received by pilots.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:16:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643951</guid>
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    <item>
      <title>PriorFusion: Unified integration of priors for robust road perception in autonomous driving</title>
      <link>https://trid.trb.org/View/2627396</link>
      <description><![CDATA[With the growing interest in autonomous driving, there is an increasing demand for accurate and reliable road perception technologies. In complex environments without high-definition map support, autonomous vehicles must independently interpret their surroundings to ensure safe and robust decision-making. However, these scenarios pose significant challenges due to the large number, complex geometries, and frequent occlusions of road elements. A key limitation of existing approaches lies in their insufficient exploitation of the structured priors inherently present in road elements, resulting in irregular, inaccurate predictions. To address this, we propose PriorFusion, a unified framework that effectively integrates semantic, geometric, and generative priors to enhance road element perception. We introduce an instance-aware attention mechanism guided by shape-prior features, then construct a data-driven shape template space that encodes low-dimensional representations of road elements, enabling clustering to generate anchor points as reference priors. We design a diffusion-based framework that leverages these prior anchors to generate accurate and complete predictions. Experiments on large-scale autonomous driving datasets demonstrate that our method significantly improves perception accuracy, particularly under challenging conditions. Visualization results further confirm that our approach produces more accurate, regular, and coherent predictions of road elements.]]></description>
      <pubDate>Thu, 26 Feb 2026 09:14:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627396</guid>
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    <item>
      <title>Analyzing drivers visual attention towards intersection conflict warning system: A study using driving simulator and eye tracking system</title>
      <link>https://trid.trb.org/View/2657083</link>
      <description><![CDATA[Unsignalized intersections are considered one of the most hazardous road locations, where drivers must carefully process visual information to make safe decisions, as improper attention allocation or lack of information on approaching traffic can lead to crashes. Intersection Conflict Warning System (ICWS) has been identified as a potential solution, however its influence on drivers' visual performance remains unexplored. This study aims to investigate the effect of ICWS on drivers' visual performance at unsignalized intersections using a driving simulator and eye tracking system. Forty-six licensed drivers participated in this study, and drivers eye movement behavior towards ICWS was analyzed under various warning and intersection visibility conditions. Additionally, the effect of education about ICWS was also examined. Experimental results showed that at the restricted-view intersections, drivers had 46% longer fixation durations and 34% more fixations on warning signboards compared to clear-view intersections. Under ICWS activated conditions, drivers exhibited significantly longer fixation duration, and a higher proportion (72%) reacted after gazing at the signboard compared to the non-activated ICWS conditions (39%). Furthermore, middle aged drivers demonstrated a shorter time to first fixation on the signboard than younger drivers under ICWS activated conditions. The findings highlight that ICWS enables drivers to notice warning signboards promptly, initiate earlier visual searches for conflicting vehicles, and respond more quickly to potential conflicts, supporting its application as an effective countermeasure for enhancing safety at unsignalized intersections.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:59:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657083</guid>
    </item>
    <item>
      <title>Effects of odor types and concentration adjustment modes of olfactory stimulation on fatigue in young drivers</title>
      <link>https://trid.trb.org/View/2657084</link>
      <description><![CDATA[This study investigated the effects of the odor type and concentration adjustment modes of olfactory stimulation on fatigue in young drivers. A multidimensional analysis was conducted using driving simulator data, the Karolinska Sleepiness Scale (KSS), and physiological indicators such as heart rate (HR) and HR variability. Under dynamic incremental concentrations, peppermint odor significantly reduced KSS fatigue scores and mean HR while elevating the root mean square of successive differences, indicating increased parasympathetic activity. Specifically, compared with the mild odor (lavender) under constant concentration, peppermint under dynamic incremental concentration most effectively alleviated fatigue, leading to a significant reduction of 18.7% in KSS scores, a 5.3% decrease in mean HR, and a 30.1% increase in root mean square of successive differences. Conversely, lavender odor at a constant concentration showed a certain degree of fatigue relief. Incremental concentrations countered the sensory adaptation and sustained driver fatigue reduction more effectively. These findings provide insight into the design of olfactory stimulation parameters for intelligent cockpits. Future research should prioritize road validation, analyze the heterogeneity of users' olfactory perceptions, and optimize dynamic concentration adjustment mechanisms. This study promoted the application and development of olfactory stimulation to reduce driver fatigue.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:59:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657084</guid>
    </item>
    <item>
      <title>When does visual distraction become dangerous in car-following? Evidence from naturalistic driving study data with causal inference on time-to-collision and braking intensity</title>
      <link>https://trid.trb.org/View/2659609</link>
      <description><![CDATA[Visual distraction is a major contributor to crash risk, particularly in car-following situations that demand continuous monitoring and rapid response. Although prior research using simulators and Naturalistic Driving Study (NDS) data has advanced the understanding, evidence remains limited on how visual distraction increases risk in real-world contexts and under which conditions it is amplified. Visual distraction is not an isolated factor, but a context-dependent phenomenon shaped by roadway conditions, traffic dynamics, and external stimuli. Beyond measuring its overall effect, it is essential to identify the circumstances in which visual distraction becomes especially hazardous. To address this gap, this study applies causal inference methods to NDS data. A Causal Forest was used to estimate the causal effect of visual distraction on two safety indicators: time-to-collision (TTC) and braking intensity. Subsequently, mediation analysis using Double Machine Learning (DML) was applied to disentangle the extent to which visual distraction mediates driving risk from the portion attributable directly to roadway and traffic conditions, thereby clarifying the indirect behavioral pathways versus structural design effects. Results show that visual distraction significantly reduces TTC, indicating heightened conflict seriousness, whereas its effect on braking intensity was not statistically significant. Mediation analysis further revealed that the effect of visual distraction on TTC varied across contexts, with stronger effects under high traffic density, ADAS-equipped vehicles, wider sidewalks, and fewer lanes. These findings underscore the importance of integrated safety strategies that mitigate visual distraction while also accounting for roadway design, traffic environment, and vehicle technologies in shaping driver behavior and risk.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659609</guid>
    </item>
    <item>
      <title>Visual motion contrast thresholds in the periphery predict older drivers’ behavior at intersections</title>
      <link>https://trid.trb.org/View/2659459</link>
      <description><![CDATA[Peripheral motion contrast sensitivity decline is likely due to a progressive dysfunction of the magnocellular pathway in the aging brain. Previous research from this group had demonstrated that the Peripheral Motion Contrast Threshold 2-minute test version (PMCT-2) predicts older drivers’ hazardous behaviors in simulated driving environments. This study extends this work by examining correlations between PMCT-2 scores and on road driving outcomes at intersections coded from video recordings of fifty older drivers (65–89) navigating predefined urban routes in their own vehicles. The authors found significant correlations between PMCT-2 and scanning errors at non-signalized and stop-signalized intersections. The authors also found significant PMCT-2 correlation with driving compliance errors, notably incomplete stops, which was further supported by single-predictor regression using heteroskedasticity-robust estimation. Multiple linear regression analyses further showed that PMCT-2 remained the only significant predictor of stop-sign compliance errors after adjusting for age, gender and scanning error rates at stop signs. In contrast, its relationship with scanning errors was attenuated in linear models, reflecting the very low frequency of scanning errors observed on the road. These findings build on prior evidence that the PMCT-2 predicts older drivers’ performance outcomes and, for the first time, demonstrate its potential to predict actual on-road driving performance at intersections.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659459</guid>
    </item>
    <item>
      <title>Visual attention and driving behavior of male autistic individuals while encountering driving hazards: A driving simulator study</title>
      <link>https://trid.trb.org/View/2664104</link>
      <description><![CDATA[Hazard perception is an important aspect of driving competence that significantly contributes to road safety. Allocating sufficient visual attention to hazards and responding accordingly can help reduce the likelihood of road crashes. Although hazard perception has been investigated to some extent in autistic individuals, little attention is given to hazards for which attention has to be divided among different hazard sources. The current study assessed visual attention and driving behavior of autistic individuals to hazards, including dividing and focusing attention (DF), environmental prediction (EP), and behavioral prediction (BP) hazards. A total of 53, male participants, 19 autistic and 34 non-autistic individuals participated in the study. All participants completed a driving simulator scenario while wearing an eye-tracking system. The included eye-tracking measures were time to first fixation (TTFF), frequency count (FC), first fixation duration (FFD), and average fixation duration (AFD). The included driving measures were brake reaction time (BRT), minimum time-to-collision (minTTC), and speed change immediately before encountering the hazard. A self-reported appraisal regarding difficulty in managing hazards was also included. A series of Linear Mixed Models (LMM) were computed to assess the effects of participant group (autistic and non-autistic) and hazard types (DF, EP and BP) on the included measures. Comparisons of visual attention between autistic and non-autistic participants when responding to hazards yielded mixed results. For certain hazards, autistic participants demonstrated faster fixation (e.g., DF and BP). In contrast, for other hazards, non-autistic participants exhibited quicker fixation (e.g., EP) and longer average fixation duration (e.g., DF and EP). For some hazards, however, both groups displayed comparable levels of average fixation duration (e.g., BP). Although variations in visual attention to hazards were observed between autistic and non-autistic individuals, these differences did not manifest in driving performance metrics. This is evidenced by the absence of significant interactions between participant groups and hazard types concerning driving measures. However, autistic individuals were more likely to experience crashes involving BP hazards than non-autistic individuals. Notably, inexperienced autistic participants had a higher crash rate on BP hazards compared to non-licensed non-autistic participants. In contrast, the crash rates were comparable between licensed participants in both groups. The study may reflect that pre-driver autistic participants could benefit from hazard perception training, particularly in dealing with BP hazards.]]></description>
      <pubDate>Wed, 25 Feb 2026 08:53:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664104</guid>
    </item>
    <item>
      <title>Mid-air haptics for automotive HUDs: A sketch anticipation and design fiction study</title>
      <link>https://trid.trb.org/View/2667363</link>
      <description><![CDATA[Innovations in the automotive industry, such as autonomous driving, AI assistants, head-up displays, and mid-air haptic touchless interactions, promise transformative benefits but may also introduce unanticipated risks and ethical concerns. To explore these potential challenges, we conducted a multi-stage study: first, we engaged 27 engineers specializing in touchless and automotive systems to envision future applications of mid-air haptics and head-up displays. Insights from this anticipatory design fiction informed the creation of high-fidelity storyboard sketches depicting six hypothetical scenarios. Using these storyboards and a custom questionnaire, we then surveyed 135 drivers across nine countries to assess their views on technology acceptance, interface usability, and responsible innovation. Results revealed significant demographic variability, alongside a dual sentiment: while drivers express enthusiasm for technological integration, they also voice concerns about safety, user control, and privacy. Our findings not only inform safer and more user-centered automotive innovation but also offer a multimodal framework for evaluating and guiding emerging technologies across diverse fields.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:24:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667363</guid>
    </item>
    <item>
      <title>Understanding Driver Perception of Sharpness, Risk, and Speed on Horizontal Curves: An Instrumented Vehicle and Questionnaire Study of Two-Lane Highway</title>
      <link>https://trid.trb.org/View/2647794</link>
      <description><![CDATA[Inappropriate speed choice is one of the major causes of single-vehicle run-off-road crashes on horizontal curves. Safe speed negotiation depends on drivers’ perception of visual cues, such as curve sharpness, speed and available sight distance, which in turn shape their risk perception. Thus, drivers’ perceptions of sharpness, risk, and speed play a critical role in speed selection on horizontal curves. However, existing research has largely focused on geometric factors for enhancing curve safety. This study first examines the effects of road geometry and driver characteristics on drivers’ perceptions of speed, sharpness, and risk, and second, how these perceptions affect objective curve speed. A naturalistic driving experiment was conducted on a 22 km stretch of a two-lane rural highway containing 28 horizontal curves. Thirty-four drivers drove an instrumented sedan equipped with a GPS data logger to collect speed and perception data. The objective speed data were collected using a GPS data logger, while a surveyor seated in the vehicle recorded perception data via a questionnaire, taking responses within 15 seconds to capture drivers’ short-term perceptions. Perceived speed was negatively correlated with perceived sharpness and perceived risk. Deflection angle, curve length, curve gradient, gradient of the preceding tangent, and education emerged as significant predictors of perceived speed. Deflection angle, curve length, and transition curve length significantly influenced perceived sharpness, whereas age significantly influenced perceived risk. Among perceived speed, sharpness, and risk, perceived speed significantly influenced the mean observed curve speed. These findings highlight the importance of integrating driver perceptions into horizontal curve design and safety interventions. Targeted measures, such as appropriate signage and visual cues, can encourage safer speeds, while education and training programs for younger and novice drivers may reduce misperceptions and enhance curve safety. Future research across diverse road sites and driver groups can provide more insights into these findings.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647794</guid>
    </item>
    <item>
      <title>Research on Safety Perception Monitoring and Early Warning Technology of Highway Construction Based on Holographic Three-Dimensional Real-World Big Data</title>
      <link>https://trid.trb.org/View/2613302</link>
      <description><![CDATA[By analyzing the safety management requirements of highway construction, the layout technology of general perception equipment and special perception monitoring equipment for highway construction safety is studied. Combining the basic characteristics of highway engineering construction safety with a holographic three-dimensional real scene, the concept of a holographic three-dimensional real scene of highway construction safety is proposed. Integrating the safety monitoring data collected by unmanned air vehicle data, video surveillance data, sensor data, and key component equipment groups, multi-source data fusion technology is studied. Based on big data, the three-dimensional model data acquisition and modeling, spatial data fusion, and business data fusion are carried out to construct the holographic three-dimensional real scene model of the key parts of highway construction safety. Using the collected three-dimensional real-world big data, the application research of construction safety monitoring and early warning of tunnels, bridges, slopes, subgrade, and pavement and temporary sites is carried out.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613302</guid>
    </item>
    <item>
      <title>Research on Optimization Method for Tunnel Contour Mark Spacing under the Environment of Colored Pavement Based on Visual Perception</title>
      <link>https://trid.trb.org/View/2613299</link>
      <description><![CDATA[To improve safety and comfort in tunnels, from the perspective of visual perception, we propose a method for optimizing contour mark spacing under the environment of colored pavement in tunnels. Firstly, we use simulation technology to conduct speed and curve perception experiments. Then, through regression analysis of experimental data, we propose models for degrees of speed and curve illusion, with the edge rate of facility, number of visible facilities, and pavement color hue as independent variables. On the basis above, we build models of optimized contour mark spacing in straight and curve line segments in tunnels, which take perception illusion and pavement color hue into account. Finally, one tunnel in Fujian Province in China is taken as an example for verification. The proposed optimization method is beneficial for improving the adaptability of the colored pavement and traffic safety facilities in tunnels.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613299</guid>
    </item>
    <item>
      <title>Decorating Sidewalls in Extra-Long Tunnels to Enhance Driving Safety: An Investigation into Its Effect on Visual Behavior</title>
      <link>https://trid.trb.org/View/2613110</link>
      <description><![CDATA[As a new type of traffic safety facility, decorated sidewall is crucial in the safety improvement and quality upgrading of tunnels. However, due to the lack of setting standards for decorated sidewall in the tunnel, the influence of various decorated sidewalls on drivers’ attention has aroused widespread controversy. To quantitatively analyze the influence of decorated sidewall on driver’s distraction in the tunnel, five schemes of decorated sidewall were designed based on driving simulation, which were compared with the tunnel without decorated sidewall. A total of 27 drivers were recruited, and high fine-grained eye-tracking data were obtained to extract evaluation indicators considering driving distraction. The differences in visual behavior characteristics under the influence of the decorated sidewall were explored by repeated measurement ANOVA. The results show that the decorated sidewall will not cause excessive distraction for the driver; the appropriate decorated sidewall has a significant effect on improving the driving level; and decorated sidewall with a rhythm of 1.27 Hz can effectively improve the driver’s alertness and visual perception, while maintaining a low visual load, demonstrating the best potential for driving safety. The research results will provide theoretical support and data guarantee for the method of using a decorated sidewall to improve the driving safety of extra-long tunnels.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613110</guid>
    </item>
    <item>
      <title>Multimodal Visual Early Warning Equipment to Improve Night Driving Safety on Highway Curves: A Driving Simulation Study</title>
      <link>https://trid.trb.org/View/2612974</link>
      <description><![CDATA[Highway curves pose significant safety risks due to challenging road conditions, limited sight distance, and increased rollover or sideslip risks, especially at night when visibility is reduced. This study uses driving simulation technology to recreate nighttime curve congestion scenarios and evaluates three visual warning systems: yellow text flashing, yellow light flashing, and red light flashing, based on principles of “cognitive alignment and moderate stimulation.” Data from 36 participants were collected, focusing on speed control, psychological comfort, and operational stability. Repeated measures ANOVA and entropy weight TOPSIS were applied to assess the effects of warning systems on driving behavior and traffic safety. Results indicate that yellow light flashing significantly improves speed control, psychological comfort, and acceleration stability, offering effective traffic safety guidance. These findings provide theoretical and practical insights for improving safety facilities on hazardous highway sections.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612974</guid>
    </item>
    <item>
      <title>Optimization Algorithm for 3D Small Object Sensing in BEV Technology for Autonomous Driving Scenarios</title>
      <link>https://trid.trb.org/View/2612969</link>
      <description><![CDATA[Complex and changing road conditions place higher demands on BEV (Bird’s-Eye View) technology. Current research shows that BEV technology faces great challenges in detecting small targets such as pedestrians. To solve this problem, this paper proposes a 3D small target perception optimization algorithm for BEV. The algorithm adopts a recursive FPN network to effectively capture multi-scale target information through recursive feature learning. In addition, it integrates historical frame data to enhance the feature representation of the current BEV. Adaptive NMS (non-maximum suppression) is applied to post-process detection results. From the BEV perspective, the proposed algorithm significantly enhances the detection of small targets, and the algorithm scores 42.3% mAP and 48.4% NDS on the nuScenes val set. The experimental results demonstrate that the algorithm has a significant advantage in the detection of small targets, which provides BEV autopilot perception technology with strong support for BEV autonomous driving perception technology.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612969</guid>
    </item>
    <item>
      <title>Application of Uncertainty to Out-of-Distribution Detection for Autonomous Driving Perception Safety</title>
      <link>https://trid.trb.org/View/2591235</link>
      <description><![CDATA[Deep learning is well used in the field of autonomous driving, but systems often encounter scenarios that are significantly different from their training data, known as out-of-distribution (OOD) scenarios. In the realm of autonomous driving safety, perception is the most crucial and complex component, with existing perception network frameworks still suffering from inadequate accuracy, compounded by the plethora of uncertain factors and OOD scenarios in real road environments. However, previous research mainly focused on uncertainty methods and OOD detection techniques, lacking comprehensive reviews that cover their origins in the context of perception safety, theoretical analyses, evaluation metrics, and their applicability in ensuring autonomous driving safety. This review analyzes the OOD and uncertainty issues faced in the autonomous driving perception domain, including some cause analyses and solution s to these issues. Additionally, the review discusses OOD detection evaluation metrics that meet the safety requirements of autonomous driving. Thirdly, it summarizes existing OOD detection methods and explores their strengths and weaknesses under autonomous driving safety with evaluation metrics. Lastly, the article analyzes the application of uncertainty techniques in OOD detection, including the shortcomings and categorization of uncertainty methods, spatial feature utilization, semantic information, and performance optimization techniques. The review also highlights the shortcomings and improvement directions of uncertainty quantification techniques in OOD detection, along with the potential for further integration with autonomous driving scenarios. This study aims to promote the research and application of uncertainty quantification and OOD detection technologies in the context of autonomous driving safety. To facilitate future research, we create a repository that includes links to relevant reviews and methodological code for learning at https://github.com/sotif-ma/OOD-Detection-Methods-and-Datasets]]></description>
      <pubDate>Thu, 19 Feb 2026 17:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591235</guid>
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