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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <description></description>
    <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>
    <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>A method to enhance drivers’ hazard perception based on psychological deception induction</title>
      <link>https://trid.trb.org/View/2679328</link>
      <description><![CDATA[Drivers’ hazard perception is a critical factor in road safety. However, existing interventions in driver safety education and training lack relevance and interactivity. Additionally, these interventions typically fail to address the perception needs of potential hazards in complex traffic environment. This study investigated the effects of a psychological deception-induced intervention on drivers’ hazard perception. A total of 45 drivers were recruited and randomly divided into a control group and two experimental groups (the informed false heart rate alerts group and the uninformed false heart rate alerts group). The authors collected and analyzed eye movement data, physiological data, and driving behavior data of drivers from all three groups across 6 scenarios using one-way ANOVA and Kruskal-Wallis tests. The results indicate that drivers in both experimental groups demonstrate enhanced hazard perception relative to the control group. Specifically, compared to the control group, drivers in both experimental groups exhibited longer average fixation durations, shorter time to first fixation, lower driving speed, smoother brake pedal force, and lower true heart rate in hazardous scenarios. These results suggest that the intervention supported earlier hazard detection and more rational responses. Furthermore, comparison between the two experimental groups revealed that the uninformed false heart rate alerts group showed greater improvement in attention allocation to hazards than the informed false heart rate alerts group. The study offers novel insights for enhancing road safety awareness and for designing future driver assistance systems.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:40:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679328</guid>
    </item>
    <item>
      <title>Eye, Heart, the Brain: The Psychophysiology of Trust in AVs</title>
      <link>https://trid.trb.org/View/2675689</link>
      <description><![CDATA[Automation misuse and acceptance, influenced by trust, environmental conditions, and confidence, have hindered drivers from fully benefiting from partially automated vehicles. This study investigates how driver trust changes with AV reliance, differences in mental and physiological states, and continuous measures’ effectiveness. The takeover drivers reported lower trust than the nontakeover drivers in all scenarios. Nontakeover drivers’ elevated DLPFC activation aligns with trust networks and emotion regulation. The groups also differed in neural activation pre- and during scenarios with the takeover group showed more PFC, V2V3, and IFC engagement pre-scenario. Gaze revealed the takeover group fixated more on the AV button or dashboard, indicating readiness to take over, while non-takeover drivers focused on the rearview mirror, reflecting situational awareness. HRV responses showed higher physiological arousal in the takeover group pre-scenario. In summary, our multimodal approach reveals takeover behavior is associated with lower trust, cognitive unloading, increased stress, and anticipatory visual attention.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675689</guid>
    </item>
    <item>
      <title>Early Drowsiness Detection Via Second-Order Derivative Analysis of Heart Rate Variability: A Non-Contact ECG Approach with Machine Learning</title>
      <link>https://trid.trb.org/View/2680736</link>
      <description><![CDATA[Drowsy driving contributes to roughly 20% of traffic fatalities, yet most detection systems rely on behavioral cues that appear only after impairment has set in. Here we ask whether first and second derivatives of heart rate variability (HRV) can detect pre-crash states earlier than conventional approaches. Twenty-five participants completed 49 driving simulator sessions while we recorded cardiac activity through capacitive ECG electrodes embedded in the seat backrest-a non-contact method that avoids the privacy concerns of camera-based monitoring. To prevent circular evaluation, ground truth labels were based solely on crash proximity rather than HRV-derived scores. The combined HRV feature set (conventional metrics plus derivatives) achieved AUC = 0.863 for pre-crash prediction; derivatives alone reached only AUC = 0.573, indicating their value as complementary rather than standalone features. Driving performance indicators remained the strongest predictors (AUC = 0.999). Temporally, derivative-based detection preceded behavioral manifestations by 5-8 min and crash events by 6.8 +/- 2.3 min. Across 1591 crashes and 6.78 million data points, we found that HRV derivatives capture physiological changes that precede overt impairment, though their utility depends on integration with other feature types.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680736</guid>
    </item>
    <item>
      <title>Seat Belt Cover for Monitoring the Driver’s Physiological Functions – Laboratory Prototype</title>
      <link>https://trid.trb.org/View/2665949</link>
      <description><![CDATA[Cardiovascular diseases rank among the leading causes of death in Slovakia. Motor vehicle drivers, in particular, represent a highly vulnerable group. They are not only more prone to these conditions, but any health complications they experience can have fatal consequences for themselves and other road users. In response to this issue, we have developed an innovative device designed to monitor the health status of drivers in real time. This device incorporates advanced technologies to measure various aspects of cardiac activity, including electrocardiography, seismocardiography, gyrocardiography, and phonocardiography, along with monitoring body temperature and respiratory activity. The article provides a comprehensive analysis of the technical specifications of this device and its potential for enhancing the prevention and early diagnosis of cardiovascular diseases among professional drivers.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665949</guid>
    </item>
    <item>
      <title>Visualizing Student Pilot Mental Workload in Real Flight Training Using Wearable Heart Rate Monitoring</title>
      <link>https://trid.trb.org/View/2665832</link>
      <description><![CDATA[This paper presents a practical verification of using wearable physiological monitoring to assess student pilots’ mental workload during real flight training. Heart rate data were continuously recorded with a smartwatch (Apple Watch SE) across the full training program of two students obtaining an ultralight pilot licence. A total of 45 measurements were collected during the training, but only selected recordings were used in this paper, focusing on the repetitive traffic circuit phases, which combine critical flight manoeuvres such as take-off, circuit, and landing. The results consistently show that heart rate values are highest during initial dual and solo flights, especially during take-off and landing phases, and gradually decrease as pilots gain experience and skills. This trend supports the hypothesis that repeated practice reduces mental workload, which can be captured through objective physiological indicators. Although the study did not include subjective workload ratings, performance assessments, or detailed control of external factors, the visualized data clearly illustrate the potential of this method. The approach can help instructors better track student progress, identify high workload phases, and tailor training more effectively. The findings suggest that combining wearable technology with intuitive visualization tools could supplement standard pilot training practices, supporting more efficient learning and improved flight safety. Future research should expand this methodology with larger datasets and integrate subjective and performance-based measures for a more comprehensive understanding.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665832</guid>
    </item>
    <item>
      <title>A Deep Learning-Based Contactless Driver State Monitoring Radar System for In-Vehicle Physiological Applications</title>
      <link>https://trid.trb.org/View/2591122</link>
      <description><![CDATA[The recent advancements in vehicle automation - and associated shift from driving to non-driving activities - has increased the importance of in-vehicle driver monitoring that is safe, trustworthy and useable. Physiological measurement of driver monitoring (rather than just eye tracking) is a nascent approach gaining attention in the space of in-vehicle technologies; however, existing contact sensor-based approaches raise concerns regarding system usage, complexity and privacy. This paper presents research which developed a novel, contactless heartrate monitoring system for drivers using Frequency Modulated Continuous Wave (FMCW) short-range radars, which was validated in vehicular environments. A combination of signal processing and neural network methodologies, incorporating Long Short-Term Memory (LSTM), was adopted to mitigate the effects of body motion and other motion artifacts that cause noisy radar data. The neural network was trained on ground truth data collected in parallel using a medical-grade BIOPAC ECG system. Similarly, the results were validated and compared against this ground truth. The experimental results evidence that FMCW radars are a promising methodology for in-vehicular cardio physiological applications, displaying an overall accuracy of 93% in detecting drivers’ heartrate (HR) and inter-beat-interval (IBI). Additionally, there was no significant difference observed in the RMSE results for driving and non-driving conditions, evidencing that the methodology performed efficiently in both the conditions. This paper demonstrates the benefits of FMCW radars for contactless physiological driver monitoring applications within automotive domains, and beyond.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591122</guid>
    </item>
    <item>
      <title>Using wearable measures to infer moments in workload from Electrodermal Activity and individual workload from Heart Rate Variability during a simulated railway signalling task</title>
      <link>https://trid.trb.org/View/2643959</link>
      <description><![CDATA[Physiological measures offer potential for real-time collection of data to inform understanding of the nature of work in safety critical settings. This study collected physiological data from wearable measures to assess the Mental Workload (MWL) of twenty participants whilst they completed a simulated railway signalling task. Electrodermal Activity (EDA) and Heart Rate Variability (HRV) temporal data were compared to task demand (number of trains) and subjective workload. Average HRV showed a strong negative correlation with average subjective workload. EDA peaks indicated moments in workload including moments of realisation, uncertainty, or time pressure during the task in some participants. HRV and EDA results imply individuals vary in their experience of workload and physiological data can detect variation between participants. Results suggest EDA and HRV data could supplement existing measures of MWL during continuous tasks, through detecting both the timing of individuals’ changing experience of workload and underlying physiological state.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:16:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643959</guid>
    </item>
    <item>
      <title>A biometric-based intelligent monitoring system for early detection and prevention of driver fatigue and distraction</title>
      <link>https://trid.trb.org/View/2620533</link>
      <description><![CDATA[Computer information technology is increasingly used in safety applications. Driver health and conduct are crucial as they significantly impact road safety. Drivers, especially those with chronic conditions, face heightened risks. A Driver Monitoring System (DMS) utilises sensors and algorithms to monitor behaviour and physiology in real-time to detect fatigue, distraction or impairment. The proposed system tracks blood pressure, oxygen levels and heart rate, alerting the driver or intervening when necessary. The aim of this paper is to suggest ways to consider certain factors that are crucial to ensure the safety of drivers. By implementing a system that keeps track of the driver's condition, a significant number of accidents can be avoided. There are many software packages available in today's industry that offer different driver monitoring devices.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620533</guid>
    </item>
    <item>
      <title>The Association Between Health Problems and Driver Status Among Older Adults</title>
      <link>https://trid.trb.org/View/2635337</link>
      <description><![CDATA[Many health problems, especially those associated with older age, can have an impact on an individual’s mobility. This paper addresses how specific functional limitations and medical conditions may be associated with driving status, while controlling for age and gender. This paper uses baseline data (N=2025) from a longitudinal survey of adults, ages 55 and older, the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS). For the 35 selected medical conditions and functional limitations, this report presents the prevalence, the relative “risk” ratio (i.e., the risk that an individual with that condition no longer drives), and the attributable risk (i.e., the percent of ex-drivers with each condition). Compared to current drivers, ex-drivers had higher rates of physical limitations (ability to ascend one flight of stairs or walk three blocks), cognitive impairment, vision problems and stroke. The conditions with the highest relative risk included personal mobility limitations (such as the ability to transfer onto or from bed and the ability to use the lavatory) and decreased peripheral vision. The relative risks of medical conditions’ effects on driving status offer a perspective on individuals’ mobility choices, and the attributable risks offer a perspective on the most important causes of driving cessation in the elderly population.]]></description>
      <pubDate>Sat, 31 Jan 2026 16:28:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635337</guid>
    </item>
    <item>
      <title>Investigating the Independent and Combined Effects of Startle and Surprise in a Simulated Flight Task</title>
      <link>https://trid.trb.org/View/2610815</link>
      <description><![CDATA[Objective: We aimed to characterize the impact of startle and surprise, both independently and in combination, on subjective feelings, behavior (task performance and gaze behavior), and several physiological parameters. Background: The effects of startle and surprise are known to affect pilots’ cognitive performance, with potential impact on safety. Startle and surprise can occur either together or independently, yet no studies have experimentally distinguished their specific effects. Method: Participants (n = 45) were each assigned to one of the three conditions while performing the MATB-II task. In the startle condition, participants were subjected to an expected loud sound. In the surprise condition, an unexpected reverse video effect was applied to the experimental interface. In the combination condition, participants were exposed to both stimuli simultaneously. Results: Surprise was associated with an increase in skin conductance without affecting performance. In contrast, startle was marked by a decline in performance on the communication sub-task, increased skin conductance and heart rate, and a narrowing of attention. When startle and surprise were combined, the results mirrored those of startle alone but included a stronger feeling of startle and surprise, and a more prolonged heart rate increase. Conclusion: Startle and surprise combined yielded more numerous significant effects on subjective, behavioral, and physiological measures than startle and surprise independently. Application: Identifying the specific impacts of startle and surprise could pave the way for their automatic detection using artificial intelligence. Safety could be enhanced through the design of specific countermeasures to help the crew cope with such states.]]></description>
      <pubDate>Thu, 18 Dec 2025 10:56:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610815</guid>
    </item>
    <item>
      <title>Walking in an Urban Environment and a Virtual Reality Replica: Comparisons of Physical Activity Duration and Intensity</title>
      <link>https://trid.trb.org/View/2612380</link>
      <description><![CDATA[Increasing walking behavior is desirable from public health, environmental, and urban planning perspectives. Virtual reality (VR) has the potential to improve the design of walkable environments. However, the current research was necessary to determine whether walking decisions in VR mirror those in the real world (RW). Participants completed two study sessions: walking in a VR simulation of a historic district (VR session) and walking in the real-life district (RW session). During each session, participants were asked to complete three tasks (e.g., find a restaurant) and stop walking following task completion. Heart rate (HR) data contained a high degree of missingness, so no HR analyses are reported. Nevertheless, walking intensity is addressed through exploratory negative binomial and Poisson regression models predicting duration in light and moderate-to-vigorous physical activity using accelerometry. These models indicated no relationship between physical activity intensity in VR and the RW. Additionally, a paired t-test and mixed-effects model indicated that walking duration was significantly longer in VR than the RW. However, exploratory analyses suggested order effects: those who walked first in the RW walked similar durations in both settings, but those that walked first in VR walked for about 5 min longer in VR (17.8 min) than in the RW (13.0 min). In conclusion, walking intensity in VR may not mimic walking intensity in the RW, however, depending on the order of condition presentation, walking decisions in VR may resemble RW decisions. Possible explanations for the observed order effects include history effects, VR navigation and skill transfer, and participant motivation.]]></description>
      <pubDate>Thu, 23 Oct 2025 17:02:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612380</guid>
    </item>
    <item>
      <title>Enhancing Heart Rate Detection in Vehicular Settings Using FMCW Radar and SCR-Guided Signal Processing</title>
      <link>https://trid.trb.org/View/2611432</link>
      <description><![CDATA[This paper presents an optimised signal processing framework for contactless physiological monitoring using Frequency Modulated Continuous Wave (FMCW) radar within automotive environments. This research focuses on enhancing heart rate (HR) and heart rate variability (HRV) detection from radar signals by integrating radar placement optimisation and advanced phase-based processing techniques. Optimal radar placement was evaluated through Signal-to-Clutter Ratio (SCR) analysis, conducted with multiple human participants in both laboratory and dynamic driving simulator experimental conditions, to determine the optimal in-vehicle location for signal acquisition. An effective processing pipeline was developed, incorporating background subtraction, range bin selection, bandpass filtering, and phase unwrapping. These techniques facilitated the reliable extraction of inter-beat intervals and heartbeat peaks from the phase signal without the need for contact-based sensors. The framework was evaluated using a Walabot FMCW radar module against ground truth HR signals, demonstrating consistent and repeatable results under baseline and mild motion conditions. In subsequent work, this framework was extended with deep learning methods, where radar-derived HR and HRV were benchmarked against research-grade ECG and achieved over 90% accuracy, further reinforcing the robustness and reliability of the approach. Together, these findings confirm that carefully guided radar positioning and robust signal processing can enable accurate and practical in-cabin physiological monitoring, offering a scalable solution for integration in future intelligent vehicle and driver monitoring systems.]]></description>
      <pubDate>Thu, 23 Oct 2025 09:22:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611432</guid>
    </item>
    <item>
      <title>Pilot Performance in Instrument-Based Tasks Under Acute Stress</title>
      <link>https://trid.trb.org/View/2611109</link>
      <description><![CDATA[Pilots often experience acute stress during flights, potentially affecting flight safety. The effect of acute stress on instrument-based tasks remains unclear. This study aimed to investigate the influence of acute stress on subjects' performance in two crucial tasks: the attitude recovery task and the landing judgment task. A total of 91 student pilots were divided into a control group and a stress group. Both groups completed a square task, with the stress group exposed to high-intensity noise to induce acute stress. Subsequently, 42 subjects performed an attitude recovery task using two formats of the attitude indicator: moving horizon and moving aircraft. The remaining 49 subjects performed a landing judgment task with three complexity levels using a landing instrument. Heart rates, trait-state anxiety scores, response times, and accuracy of the tasks were analyzed.  Heart rates and state anxiety scores increased following stress induction. In the attitude task, the stress group responded faster than the control group in the moving-horizon format (467.55 ms vs. 491.45 ms) but had lower accuracy (98.65% vs. 99.73%). In the moving-aircraft format, response times (stress: 454.15 ms, control: 474.73 ms) and accuracy (stress: 98.55%, control: 99.38%) showed no significant differences between the two groups. In the low-complexity landing task, the stress group (1015.79 ms) responded faster than the control group (1168.17 ms).  The impact of acute stress on performance depends on task complexity and stress intensity. While stress impairs performance in complex tasks by increasing errors, it enhances performance in simpler tasks by accelerating responses without compromising accuracy.]]></description>
      <pubDate>Mon, 20 Oct 2025 13:38:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611109</guid>
    </item>
    <item>
      <title>Investigating Heart Rate Variability of Metro Drivers before an Inappropriate Stop at Platform with Wearable Devices</title>
      <link>https://trid.trb.org/View/2611124</link>
      <description><![CDATA[Inappropriate stops at platforms (ISPs) of metros, including platform early stops (PESs) and platform overruns (POs), may hinder operation safety and efficiency. One possible reasons is that metro drivers suffer aberrant physiological states before braking on the metro. However, this issue has rarely been analyzed in previous studies. This study investigated metro drivers’ heart rate variability (HRV) features before metro stops and uncovered an association between HRV features and ISPs. Electrocardiogram signals from 32 professional metro drivers were collected through simulated driving experiments. Their RR intervals (The drivers' time difference between successive R waves on an electrocardiogram) within 8, 30, and 60?s before metro stops were examined. Drivers’ sociodemographic and HRV features extracted from different time-windows were processed using principal component analysis to reduce multicollinearity. The study applied multinomial logistic regressions to relate the principal components to the types of metro stop. The results revealed that drivers’ RR intervals within 8 and 60?s before PESs/POs were significantly different from those within 0 and 8?s before PESs/POs. The RR intervals before PESs and POs were significantly larger and smaller, respectively, than those before non-ISPs. Drivers who were relaxed during the approaches tended to trigger more PESs, whereas those who were tense and nervous during the operations tended to trigger more POs. The associations between 12 HRV features extracted by the 8-s time-window and ISP type were different from those extracted by the 30- and 60-s time-window. Additionally, older and experienced drivers were likely to trigger PESs, whereas young and experienced drivers were likely to trigger more POs.]]></description>
      <pubDate>Mon, 20 Oct 2025 08:42:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611124</guid>
    </item>
    <item>
      <title>At the Heart of Intersections: Analyzing Their Influence on Driver Heart Behaviour</title>
      <link>https://trid.trb.org/View/2563676</link>
      <description><![CDATA[Although it may seem intuitive that intersection-related driving conditions could influence a driver’s heart rate (HR)—a key indicator of stress and cognitive load—prior research exploring this assumption has been limited. To address this gap, this paper investigates the physiological impact of intersections on a driver’s HR. By utilizing video and HR data collected from the Honda Research Institute (HRI), the authors apply E-Tests to assess the means and variance of Poisson distributions relating to HR events occurring both within and outside of intersections. The analysis supports the preconceived notion that intersections may significantly influence an individual’s HR, suggesting heightened driver stress or cognitive load in response to these critical road junctures. These findings could inform urban traffic design and enhance vehicle technology, promoting safety and comfort in an increasingly automated driving environment. The code developed for this analysis is available https://github.com/Joelmillr/Heart-and-Intersections.]]></description>
      <pubDate>Fri, 17 Oct 2025 16:38:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563676</guid>
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