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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>TSCFNet: Temporal Spectral Feature Cross Fusion Network for Imbalanced Sea State Estimation in Autonomous Ships</title>
      <link>https://trid.trb.org/View/2685877</link>
      <description><![CDATA[Sea state estimation (SSE) is critical to the safety of maritime transport and the reliability of autonomous ships. The frequency of different sea states varies significantly, leading to uneven data distribution. Existing deep learning methods for SSE typically focus on feature extraction, often using simple splicing and fusion, which can result in cross-domain incoherence and degrade model performance. Addressing sea state classification imbalance is often done through distance-based classifiers (e.g., prototype classifiers), but these can be less sensitive to minority classes, and using few prototypes for a class limits the expression of intra-class variations. To overcome these challenges, we propose the Temporal Spectral Cross Fusion Network (TSCFNet), which extracts temporal and spectral features. These are integrated via an innovative temporal spectral cross fusion module to maximize their complementary advantages. Additionally, we introduce a multi-fusion loss function, including temporal, spectral, and fusion losses, to optimize features across different dimensions. This approach improves the performance for minority classes and captures intra-class differences more effectively, solving the problem of category imbalance. Experimental results show that TSCFNet significantly outperforms baseline methods on two imbalanced sea state datasets and multiple multivariate spatio-temporal datasets.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:33:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685877</guid>
    </item>
    <item>
      <title>CDFIT: A Transformer Using Cross-Modal Dual-Stream Feature Interaction for Multispectral Pedestrian Detection</title>
      <link>https://trid.trb.org/View/2685853</link>
      <description><![CDATA[Modality imbalance is a significant challenge for multi-modal interaction at various depths in multispectral pedestrian detection under varying illumination environments. To overcome the limitations of current cross attention in addressing the modality imbalance, we propose the Cross-Modal Dual-Stream Feature Interaction Transformer (CDFIT). CDFIT capitalizes on the Transformer’s ability to learn long-range dependencies, extracting global intra-modal and inter-modal correlations during the feature interaction phase. Crucially, in order to effectively eliminate the interference of the self-attention within one modality to the alternative one, we propose horizontal and vertical correlation decoupling modes to divide and reassemble the attention maps in CDFIT. This facilitates more purified inter-modal attention while preserving relevant intra-modal self-attention, reducing the information interference. Meanwhile, in CDFIT, we expand Transformer into dual-stream pathways to align and assemble the information from RGB and thermal modalities across depths separately, thereby greatly enhancing the performance of multispectral object detection. Comprehensive experiments and ablation studies on benchmark datasets demonstrate that CDFIT achieves superior performance compared with state-of-the-art methods.]]></description>
      <pubDate>Wed, 26 Aug 2026 16:38:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685853</guid>
    </item>
    <item>
      <title>Anisotropic Diffusion-Based Denoising for Enhanced Road Segmentation in Multispectral Satellite Imagery</title>
      <link>https://trid.trb.org/View/2729554</link>
      <description><![CDATA[Precise road maps are vital for urban planning, transportation management, and emergency response in a diverse nation like India that has a road length of more than 6.67 million kilometres made up of various road types including national highways, state highways, and rural roads. Acquiring roads from satellite images is challenging due to noise, shadows and varying characteristics of roads in complex urban and rural landscapes. So, to solve this, the proposed work offers a hybrid framework that integrates anisotropic diffusion, which removes noise while maintaining road edges, and a U-Net deep learning model for pixel-wise road segmentation. The developed system uniquely uses OpenStreetMap (OSM) data for validation and corrections, allowing the system to improve road boundaries as well as locate new/unmapped roads. Additional image augmentation methods, such as rotation, flipping, and scaling will improve the performance of the model within complex Indian environments. The experimental results indicate a mean IoU of 95%, resulting in an improvement of 23% over the baseline U-Net and outperformed additional models such as DeepLabV3+. The outputs are generated as maps that can be used as GIS compatible maps which can be used instantly in real-world applications. This framework offers a deployable, scalable and accurate approach to mapping the complex road systems in India, supporting urban planning, navigation updates and disaster response.]]></description>
      <pubDate>Fri, 21 Aug 2026 17:00:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2729554</guid>
    </item>
    <item>
      <title>Csnr and Jmim Based Spectral Band Selection for Reducing Metamerism in Urban Driving</title>
      <link>https://trid.trb.org/View/2732136</link>
      <description><![CDATA[Protecting Vulnerable Road Users (VRU) remains a key challenge in automotive perception, particularly when RGB imagery exhibits metameric ambiguity under challenging illumination. Hyperspectral Imaging (HSI) can provide material-dependent cues beyond RGB, including in the Near-Infrared (NIR), but its high dimensionality limits deployment in automotive systems. This paper proposes a compact band-selection pipeline that combines joint mutual information maximization with a patch-based contrast signal-to-noise ratio criterion to select a practical 3-band HSI subset for VRU perception. On the Hyperspectral City V2 dataset, the proposed pipeline selects a VIS-NIR triplet at 521nm, 753nm, and 903nm. We evaluate VRU-Road separability under higher-illumination scenes and an objectively defined low-illumination subset using per-scene distributions, and bootstrap confidence intervals. Results are reported in both a metric-ready representation (for direct comparison with co-registered RGB) and a sensor-native representation. Downstream utility is further evaluated by semantic segmentation using U-Net, DeepLabV3+, and PSPNet over three random seeds. The selected triplet improves VRU-Road separability on key criteria, remains robust under low illumination, and yields competitive downstream performance among the evaluated HSI-derived inputs. Overall, the proposed approach reduces spectral dimensionality from 128 bands to 3 (97.7% reduction) and supports compact multispectral sensing as a practical complement to RGB for robust VRU perception.]]></description>
      <pubDate>Wed, 19 Aug 2026 09:27:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732136</guid>
    </item>
    <item>
      <title>Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation</title>
      <link>https://trid.trb.org/View/2732097</link>
      <description><![CDATA[Hyperspectral sensing provides rich spectral information for scene understanding in urban driving, but its high dimensionality poses challenges for interpretation and efficient learning. We introduce Learnable Quantum Efficiency (LQE), a physics-inspired, interpretable dimensionality reduction (DR) method that parameterizes smooth high-order spectral response functions that emulate sensor quantum efficiency curves. Unlike conventional methods or unconstrained learnable layers, LQE enforces physically motivated constraints, including a single dominant peak, smooth responses, and bounded bandwidth. This formulation yields a compact spectral representation that preserves discriminative information while remaining fully differentiable and end-to-end trainable within semantic segmentation models (SSMs). We conduct systematic evaluations across three publicly available multi-class hyperspectral urban driving datasets, comparing LQE against six conventional and seven learnable baseline DR methods across six SSMs. Averaged across all SSMs and configurations, LQE achieves the highest average mIoU, improving over conventional methods by 2.45%, 0.45%, and 1.04%, and over learnable methods by 1.18%, 1.56%, and 0.81% on HyKo, HSI-Drive, and Hyperspectral City, respectively. LQE maintains strong parameter efficiency (12-36 parameters compared to 51-22 K for competing learnable approaches) and competitive inference latency. Ablation studies show that low-order configurations are optimal, while the learned spectral filters converge to dataset-intrinsic wavelength patterns. These results demonstrate that physics-informed spectral learning can improve both performance and interpretability, providing a principled bridge between hyperspectral perception and data-driven multispectral sensor design for automotive vision systems.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732097</guid>
    </item>
    <item>
      <title>Yt-Swin: Context-Adaptive Spectral Fusion for Cross-Modal Uav Remote Sensing</title>
      <link>https://trid.trb.org/View/2732055</link>
      <description><![CDATA[Accurate object detection in multimodal remote sensing images remains a significant challenge for autonomous all-weather systems, necessitating adaptive integration of complementary RGB and infrared (IR) data under diverse environmental conditions. Existing fusion approaches often rely on fixed, manually designed strategies or complex multi-expert frameworks, which lack the flexibility to dynamically achieve fusion policies in response to real-time sensor degradation. To address these limitations, we propose YT-SWIN, a novel adaptive fusion framework that synergistically combines the YOLOv10n backbone with a Swin Transformer-based attention mechanism to achieve environment-aware multimodal perception. Our work introduces three core modules: first, Self-Supervised Environment Inference Module that learns to characterize weather and illumination conditions directly from input data, eliminating the reliance on labeled environmental metadata. Second, a Learnable Frequency Decomposition Block that constructs optimized spectral representations through data-adaptive filtering, enhancing multimodal feature representation. Third, a Hierarchical Cross-Modal Attention Network (HCAN) built upon lightweight Swin Transformer blocks dynamically gates and fuses RGB and IR features across multiple spatial and frequency scales, conditioned on the inferred environmental context. Additionally, an adaptive Channel Pruning Strategy integrated into the multi-scale feature pyramid maintains real-time inference speeds without compromising detection accuracy. Extensive experiments demonstrate that YT-SWIN achieves state-of-the-art performance, attaining mAP scores of 83.08% on the DroneVehicle dataset and 77.4% on the VEDAI dataset while operating in real time. The framework demonstrates strong generalization capability across environments, establishing a new paradigm for efficient, adaptive, and robust multimodal perception in unstructured outdoor scenarios.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732055</guid>
    </item>
    <item>
      <title>Track Irregularity Prediction and Effect on the Dynamic Response of the 600 Km/H Maglev System</title>
      <link>https://trid.trb.org/View/2731827</link>
      <description><![CDATA[The absence of 600 km/h operational high-speed Maglev lines leads to a unavailability of empirical track irregularity data at this speed, challenging accurate assessment of the system's dynamic response. This study presents a framework for track irregularity estimation suitable for 600 km/h applications, integrating measured data at 430 km/h and dynamic simulation outputs from a vehicle-magnetic force-track coupling model. By implementing filters and wavelet signal decomposition, the effective wavelength of track irregularity spectrum is extended to 1 m–200 m, optimizing its suitability for dynamic calculation at high-speed operation. Additionally, the autoregressive (AR) spectral estimation is employed to preserve characteristic frequencies of high-speed Maglev systems while enhancing the resolution of the irregularity spectrum. Finally, the obtained track irregularity spectrum is numerically inverted and applied into the dynamic coupling model for 500 sets of dynamic simulation analysis. The results show that the dynamic response of the 600 km/h Maglev system under the irregularity proposed in this study is greater than that under the irregularity proposed in existing research and 430 km/h measured irregularity. The track irregularity obtained in this study is more accurate and appropriate for evaluating the dynamic characteristics of 600 km/h Maglev systems. The stochastic results show there is a probability of 0.04% that the stability index of the car body surpasses the threshold of 2.5, while the minimum levitation gap of individual points exceeding the permissible range of ±4 mm.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731827</guid>
    </item>
    <item>
      <title>Wheel-Speed-Sensor-Based Spectral Classifier for Road Surface Roughness</title>
      <link>https://trid.trb.org/View/2685834</link>
      <description><![CDATA[In this paper, we propose a novel signal processing method for road surface roughness classification exclusively from wheel speed sensor signals. Road surface quality has a significant impact on fuel consumption and driving safety. Traditionally, it has been measured using specially equipped vehicles and, more recently, shared via cloud-based infrastructure; however, such data can be unavailable or quickly become outdated, making onboard solutions essential. We analyzed a large wheel speed sensor dataset from various test maneuvers to determine how road surface roughness influences spectral characteristics under different conditions, including changes in speed, tire pressure, payload, and tire type. The proposed road surface roughness classifier uses only wheel speed sensor signals. It selects signal segments appropriate for processing based on driving conditions and computes their order spectra. The number and relative power of the spectral peaks within the identified interval of interest of the order spectrum are related to road surface roughness. The implemented classifier is capable of distinguishing between rough and smooth road surfaces based on the number of peaks in the interval of interest. The overall accuracy of the implemented road surface roughness classifier was $87.4 \,\%$.]]></description>
      <pubDate>Mon, 10 Aug 2026 11:16:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685834</guid>
    </item>
    <item>
      <title>Edge-V: Vehicular Edge Intelligence through Multi-Band Unlicensed Spectrum Access</title>
      <link>https://trid.trb.org/View/2731545</link>
      <description><![CDATA[Technological advances in the automotive field are driving the development of smarter, greener, and more autonomous vehicles. These vehicles will need to communicate via Vehicle-to-Everything (V2X) wireless communications and perform advanced Deep Learning (DL) tasks while handling large data volumes with low latency and high reliability. Although 5G is frequently viewed as a comprehensive solution for addressing the demanding environment of next-generation autonomous vehicles and of Vehicular Edge Intelligence (VEI), relying solely on cellular networks poses challenges like spectrum congestion, delays in edge offloading, and poor coverage in certain areas. Current unlicensed spectrum technologies also fall short of the VEI requirements. On this basis, we propose Edge-V, a novel framework combining unlicensed spectrum technologies to provide low-latency, high-throughput connectivity with reliable task offloading. Edge-V uses a Dedicated Short-Range Communications (DSRC) link for exchanging standardized messages, traditional Wi-Fi for connecting on-board devices and sensors, and mmWave for high-speed, low-latency connectivity. With the aim of optimally allocating tasks, an Offloading Manager module is included, based on a system model which is mathematically formulated, and used to propose a sample greedy strategy within Edge-V. Our laboratory and field tests, thanks to an open and low-cost Proof-of-Concept, show that Edge-V can reduce latency by up to 65% when compared to cellular/cloud-based solutions.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731545</guid>
    </item>
    <item>
      <title>Yolo-Mslite: Lightweight Multispectral Object Detection Algorithm with Feature Channel-Wise Knowledge Distillation for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2730964</link>
      <description><![CDATA[The assisted driving system is a key strategy for promoting the growth of science and technology since it improves the driving experience, ensures stable vehicle operation, and protects the lives of drivers. Object detection algorithms, which are the basic technology of the assisted driving system, significantly influence its stability and sensitivity. By fusing visible and infrared pictures, multispectral object detection (MOD) methods have been suggested to improve detection accuracy. Nonetheless, the current approaches for feature-level fusion detection exhibit low detection efficiency. To solve this issue, we present YOLO-MSLite, a lightweight multispectral object recognition technique based on feature-channel-wise knowledge distillation. The technique improves the Conv and C3 modules of the YOLOv5 backbone by introducing group convolution, which decreases the number of parameters while allowing the one-stream network to interact with features. To increase the information selection capabilities of YOLO-MSLite, two-stream, and one-stream models are employed as the teacher and student models, respectively. Experiment findings on several datasets show that YOLO-MSLite achieves the same degree of accuracy as existing state-of-the-art approaches while being lighter in structure and more efficient in detection. The validation findings of the algorithm installed on an embedded platform further reveal that the model gets good detection results and can reach the level of real-time detection.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730964</guid>
    </item>
    <item>
      <title>Noma Systems over Fluctuating Nakagami-M Fading Channels</title>
      <link>https://trid.trb.org/View/2730709</link>
      <description><![CDATA[Non-orthogonal multiple access (NOMA) is an emerging promising technology for beyond 5G and 6G networks. However, it is essential to investigate its performance over various fading channels. In this work, we shed light on the efficiency of downlink transmission of NOMA systems over the recently proposed Fluctuating Nakagami-m fading channels whose applications cover vehicle-to-vehicle (V2V) communications, and device-to-device (D2D) communications among others. Specifically, we consider an analytical study on the performance of a NOMA transmission in terms of the outage probability, diversity gain, coding gain, and ergodic capacity. The results show that the diversity gain of each user depends on the total number of multipath clusters (m) of the Fluctuating Nakagami-m fading channel, while the coding gain depends on several parameters including m, the fading severity parameter (ms), power allocation scheme, imperfection of the successive interference cancellation (SIC) scheme, and the QoS constraint. Two power allocation schemes have been investigated and compared. Representative simulations have validated the accuracy of the analytical derivations.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730709</guid>
    </item>
    <item>
      <title>Optimization of Spectrum Resource Allocation for Vehicle Platoon in V2x Networks Based on Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2727846</link>
      <description><![CDATA[The 5G NR-V2X standard explicitly identifies vehicle platoon as a key application scenario and considers it an essential component for achieving advanced autonomous driving. Therefore, this paper focuses on spectrum allocation optimization for vehicle platoons in the Internet of Vehicles (IoV). To minimize the Age of Information (AoI) between the platoon leader and the infrastructure while maximizing the data transmission probability within the platoon, a multi-agent deep reinforcement learning algorithm is proposed. This algorithm integrates random network distillation and gradient entropy minimization to jointly optimize communication pattern switching, channel selection, and power control. Considering the large state observation space caused by the time-varying channel conditions in IoV, a random network distillation module based on an intrinsic reward mechanism is introduced to enhance the state exploration capability of vehicle platoons. Furthermore, to strengthen coordination among platoon members, a gradient entropy minimization-based approach is employed to improve credit assignment, enabling platoons to accurately assess their contribution to global performance and dynamically adjust their strategies. Simulation results validate the effectiveness of the proposed algorithm, demonstrating superior performance in reducing AoI and improving data transmission probability in platoon-based vehicular communications.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727846</guid>
    </item>
    <item>
      <title>SSA-SVR-NGO based hydrodynamic derivatives identification through full-scale sea trial data</title>
      <link>https://trid.trb.org/View/2725487</link>
      <description><![CDATA[Ship maneuvering derivatives are conventionally estimated at the design stage through towing-tank experiments or computational fluid dynamics (CFD) simulations. However, these traditional approaches are frequently limited by high operational costs and experimental complexity. To provide a more versatile and economical solution for in-service vessels, this study proposes an integrated identification framework based on Singular Spectrum Analysis (SSA), Support Vector Regression (SVR), and Northern Goshawk Optimization (NGO). Initially, SSA is utilized to preprocess full-scale trial data, effectively extracting underlying kinematic trends from high-frequency environmental noise. Subsequently, SVR-based regression models are established for hydrodynamic derivative identification, with the NGO algorithm applied to autonomously optimize the model hyperparameters. Furthermore, an iterative sample refinement strategy is introduced as a post-processing step to eliminate non-physical outliers, thus enhancing the physical consistency of the identified derivatives. Simulation results demonstrate that the maneuvering motion model reconstructed using the identified derivatives exhibits satisfactory predictive performance and reasonable generalization capability. By leveraging readily available operational data, this framework potentially offers a versatile and economical methodology for maneuvering modeling of various vessel types under real-world sea conditions.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725487</guid>
    </item>
    <item>
      <title>Multi-Source Patch Feature Fusion With Neighborhood Flash Attention Transformer for Pixel-Level Vehicle and Road Recognition in Hyperspectral Image</title>
      <link>https://trid.trb.org/View/2617865</link>
      <description><![CDATA[Hyperspectral imaging can capture the spectrum of each pixel in an image across various wavelengths, providing unparalleled opportunities for precise detection, classification, and analysis of transportation infrastructure. However, traditional methods often struggle with the curse of dimensionality, inter-class variability, and the spectral-spatial trade-off inherent in hyperspectral data. To address these challenges, we introduce a novel Multi-Source Patch Feature fusion based Neighborhood Flash Attention Transformer (MSPF-NFAT) for pixel-level vehicle and road recognition in hyperspectral images (HSIs). Our methodology hinges on the insight that the integration of complementary features from multiple sources and scales can significantly enhance classification performance. Specifically, the MSPF is designed to aggregate and harmonize features extracted from both spectral and spatial dimensions, as well as from different contextual scales within the image. This fusion process ensures a richer representation of the data, capturing both the fine-grained details and the broader contextual information essential for accurate classification. Building upon this enriched feature set, we employ the NFAT, a state-of-the-art attention mechanism that focuses on capturing local spatial relationships while efficiently scaling to accommodate the high-resolution characteristics of hyperspectral data. In addition, extensive experimental results on four widely used HSIs datasets show that our newly proposed method provides superior performance compared to other state-of-the-art methods.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617865</guid>
    </item>
    <item>
      <title>Bearing and System Sensitivity Measurements on the GPS Fixed Interference Monitoring Detection System (FIMDS)</title>
      <link>https://trid.trb.org/View/2698477</link>
      <description><![CDATA[This paper documents the testing activities and their results which have taken place at the Federal Aviation Administration's William J. Hughes Technical Center (WJHTC) in order to assess the Direction Finding (DF) capabilities of the Cubic AA2030 DF Antenna Array and DF4400 Processor in the GPS L1 band and in the VHF air-to-ground Communications band. Bearing and system sensitivity data were collected for two DF Processor modes (AM & FMN) as the DF array was varied in azimuth from 0-360 degrees in 45-degree increments. L-Band testing utilized four different interferer heights (7.2 ft, 11.3 ft, 16 ft, and 21 ft) at two RF frequencies (1560 & 1590 MHz). The VHF interferer height was 7.0 ft radiating at 127.025 MHz. Utilizing a simulated interferer as the transmit source, the bearing on the DF processor was recorded. The system sensitivity was measured by reducing the interferer's RF power level sufficiently to induce a +/- 6-degree jitter in the original bearing reading. The Field Strength incident on the DF array was then measured with a calibrated antenna and spectrum analyzer. Utilizing the Antenna Factor and test cable loss, the raw spectrum analyzer reading was converted to Field Strength in units of dBuV/meter, and then to system sensitivity in uV/m. The results were compared to the vendor's specification of 20 uV/m (L-Band) and 0.8 uV/m (VHF-Band).]]></description>
      <pubDate>Tue, 26 May 2026 10:09:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698477</guid>
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