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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Numerical Methodology for Aircraft Light Explosion Proofness Study</title>
      <link>https://trid.trb.org/View/2712092</link>
      <description><![CDATA[The increasing demand for safety and reliability in aerospace applications necessitates rigorous testing of aircraft components, including light units, for explosion proofness. Traditional explosion proofness tests are destructive, expensive, and time-consuming, requiring significant resources for test setups and prototypes. To address these challenges, this research presents a numerical methodology using Computational Fluid Dynamics (CFD) simulations to investigate the explosion proofness for aircraft light units. The primary motivation of this study is to establish a computational framework that supports early-stage design screening, reduces the number of physical prototypes, and enhances understanding of explosion behavior before formal qualification testing.This work contributes to advancing engineering practices in the aerospace industry by demonstrating the efficacy of CFD simulations in evaluating and enhancing the explosion proofness of light units. The proposed CFD model, implemented in ANSYS Fluent, adheres to the standards outlined in DO 160 for case setup, ensuring the accuracy and relevance of the simulation results. The methodology involves creating a simulation domain for the light unit, initially containing an air-fuel mixture with a localized high-temperature region to initiate ignition. This setup replicates the conditions of actual explosion proofness tests, providing a realistic assessment of light unit performanceThis CFD simulation methodology incorporates reduced chemical reaction mechanisms to model the explosion process effectively. By simplifying the chemical reactions involved, the computational load is minimized, making the simulations both accurate and feasible. This approach ensures that the CFD model can provide precise insights into the explosion dynamics while maintaining computational efficiency.]]></description>
      <pubDate>Wed, 10 Jun 2026 13:20:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712092</guid>
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    <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>Simulation based Prediction and Optimization of Acoustic Comfort in Vehicle Interiors</title>
      <link>https://trid.trb.org/View/2692125</link>
      <description><![CDATA[Passenger expectations for quiet and acoustically comfortable vehicle interiors have increased significantly, driven by advancements in electric vehicles and premium audio systems. Acoustic comfort affects perceived quality, communication ease, and overall driving experience. This paper presents a simulation-driven methodology to predict and optimize interior noise performance during the early design phase, focusing on high-frequency acoustic transfer functions and trim material absorption properties. Traditional NVH development relies heavily on physical testing, which is time-consuming and costly. Early-stage predictive tools are essential to evaluate acoustic performance before prototype availability. High-frequency noise (1kHz–12kHz) is particularly challenging due to complex reflections and absorption behavior. Acoustic trims play a critical role in shaping the cabin’s sound field, and their properties must be optimized to achieve desired sound quality. A novel simulation approach is developed using Raytracing (Beam + Particle) to model sound propagation within the vehicle cabin. The method calculates ATFs between point sources (e.g., door panels) and receiver positions (passenger ears), enabling spatially resolved acoustic analysis. This supports early design evaluations by predicting how changes in geometry and materials affect perceived noise levels. Using HEEDS, a DOE-based optimization is performed on frequency-dependent absorption properties of acoustic trims. The trim package includes carpet, headliner, seats, doors, and firewall. The optimization targets mid-to-high frequency ranges where material behavior significantly influences sound quality. Multiple design iterations are evaluated to identify configurations that minimize intrusive noise and enhance tonal balance. A full-vehicle correlation study is conducted to validate the simulation results. Measured ATFs from a physical prototype are compared with simulated data. The acoustic trim package used in the prototype includes all major components. The Raytracing-based ATF model shows strong correlation with measured data. The methodology enables early identification of design choices that degrade or enhance acoustic comfort.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692125</guid>
    </item>
    <item>
      <title>Bias and Uncertainty in the Time, Position, and Speed Data of Consumer-Grade GPS Devices</title>
      <link>https://trid.trb.org/View/2692091</link>
      <description><![CDATA[The goal of this study is to quantify the accuracy (bias) and precision (uncertainty) of the time, position, and speed data acquired by a range of consumer-grade devices (4 bike computers, 5 watches, 1 application on 3 smart phones, and a camera) that access Global Positioning System (GPS) satellite signals. We acquired data at each device’s maximum sampling rate (typically 1 Hz) during 207 minutes (twelve sessions of ~17 min) over 61.6 km of road cycling. The time and position data from these devices were compared to real-time kinematic (RTK) data acquired using a differential GPS system, and speed data from these devices were compared to a high-resolution wheel speed sensor synchronized to the RTK data in order to statistically estimate the bias and 95th percentile confidence intervals of the uncertainty of the devices’ data. Overall, we found the position and speed data from the devices generally lagged the reference by 4 s or less, although the lags between the speed and position data within a device were less (0.0 to 1.2 s) and more precise. We found small position biases (0.1 to 1.4 m), although the major axis of the 95th percentile confidence ellipses of the position uncertainties ranged from ±3.4 to ±7.2 m across the devices. The speed biases were also small (-0.6 to 0.0 m/s) and had 95th percentile confidence intervals that were between 0.35 and 1.04 m/s wide. These findings help establish the accuracy and uncertainty across a range of consumer-grade GPS-enabled devices and to probabilistically interpret these data for collision reconstruction purposes.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692091</guid>
    </item>
    <item>
      <title>A Dataset for Visual Classification of Battery Fire and Smoke</title>
      <link>https://trid.trb.org/View/2692074</link>
      <description><![CDATA[Battery fires pose a significant risk across a wide range of applications, including electric vehicles, consumer electronics, and grid-scale energy storage systems. Early detection of fire and smoke is critical to preventing catastrophic failures and ensuring human safety. In this study, we developed a synthetic dataset of battery fire and smoke images in the context of a simple battery pack. The primary application of this dataset is to support the development of a machine learning–based visual classification system capable of accurately detecting battery fires and smoke in real time at an early stage. The intended outcome is a deployable classification system that enhances battery safety through rapid visual identification of hazardous conditions.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692074</guid>
    </item>
    <item>
      <title>HaloBus: Edge Computing-Enabled Real-Time Boarding and Exit Detection for Enhanced Transportation Safety Using Lightweight AI</title>
      <link>https://trid.trb.org/View/2692073</link>
      <description><![CDATA[This paper proposes HaloBus, an innovative, edge-computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems place the burden of implementation on individual households, often requiring independent methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and ensuring child safety is a primary concern for parents and caregivers. That is why HaloBus was invented. The system employs YOLOv5us—an Ultralytics-enhanced, anchor-free, split-head architecture that offers a superior accuracy speed trade-off. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident response to proactive safety. Trained on over 70,000 labeled and unlabeled images, the model can accurately detect multiple students simultaneously, significantly reducing false positives. In real-world deployment, the model sustained 30 frames per second on the Raspberry Pi and achieved detection confidence levels exceeding 75% even when subjects wore sunglasses or hoodies. With opt-in participation for each family, HaloBus effectively balances detection efficiency and privacy protection. Overall, HaloBus offers a low-cost, scalable, and ethically conscious approach to enhancing school-bus safety by delivering reliable, on-device boarding and exit detection for multiple students in varied real-world conditions.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692073</guid>
    </item>
    <item>
      <title>A Cooperative Hand Gesture Recognition System for Human Machine Interface to Control Robotic Arms</title>
      <link>https://trid.trb.org/View/2691972</link>
      <description><![CDATA[In the context of Industry 5.0, effective human–machine collaboration requires seamless and natural interaction. Hand-Gesture Recognition (HGR) has emerged as a promising technology for developing human–machine interfaces (HMI) that enable users to control robotic systems without physical controllers or wearable devices. This research presents a real-time HGR system designed to control a 6-Degree-of-Freedom (DoF) robotic arm using YOLOv10, a state-of-the-art deep learning model for hand gesture detection and classification. While YOLOv10 delivers high recognition accuracy, its computational demands surpass the capabilities of edge devices typically mounted on robotic platforms, creating a hardware bottleneck. To address this challenge, a cooperative client–server architecture is proposed, distributing computational workload between the edge device and a more powerful remote server. An RGB camera attached to the robotic arm captures hand gesture images and transmits them to the server via the User Datagram Protocol (UDP). The server performs real-time inference using YOLOv10 and returns the detection results to the edge device, which translates the recognized gestures into corresponding robotic arm movements. Experimental evaluation demonstrates an interfacing speed of approximately 15.7 frames per second and an 11.54 times improvement in performance compared with standalone edge-based processing. The proposed cooperative HGR system successfully integrates advanced computer vision techniques with robotic control to deliver a responsive, touch-free interface, enabling smooth, natural HMI. By overcoming edge-computing limitations, this research contributes to the advancement of Industry 5.0, supporting applications in healthcare, assistive robotics, industrial automation, and collaborative robotics, and promoting effective and safe human–machine collaboration.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691972</guid>
    </item>
    <item>
      <title>Acoustic Simulation for a Vehicle Audio System Across the Full Audible Frequency</title>
      <link>https://trid.trb.org/View/2691956</link>
      <description><![CDATA[The Audio system is an important part of the design of a vehicle cabin. In the vehicle development process, the audio system needs to be tuned for optimal acoustic performance. Traditionally, this process is performed physically on vehicles. In this paper, a methodology is developed to numerically simulate the acoustic performance of the audio system across the full audible frequency range. To provide validation of the method, the p/v acoustic transfer functions (ie., the sound pressure p at the passengers’ ears divided by the voltage inputs v) are measured for different speakers in a production vehicle. As the sound perceived by the passengers depends on both the source and the path, the method development is split into two parts: (a) characterization of parameters that describe the loudspeaker as a source and (b) representation of the vehicle cabin as a path. The speaker parameters are characterized from sound radiation data measured in a 2pi chamber. To represent the vehicle cabin, a hybrid BEM-SEA model is utilized in which the cabin is fully deterministic below 1kHz and is statistical between 1 kHz and 20 kHz. The speaker model is then integrated into the cabin model in order to predict the acoustic transfer functions. This model accounts for two-way coupling between the speakers and the cabin. The results show that the predicted transfer functions are in very good agreement with the data from acoustic measurements. Therefore, performing the audio tuning virtually by numerical simulation is a feasible solution for the industry.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691956</guid>
    </item>
    <item>
      <title>Innovative Smart Switch Architecture Catering to 12 Volts to 48 Volts Battery Bus Requirements</title>
      <link>https://trid.trb.org/View/2691848</link>
      <description><![CDATA[Electronics is entering rapidly into all automotive subsystems, performing control and monitoring tasks apart from making the entire vehicle intelligent. Interface with the external automotive eco-system needs careful attention during the system design. It defines how seamlessly the electronic unit interacts with rest of the vehicle. It needs to do so in an effective manner without compromising on cost and other automotive application constraints. This paper focusses on the “smart switch building block” that forms heart of an automotive output interface echo system.: Its importance stems from the fact that, a smart switch is an indispensable building block for any electronic control system driving external loads. As various novel electical and electronics architectures are entering various vehicle segments, the need for a single reusable solution that will cater to 12 Volts to 48 Volts battery buses is increasingly being felt. However, no prevelant solution meets this requirement. Even for 12 volts and 24 volts buses different solutions are sometimes required to be used. Other areas where the existing solutions need improvement include ease of hardware and software interface apart from lack of robust short-circuit protections. Diagnostics architecture of currently available (legacy) smart switch solutions add to the complexity of the interface for interpretation of the malfunctions. This paper proposes a novel architecture that attempts to address all these short-comings across the buses. This not only reduces the time to market but also reduces engineering and Bill of Material (BOM) costs due to a frugally engineered solution.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691848</guid>
    </item>
    <item>
      <title>Structuring Cross-Disciplinary Development: A Systems Engineering
          Framework for ASPICE-Compliant Mechatronic and Cyber-Physical
          Engineering</title>
      <link>https://trid.trb.org/View/2691833</link>
      <description><![CDATA[Mechatronic and cyber-physical systems emerge from interdisciplinary design                     efforts, integrating software (SW), electronics, and mechanical components.                     Developing such systems places high demands on organizations and processes,                     particularly regarding efficient collaboration across domains. A key challenge                     lies in establishing organizational structures and workflows which allow                     cross-discipline work and at the same time ensure compliance with regulations                     and adherence to standards such as Automotive Software Process Improvement and                     Capability Determination (ASPICE). In response, the authors have developed an                     Engineering Process Framework (EPF) grounded in International Council on Systems                     Engineering (INCOSE) systems engineering principles. The EPF provides a                     structured approach for system development and therefore defines company-wide                     processes and methods. This paper presents the development of the EPF’s                     functional logic and its implementation within a tool landscape. Furthermore, a                     selected process is used to illustrate how the EPF supports development                     engineers in their daily work.]]></description>
      <pubDate>Tue, 14 Apr 2026 14:56:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691833</guid>
    </item>
    <item>
      <title>Heat Dissipation with Heat Pipe Using Particle Swarm Optimization and Being Used for High Power LED</title>
      <link>https://trid.trb.org/View/2113803</link>
      <description><![CDATA[Electronic components will generate heat in the process of operation, and the heating power consumption of components will gradually increase with the decrease of volume. With the volume miniaturization and integration, the heat flux will continue to increase. Therefore, in order to ensure the reliability of electronic components, heat dissipation treatment must be carried out, and heat pipe cooling is one of the effective methods. For the structure design of heat sink with heat pipe, this paper uses an improved particle swarm optimization algorithm to reduce the maximum temperature at the bottom of heat sink by optimizing the length and number of fins and the number of heat pipes. At the same time, the designed heat sink is used for heat dissipation of high power LED, and the experimental results show that it can fully meet the requirements of LED heat dissipation in practical application.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:15:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113803</guid>
    </item>
    <item>
      <title>Electro Magnetic Radiation Analysis for the Vehicle Cable and Shielding Techniques and Performance Assessment</title>
      <link>https://trid.trb.org/View/2663539</link>
      <description><![CDATA[This study discusses the generalized workflow and design techniques for detecting radiated emissions from vehicle electronic systems to ensure an electromagnetic compatible (EMC) vehicle specified by radiated emission standards such as CISPR-12 and CISPR-25. In this work, CST studio suite software is used to examine the vertical polarization in an E vehicle. The results of the radiated emission are plotted as dBμV/m vs Hz to understand the radiation effects generated by different electronic devices across different frequencies. The discussed method serves as a guide for forming a virtual electromagnetic environment where a real vehicle is simulated to study the interference effects and design a suitable filter to reduce the effect of EMI.]]></description>
      <pubDate>Mon, 02 Feb 2026 16:36:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663539</guid>
    </item>
    <item>
      <title>MOSAIC-TARA: A Comprehensive TARA Methodology for Automotive Cybersecurity</title>
      <link>https://trid.trb.org/View/2663414</link>
      <description><![CDATA[Threat Analysis and Risk Assessment (TARA) is a continuous activity, acting as a foundation of cybersecurity analysis for electrical and electronics automotive products. Existing TARA methodologies in the automotive domain exhibits challenges due to redundant and manual processes, particularly in handling recurring common assets across Electronic Control Units (ECUs) and functional domains. Two primary approaches observed for performing TARA are Manual-Asset-Centric TARA and Catalogue-Driven TARA. Manual-Asset Centric TARA is constructed from scratch by manually identifying the assets, calculating risks by likelihood, and impact determination. Catalogue-Driven TARA utilizes the precompiled likelihood and impact against identified assets. Both approaches lack standardized and modular mechanisms for abstraction and reuse. This results in poor scalability, increased efforts, and difficulty in maintaining consistency across vehicle platforms. The proposed method in this research overcomes such challenges, named as “MOSAIC-TARA”. It is a Modular, Scalable, Adaptive, Interoperable, Comprehensive TARA, decomposing a vehicle system into functional domains or ECUs, and further into its components. Each module is independently assessed and analyzed for potential threats, damage scenarios, and security goals, formulating the multiple TARA modules. These independent individual TARAs are then aggregated based on the architecture to derive ECU level TARA. The modularity of this method supports reusability of assessments across different ECUs, functional domains, and vehicle platforms. This enables optimized and efficient TARA tailored to different system configurations. Additionally, the presented approach introduces damage scenarios classification based on impact criticality, as the same component may lead to varying damage impact depending on the context. Thus, the TARA modules are developed for various levels of damage impacts, provides adaptability towards impact criticality of selected ECU or its functions. MOSIAC-TARA aligns with ISO/SAE 21434 and supports efficient reusable risk-driven design.]]></description>
      <pubDate>Mon, 02 Feb 2026 16:36:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663414</guid>
    </item>
    <item>
      <title>Research on the Construction of Hardware Reliability System of Electronic Equipments for Rail Traffic Vehicles</title>
      <link>https://trid.trb.org/View/2614467</link>
      <description><![CDATA[Reliability engineering is a science and technology to fight against product failure, which includes reliability requirements and allocation, reliability analysis, reliability modeling and prediction, reliability design, reliability test, reliability testing, operational reliability and other activities. The important condition for the high-quality development of rail traffic is the stable operation of equipment, and the electronic equipment of rail traffic vehicles is mostly the “brain” of the key system. At present, the contradiction between performance optimization and structural complexity is increasingly prominent. In order to cope with the variable operating conditions and harsh environment of vehicles, the requirements for reliability are getting higher and higher. It is of great significance to carry out reliability engineering for its high-quality development. This paper introduces the construction of the reliability system of the electronic equipment of rail traffic vehicles, discusses the guiding ideology of the construction of the electronic equipment reliability system based on the actual situation, expounds the content of the reliability system from three aspects: management system, technical system and resource system, and introduces each reliability activity in detail and with emphasis based on the practical application experience. Combined with the experience and lessons, the paper puts forward some suggestions on the construction of reliability system.]]></description>
      <pubDate>Tue, 16 Dec 2025 09:26:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614467</guid>
    </item>
    <item>
      <title>Charting Digital Waters: Strengthening U.S. Coast Guard marine casualty investigations with electronic evidence</title>
      <link>https://trid.trb.org/View/2635920</link>
      <description><![CDATA[Electronic evidence has become a crucial element of U.S. Coast Guard marine casualty investigations.  For example, voyage data recorders (VDRs) provide investigators with a comprehensive, chronological record of events leading up to an incident, enabling a thorough understanding of voyage events and factors contributing to a complex accident. Recognizing the escalating importance and intricate nature of digital evidence in marine casualty investigations, the Coast Guard has strategically developed specialized units to expertly manage the complexities of electronic data collection, preservation, and in-depth analysis. These include the Investigations National Center of Expertise (INCOE), the Digital Forensics Laboratory (DFL), and selected members from the Coast Guard Auxiliary. These units apply cyber forensic best practices to the accuracy and relevance of the data they gather while ensuring evidence is properly collected, safeguarded, and maintained. This disciplined approach preserves the integrity and defensibility of the entire investigative process, ensuring that digital evidence can withstand external scrutiny and support the success of the investigation.]]></description>
      <pubDate>Fri, 05 Dec 2025 14:12:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635920</guid>
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