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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>
    <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>Field-Based Evaluation of Physiological Fatigue Reduction in Fuel Cell Refuse Collection Vehicle Using Heart Rate Variability</title>
      <link>https://trid.trb.org/View/2717319</link>
      <description><![CDATA[Understanding the physiological impact of vehicle electrification on operators remains an important but underexplored issue in commercial vehicle research. This study quantitatively evaluates the physiological fatigue of drivers and onboard crew members during real-world operation of commercial refuse-collection vehicles by comparing a diesel-powered vehicle with a fuel cell electric vehicle (FCEV). Both vehicles were operated on the same routes under comparable real-world operating conditions, including similar time periods and operational tasks, during municipal waste collection service. Heart Rate Variability (HRV) metrics were obtained from R-R interval (RRI) data recorded using a Polar heart rate sensor. The Root Mean Square of Successive Differences (RMSSD), a time-domain index reflecting short-term parasympathetic activity, and Poincaré (Lorenz) plot area (LP area), a nonlinear HRV index reflecting overall autonomic nervous system modulation, were calculated. In-cabin vibration and noise levels were also measured as supplementary context to support the interpretation of physiological responses.The results indicate that both RMSSD and LP area were higher during FCEV operation than during diesel vehicle operation. For the driver, RMSSD increased by approximately 61.65% and the LP area by approximately 49.91%. For the onboard crew member, RMSSD increased by approximately 18.79% and the LP area by approximately 46.02%.These findings suggest a consistent association between reduced vibration and noise characteristics in the FCEV and increased HRV indices, indicating reduced physiological fatigue during operation. This study provides quantitative evidence that fuel cell electric commercial vehicles are associated with improved occupational conditions, extending beyond conventional environmental benefits.]]></description>
      <pubDate>Tue, 23 Jun 2026 10:30:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717319</guid>
    </item>
    <item>
      <title>Non-Intrusive Fatigue Detection for Pilots</title>
      <link>https://trid.trb.org/View/2712086</link>
      <description><![CDATA[Pilot fatigue represents a critical concern in aviation safety, as it can significantly impair cognitive functions, decision-making abilities, and reaction times. In addition to decreasing performance, in-flight chronic fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and workload. Conventionally, the risk due to fatigue in aerospace is reduced by flight time limits and controlled rest requirements. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Hence, there is need to detect pilot fatigue in real time.There is ongoing research to detect pilot fatigue using devices that can capture Electroencephalogram (EEG) and Electrocardiogram (ECG). Though these devices have high fidelity, they are intrusive and can limit pilot activity. This limitation could potentially be overcome by non-intrusive devices such as a smart watch/wrist band/goggles which can measure physiological parameters that provide insights into pilot’s mental health. Heart rate variability (HRV) is one such physiological marker of interest for detecting pilot fatigue in real time. HRV can be effectively derived by processing raw Photoplethysmography (PPG) signals to gain insights into the autonomic nervous system, enabling the assessment of physiological state. Wearable devices such as a wristwatch are used in the current study to measure PPG data. Time and frequency domain analysis were performed to evaluate the potential of HRV indices. The analysis of R-R intervals and the Low Frequency / High Frequency (LF/HF) ratio plots, derived from HRV signals, revealed distinct characteristics that differentiate between an alert and a fatigued pilot. This study demonstrates a reliable non-intrusive method for detecting pilot fatigue and enhancing flight safety.]]></description>
      <pubDate>Wed, 10 Jun 2026 13:18:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712086</guid>
    </item>
    <item>
      <title>Neurocognitive Validation &amp; Transition Study – Phase 1, Rev B</title>
      <link>https://trid.trb.org/View/2703851</link>
      <description><![CDATA[The Office of Aerospace Medicine is evaluating alternative neurocognitive screening tools to support pilot medical certification and reduce reliance on the Federal Aviation Administration's (FAA’s) current single-vendor, proprietary test, which presents continuity and operational risk if the product becomes unavailable or compromised. The FAA has partnered with multiple developers to produce derivative tests tailored for aviation use; however, an independent expert assessment is required to determine whether these tools are ready for operational deployment or require additional validation. This research will provide that assessment, ensuring that neurocognitive impairment relevant to pilot performance can be reliably identified before it presents safety risk, and will directly inform whether the derivative tools can be adopted as-is or whether further reliability, feasibility, or validation studies are needed to support future implementation decisions.]]></description>
      <pubDate>Mon, 18 May 2026 10:37:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703851</guid>
    </item>
    <item>
      <title>Artificially Intelligent Rapid Rescue Vehicle System</title>
      <link>https://trid.trb.org/View/2663389</link>
      <description><![CDATA[The rapid development of science and technology has impacted on the human lifestyle. The automotive industry plays a crucial role as travel is an integral part of human lifestyle. This indeed has increased the need and demand for automotive domain to step ahead with technology and innovations. Especially, related to ADAS features and AI/ML based algorithms to provide comfort, safety, and many other factors for the consumers. The busy life of human beings has shown an increased rate of many health-related issues like stress, anxiety, heart attacks, blood pressure and so on. The existing system in vehicles detects health emergency and triggers SOS to the emergency service center. However, several catastrophic events occur due to delayed information, thus there is a need for a proactive solution that combines technology and human safety.In this work, we have investigated the different methods which detect the health issues of occupants in a vehicle by monitoring their stress level, heart rate, blood pressure and so on. We propose a solution which helps to navigate to the nearest health center or ambulance meeting point in emergency cases, overcoming technical glitches and delays by driving cars to the emergency center or meeting point, thus saving time for occupants. The prerequisite is that the vehicle has an advanced driver assistance system, detects health emergency of the occupants, V2X communication and SOS are triggered with the basic details of the situation. The system selects the nearest relevant hospital to drive to or requests the SOS center for the geo-coordinates of the ambulance meeting point using V2X communication. As soon as the system receives information related to meeting point from SOS center, autonomous driving mode is initiated, acknowledgment is sent to SOS center, and live location is shared for better communication and coordination. Additionally, the system triggers a siren and emergency lights to indicate an emergency drive, ensuring safety and a clear path. This proactive solution increases the probability of rescuing occupants by taking necessary action, rather than just monitoring, reporting, and waiting for measures.]]></description>
      <pubDate>Mon, 02 Feb 2026 16:36:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663389</guid>
    </item>
    <item>
      <title>Research on Automotive Vibration Comfort Based on Psychological
          Indicators</title>
      <link>https://trid.trb.org/View/2614445</link>
      <description><![CDATA[In recent years, the vibration comfort of automobiles has become a key                     consideration for consumers when purchasing vehicles. This study introduces                     human electrocardiogram (ECG) signals and blood pressure, and proposes a comfort                     prediction model based on physiological indicators. The research steps include:                     obtaining riding indicators and subjective feelings on flat and bumpy roads, and                     analyzing the differences in heart rate variability indicators and blood                     pressure under different road conditions through paired sample tests; playing                     different sound signals on bumpy roads, and using repeated measures analysis of                     variance to explore their impacts on physiological indicators and subjective                     evaluations; conducting data validity tests on the subjective evaluation                     results, and constructing a comfort prediction model based on correlation                     analysis and support vector regression algorithm. The results show that there                     are significant differences in indicators such as the average RR interval and                     standard deviation of normal-to-normal intervals (SDNN) under different riding                     environments; music in the frequency band of 200Hz to 600Hz can significantly                     improve comfort, and the average relative error of the prediction model is                     8.209%. This study can provide data support for automobile manufacturers to                     optimize the design of suspension systems and seats. At the same time, by                     monitoring the physiological indicators of passengers, the vehicle system can                     adjust the sound signals in real time to alleviate the discomfort caused by                     bumps and enhance the driving experience.]]></description>
      <pubDate>Mon, 27 Oct 2025 17:04:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614445</guid>
    </item>
    <item>
      <title>Impact of acute glucose episodes on adherence to speed limits in the naturalistic setting for drivers with diabetes: An application of linear quantile mixed models</title>
      <link>https://trid.trb.org/View/2602081</link>
      <description><![CDATA[Diabetes can cause complications from hypoglycemic and hyperglycemic episodes, impairing cognitive and motor skills essential for safe driving. Advances in low-cost sensors and wearable technologies have enabled naturalistic driving studies (NDS) that simulate real-world conditions while monitoring drivers’ blood glucose levels. This paper analyzes data from an NDS conducted in Nebraska, focusing on how drivers with Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM), as well as control participants without diabetes, adhere to speed limits of 50–75 mph on highways. Alongside a conventional Linear Mixed Effects Model (LMM), the authors introduce a novel Linear Quantile Mixed Effects Model (LQMM) to evaluate six quantiles (τ = 0.10, 0.25, 0.50, 0.75, 0.85, and 0.90) of speed limit adherence during acute glucose episodes, including hyperglycemia and hypoglycemia. Findings show that hypoglycemia generally leads T1DM drivers to drive more cautiously and remain below speed limits. No significant effects of hypoglycemia or hyperglycemia were observed on T2DM drivers’ speed adherence, suggesting glycemic fluctuations may not substantially influence their behavior. Hyperglycemia was linked to increased caution among T1DM drivers, consistent with evidence of heightened physiological awareness in this group. Control drivers exceeded speed limits more often than those with diabetes, especially relative to T2DM drivers. Roadway characteristics (e.g., traffic flow and speed limits) and age also influence speed behavior, highlighting important contextual factors. By utilizing distribution-based methods like LQMMs that account for participant heterogeneity, this paper presents a nuanced view of speed control patterns, yielding new insights into how diabetes affects driving safety.]]></description>
      <pubDate>Mon, 13 Oct 2025 08:48:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602081</guid>
    </item>
    <item>
      <title>CogStudy Independent Review Panel Report of Recommendations</title>
      <link>https://trid.trb.org/View/2601520</link>
      <description><![CDATA[Objective: This project aimed to provide independent subject matter expertise to the Federal Aviation Administration (FAA) by evaluating alternative neurocognitive tests for aeromedical certification, including consideration of what, if any, future validation research should be performed. Methods: An Independent Review Panel (IRP) of neuropsychology and cognitive-testing experts reviewed neurocognitive test batteries and informational documents and responded to a questionnaire provided by the FAA. The panel produced a consensus report to document their findings and recommendations. The panel deliberated on data collection approaches to promote validity and reliability, and to guide a decision on best practices for utilizing the alternative neurocognitive tests. Results: The IRP provided recommendations on whether validation research should be conducted, along with caveats, implementation considerations, and conditions to support safe, evidence-based integration into aeromedical decision-making. Conclusions: The IRP findings are deliberative and intended to inform FAA aeromedical certification, balancing practical considerations with rigorous scientific standards to safeguard aviation safety.]]></description>
      <pubDate>Fri, 03 Oct 2025 11:54:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601520</guid>
    </item>
    <item>
      <title>Exploring the Correlation between Physiological States and Driving Behavior on Highways with Integrated ECG Measurements</title>
      <link>https://trid.trb.org/View/2571705</link>
      <description><![CDATA[Human driver errors, such as distracted driving, inattention, and aggressive driving, are the leading causes of road accidents. Understanding the underlying factors that contribute to these behaviors is critical for improving road safety. Previous studies have shown that physiological states, like raised heart rates due to stress and anxiety, can influence driving behavior, leading to erratic driving and an increased risk of accidents. In this study, we conducted on-road tests using a measurement system based on the Driver-Driven vehicle-Driving environment (3D) method. We collected physiological signals, specially electrocardiography (ECG) data, from human drivers to examine the relationship between physiological states and driving behaviors. The aim was to determine whether ECG can serve as an indicator of potential risky driving behaviors, such as sudden acceleration and frequent steering adjustments. This information enables automated driving (AD) systems to intervene in dangerous situations. We collected measurements from 22 participants, each tested for 15 minutes on the highway, resulting in a dataset of 330 minutes of physiological data and over 500 km of driving data. The data was segmented into 15-second intervals for detailed analysis. Each segment was labeled twice: physiological states classified as ’stress’ or ’relaxation’ based on heart rate derived from ECG, and driving styles categorized as ’defensive’, ’average’, or ’sporty’ based on CAN-Bus data. Preliminary findings revealed a significant correlation between overall driving behavior on the highway and physiological states. We selected key driving parameters, including velocity, acceleration, lateral acceleration, and yaw rate. We found that acceleration in longitudinal and lateral direction can best indicate driver control and intention, and they vary significantly under two physiological states. This study focuses on how physiological signals change during aggressive driving and aims to establish these signals as indicators for alerting drivers, ultimately reducing the risks of accident associated with aggressive driving behaviors.]]></description>
      <pubDate>Tue, 08 Jul 2025 10:41:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571705</guid>
    </item>
    <item>
      <title>Improving health screening for commercial vehicle drivers</title>
      <link>https://trid.trb.org/View/2550856</link>
      <description><![CDATA[This discussion paper aims to guide stakeholder input by providing preliminary data and evidence and outlining potential options to address screening of commercial vehicle drivers for cardiac risk, diabetes and sleep disorders.]]></description>
      <pubDate>Wed, 07 May 2025 13:46:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550856</guid>
    </item>
    <item>
      <title>Identifying Negative Driver States that Share Commonalities for Interventions</title>
      <link>https://trid.trb.org/View/2539601</link>
      <description><![CDATA[Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the research, we discover that the tendency to explore these singular driver states with only a comparison to “normal” driving is common. Additionally, research on interventions for these driver states is relatively scarce (fewer than 10% of cognitive load-related papers we examined assessed or discussed intervention solutions) and narrowly tailored to specific states [e.g., 4, vis-à-vis cognitive load]. States that share common behavioral and physiological markers tend to be explored independently when a more universal and integrated approach may be warranted. In this paper, we identify the need for a driver state and intervention framework that addresses these limitations by exploring state indicators and their overlap, interventions for one or multiple states, and major research gaps. Our framework offers practical approaches for handling one or many driver states, including interventions that may be deployed at different timings during a trip.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539601</guid>
    </item>
    <item>
      <title>Workload Estimation for a Military Ground Vehicle Crew using Supervised Machine Learning of FACS Action Unit Intensity Data</title>
      <link>https://trid.trb.org/View/2539340</link>
      <description><![CDATA[The proliferation of intelligent technologies in the future battlefield necessitates an exploration of crew workload balancing strategies for human-machine integrated formations. Many current techniques to measure cognitive workload, through qualitative surveys or wearable sensors, are too brittle for the harsh, austere operational environments found in military settings. Non-invasive workload estimation techniques, such as those that analyze physiological effects from video feeds of the crew, present a way forward for workload-aware Soldier-machine interfaces that could trigger events – such as task reallocation – if limits on crew or individual workload are exceeded. One such technique that is being explored is the use of facial expression analysis for workload estimation. We present the performance results of regression and classification models developed from supervised machine learning algorithms that predict pNN50, a common heart rate variability metric used as a physiological measure for workload, from action unit intensity data of the Facial Action Coding System (FACS). Drawing from these results, we propose implementation recommendations for leveraging facial expressions to inform crew workload in workload-aware Soldier-machine interfaces. We conclude with a discussion on open challenges and areas of exploration for non-invasive workload estimation in military vehicle applications.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539340</guid>
    </item>
    <item>
      <title>Integrated Multimodal System for Real-Time Driver Fatigue Detection and Cognitive Load Assessment</title>
      <link>https://trid.trb.org/View/2539108</link>
      <description><![CDATA[As human drivers' roles diminish with higher levels of driving automation (SAE L2-L4), understanding driver engagement and fatigue is crucial for improving safety. We developed an integrated hardware and software system to analyze driver interaction with automated vehicles, with a particular focus on cognitive load and fatigue assessment. The system includes three submodules; namely the Driver Behavior Measurement (DBM), Vehicle Dynamics Measurement (VDM), and the Driver Physiological Measurement (DPM). The DBM module uses electro-optical (EO) and infrared (IR) camera to track a number of facial features such as eye aspect ratio (EAR), mouth aspect ratio (MAR), pupil circularity (PUC), and mouth to eye aspect ratio (MOE). Although determining these metrics from images of the driver’s face in conditions such as low light or with sunglasses is challenging, the paper showed that fusion of EO and IR image analysis produces robust performance. The VDM module utilizes an Inertial Measurement Unit (IMU) to provide vehicular motion data such as speed, acceleration, braking and yaw rate to aid detection of fatigue-related irregularities. A wearable heart rate monitor was used in the DPM module to track driver heart rate as an indicator of stress and fatigue. Data from these modules is fused and processed using a previously published CNN-LSTM model, achieving 90.1% accuracy in detecting fatigue in preliminary tests performed with one driver. The test results show that the system is robust, scalable, and suitable for large-scale studies on driver engagement with highly automated vehicles.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539108</guid>
    </item>
    <item>
      <title>Independent Review Panel to Assess Criteria for Alternative Neurocognitive Tests Validation</title>
      <link>https://trid.trb.org/View/2508959</link>
      <description><![CDATA[The Office of Aerospace Medicine is developing alternative neurocognitive screening tests, partnering with three neurocognitive test developers to create derivative tests tailored to Federal Aviation Administration (FAA) requirements and supported by pilot normative data. However, the neuropsychology community of practice has expressed concerns about the need for revalidation of the derivative tests before clinical adoption. Therefore, an Independent Review Panel is required as soon as practical to evaluate the tests objectively and provide guidance on further development to ensure scientific validity, adherence to professional standards, and alignment with FAA requirements.]]></description>
      <pubDate>Wed, 12 Feb 2025 11:07:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2508959</guid>
    </item>
    <item>
      <title>Noninferiority Trial of a Simulated Kiosk for Remote Aviation Medical Exams</title>
      <link>https://trid.trb.org/View/2499023</link>
      <description><![CDATA[Federal Aviation Administration (FAA) pilot stakeholders have raised concerns about limited access to Aviation Medical Examiners (AMEs) and Human Intervention Motivation Study (HIMS) AMEs in some U.S. regions due to rising demand and a shrinking examiner pool. This mirrors broader U.S. healthcare trends, where telemedicine is addressing provider shortages. To transition pilot exams from in-person to remote, the FAA’s Office of Aerospace Medicine requires evidence that remote exams are as effective as in-person ones. This builds on prior research showing that telemedicine technology can meet pilot medical exam needs. Since exams are performed by designated providers, this research will require collaboration with a healthcare system.]]></description>
      <pubDate>Tue, 28 Jan 2025 11:24:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2499023</guid>
    </item>
    <item>
      <title>Efficacy and feasibility of a breath sensor for detecting driver fatigue and drowsiness</title>
      <link>https://trid.trb.org/View/2452797</link>
      <description><![CDATA[Efficacy and feasibility of a breath sensor for detecting driver fatigue and drowsinessCarl I. Schulman, Chitvan Killawala, Umer Bakali, Jeramy Baum, Emre Dikici, Kevin Miller, Kelly Withum, Sapna Deo, Leonidas Bachas and Sylvia DaunertExploring disparities in cannabis-impaired driving: a sociodemographic and behavioural analysis based on the Canadian Automobile Association (CAA) surveys in Ontario, 2021–2023Renzo Calderón Anyosa, Robert Colonna, Christine M. Wickens, Michael Stewart and Brice BatomenInflammatory protein elicitation in response to whole-body vibration exposureNicholas Miller, Suzanne Konz, Steven Leigh and Holly CyphertMotor vehicle crash occupants with tibial fracture have different outcomes based on patient zip codeDanelson K. A., Cooper A., Gwam C., Reiser J., Henry K. and Pilson H.Pedal confusion, pedal errors, and consequent unintended accelerations in drivers with diabetic peripheral neuropathyM. Esselaar, M. Perazzolo and D. E. Marple-HorvatInvestigating the determinants of over-speeding behavior among car drivers in India using theory of planned behavior and psychological flow theoryHarshita Joshi and Ankit Kumar YadavRisk factor for serious injury of far-side occupants in motor vehicle side crashes using the KIDAS (Korean In-Depth Accident Study) dataChan Young Kang, Kang Hyun Lee, Oh Hyun Kim, Yeon Il Choo, Dooruh Choi, Dong Gu Kang, Jin Ho Yu, Jung Hun Lee Hee Jin Kim and Hee Young Lee]]></description>
      <pubDate>Mon, 27 Jan 2025 15:11:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452797</guid>
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