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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
    <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 New Map/Odometer Tunnel Positioning Method Using Random Forest-Derived Heading Estimation</title>
      <link>https://trid.trb.org/View/2732122</link>
      <description><![CDATA[In global navigation satellite system (GNSS)-denied scenarios, odometer/inertial navigation system (INS) integration for train positioning tends to diverge due to accumulated attitude errors caused by uncorrected sensor drift. To provide a heading without sensor drift and assist odometer dead reckoning, a map-based heading/odometer positioning method and its optimism are proposed. In the map-based heading/odometer positioning framework, the heading is obtained by projecting and searching on the digital track map and then computing it using the corresponding interval. With the heading and odometer velocity, the position is computed from the train derived distance. Theoretically, shorter intervals provide more accurate headings and positions. However, due to onboard storage limitations, the stored interval information must be reduced, which means longer interval lengths are more acceptable to fulfill the industrial requirement. Thus, in the optimism for this method, a random forest (RF)-based heading estimation strategy is introduced to obtain accurate headings from long-interval maps. To be detailed, it utilizes both long- and short-interval digital track maps during offline training. By redividing the long-interval map to establish a correspondence with the short-interval map that provides accurate headings, a mapping from long-interval map data to accurate headings can be constructed, and the RF model is used to learn this mapping. During online prediction, the RF model can estimate headings from the onboard long-interval map for positioning. Experiments and dataset collection were conducted on the Qinghai-Tibet Railway between Sanglie and Dangxiong stations. A digital track map was generated from real-time kinematic (RTK) positioning-derived trajectory data and resampled at 3 m and 100 m intervals to form short-interval and long-interval map databases, respectively. Experimental results show that the RF-based strategy achieves heading accuracy comparable to that obtained using short-interval maps and improves map-based heading/odometer positioning performance in GNSS-denied environments when only long-interval maps are available.]]></description>
      <pubDate>Wed, 19 Aug 2026 09:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732122</guid>
    </item>
    <item>
      <title>Precise Train Positioning With Unscented Kalman Filter and Low-Cost Sensors</title>
      <link>https://trid.trb.org/View/2685745</link>
      <description><![CDATA[This contribution is embedded into the challenge of track fault localization with low-cost hardware. For precise localization on the track, with an accuracy of a few decimeters for separating overlapping errors, a high resolution trajectory is needed and therefore sensor fusion is used. The commonly used combination of sensors consists of Global Navigation Satellite Systems and Inertial Measurement Units. The steps of the Kalman filter for sensor fusion are covered and afterwards the Unscented transform is described. This transform is applied to the prediction step of the Kalman filter. The implemented filters are extended by an adaptive stochastic model that applies to the observations used in the update steps. The Error-state Kalman filter and the Unscented Kalman filter are compared with and without the adaptive stochastic model with respect to their resulting root-mean-square (RMS) values. It is observed that the applied adaptive stochastic model improves performance for both filters by a small margin of 2 to 3 cm down to an RMS of 0.26 m. Meanwhile the roll angle estimation achieves deviations down to 0.1°. Both implemented filters achieve comparable results.]]></description>
      <pubDate>Mon, 17 Aug 2026 08:27:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685745</guid>
    </item>
    <item>
      <title>Frequency Domain Transformation-Aided Lidar-Inertial Pose Estimation for Autonomous Vehicles Using Implicit Neural Map</title>
      <link>https://trid.trb.org/View/2731753</link>
      <description><![CDATA[Point cloud map-based pose estimation constitutes the cornerstone of autonomous vehicle navigation systems, yet existing techniques suffer from accuracy degradation that depends on resolution. Excessively dense point clouds impose prohibitively high computational loads, whereas overly sparse representations compromise feature distinctiveness, thereby constraining the reliability of real-time positioning. Implicit neural field map emerges as a promising alternative, offering lightweight representation and high-resolution reconstruction. However, current implicit map-based localization methods struggle with initialization, state estimation accuracy, and real-time performance. To address these challenges, this work proposes a novel LiDAR-inertial localization system for vehicles that leverages implicit neural maps augmented by frequency-domain descriptors. A prior map is firstly constructed using neural point models and corresponding descriptors. During the localization process, point cloud frames are converted into Bird's Eye View (BEV) images, from which descriptors are extracted using frequency domain transformation. These descriptors are then used to search the map database and obtain an initial pose estimate. The scan-to-map registration is performed by aligning the point cloud to the implicit neural model. And the short-term high-precision characteristics of the inertial navigation system are utilized to provide state prediction, improving real-time performance. Finally, the state estimation is refined and output through factor graph optimization. Extensive experiments conducted on both public and custom datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy and efficiency.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731753</guid>
    </item>
    <item>
      <title>Integrated Aisgp Model for Real-Time Autonomous Vehicle State Estimation and Path Tracking Control</title>
      <link>https://trid.trb.org/View/2731772</link>
      <description><![CDATA[Autonomous vehicles often face issues such as inaccurate state estimation and insufficient path tracking precision caused by model biases and sensor noise in complex dynamic scenarios. Therefore, this paper proposes an autonomous vehicle state estimation and path tracking control model based on the adaptive incremental sparse spectrum gaussian process (AISGP), which integrates model predictive contour control (MPCC) and moving horizon estimation (MHE). By constructing a hierarchical AISGP model and combining a residual prediction model, the model can efficiently capture unmodeled vehicle dynamic characteristics and noise interference. The AISGP-MHE module optimizes the state estimation of lateral velocity and yaw rate, and the AISGP-MPCC module optimizes the path tracking control strategy by minimizing the contour and lag error. Evaluations on CarSim demonstrate that the proposed method improves vehicle tracking accuracy by 17% compared with traditional schemes, and the inference speed of AISGP is 8.4 times faster than that of Gaussian Process (GP).]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731772</guid>
    </item>
    <item>
      <title>A Dual-LiDAR ship-shore collaborative pose estimation method for USV berthing and unberthing</title>
      <link>https://trid.trb.org/View/2737846</link>
      <description><![CDATA[This paper presents a ship-shore LiDAR collaborative pose estimation method for USVs. The proposed approach enables joint environmental perception by integrating data from shipborne and shore-based LiDAR sensors through point-to-point communication, thereby providing rapid and accurate pose information for the USV. The method comprises three core modules: place recognition, ship-shore relative pose estimation, and factor graph optimisation. Experiments conducted in an open robotic competition simulation environment demonstrate the effectiveness of the proposed system. The ship-shore collaborative pose estimation framework corrects accumulated drift and improves the global consistency of the shipborne trajectory relative to the fixed shore reference. Experimental results indicate that the proposed method attains an absolute trajectory error of 0.42 m and a real-time processing rate of 14.2 frames per second (FPS) in berthing and unberthing scenarios characterised by occlusions and blind zones. These results indicate that the method can support safe and autonomous USV berthing operations in complex port environments, providing critical technical support for the advancement of next-generation intelligent vessels.]]></description>
      <pubDate>Wed, 05 Aug 2026 09:12:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737846</guid>
    </item>
    <item>
      <title>Integrated Sensing, Communication, and Positioning in Cellular Vehicular Networks</title>
      <link>https://trid.trb.org/View/2731560</link>
      <description><![CDATA[In this correspondence, a novel integrated sensing and communication (ISAC) framework is proposed to accomplish data communication, vehicle positioning, and environment sensing simultaneously in a cellular vehicular network. By incorporating the vehicle positioning problem with the existing computational-imaging-based ISAC models, we formulate a special integrated sensing, communication, and positioning problem in which the unknowns are highly coupled. To mitigate the rank deficiency and make it solvable, we discretize the region of interest (ROI) into sensing and positioning pixels respectively, and exploit both the line-of-sight and non-line-of-sight propagation of the vehicles' uplink access signals. The resultant problem is shown to be a polynomial bilinear compressed sensing (CS) reconstruction problem, which is then solved by the alternating optimization (AO) algorithm to iteratively achieve symbol detection, vehicle positioning and environment sensing. Performance analysis and numerical results demonstrate the effectiveness of the proposed method.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731560</guid>
    </item>
    <item>
      <title>Robust High-Accuracy Cellular Base Station Positioning for User Equipment Based on Multi-Signal Fusion Passive Imaging</title>
      <link>https://trid.trb.org/View/2730980</link>
      <description><![CDATA[Emerging applications such as smart cities and smart transportation require sub-decimeter accuracy in user equipment (UE) location services to enhance safety and reliability. Therefore, robust high-accuracy positioning technology is crucial for these applications. However, existing two-step positioning methods rely on numerical solutions, which are susceptible to non-ideal factors, resulting in non-convergent solution errors of several meters, rendering accurate multi-target positioning difficult. We propose a cellular base station (BS) positioning method for UE based on a multi-signal fusion passive imaging algorithm. The proposed approach provides highly robust location services with sub-decimeter-level accuracy and enables simultaneous multi-target positioning. The algorithm decouples the phase difference from UE to distributed BSs into spatial frequency and reconstructs the spatial spectrum by fusing spatially sampled frequency points from the UE uplink multi-signal. The UE is imaged as a point target to obtain its spatial distribution information. With the fusion of multi-signal received by distributed BSs, the integral side-lobe ratio and minimum discernible distance are improved, and the non-line-of-sight interference is suppressed, thus enhancing the robustness of positioning accuracy. Compared with the existing two-step methods through simulation, the proposed method demonstrates more robust positioning accuracy under the same non-ideal conditions. Field experiments demonstrate a maximum positioning error of approximately 5 cm, with decimeter-level accuracy even when the signal-to-noise ratio deteriorates to -15 dB. Additionally, multi-target positioning can be achieved directly without data association limitations.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730980</guid>
    </item>
    <item>
      <title>Polarimetric Vehicle Positioning System</title>
      <link>https://trid.trb.org/View/2579739</link>
      <description><![CDATA[The purpose of this work is to improve the safety of motor vehicles by developing a polarimetric positioning system. The scientific novelty lies in the fact that the issue of improving traffic safety is first achieved by using a polarimetric positioning system. The polarimetric model for determining the linear-angular parameters of the relative position of vehicles is analyzed. The use of the polarimetric measurement method for determining the linear-angular parameters of the relative position of vehicles is possible due to the binding of the azimuth of the polarization plane of a linearly polarized beam to the direction of its emission and the conduct of radiation in several channels that differ in the function of the dependence of the azimuth of the polarization plane of a linearly polarized beam on the direction of emission. The developed polarimetric vehicle positioning system allows simultaneous determination of positional and orientation parameters of the vehicles’ relative attitude. This approach is aimed at reducing the number of road accidents and can be used in the study of dynamic loads acting between the car wheel and the road surface. These studies are especially relevant in terms of overcoming bridge crossings with pavement defects by trucks, where dynamic loads can increase significantly. This should be taken into account during the design, construction, operation, and rehabilitation of road bridges in wartime and postwar periods.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579739</guid>
    </item>
    <item>
      <title>Feature-Driven Wavelet Analysis for GNSS Jammer Type Recognition</title>
      <link>https://trid.trb.org/View/2713891</link>
      <description><![CDATA[GNSS receivers in smart mobility ecosystems are increasingly exposed to intentional and unintentional jamming. We present a fast, interpretable pipeline that categorizes common jammer families (continuous wave, linear chirp, pulsed, wide-band noise, and multi-tone) using short I/Q windows. The method applies a continuous wavelet transform (CWT) with a Morse wavelet to obtain a scalogram, from which we derive an interpretable feature set: ridge slope (Hz/s) and goodness-of-ft R2 along the dominant time–frequency ridge, time-domain energy-envelope statistics (duty cycle), scale/PSD entropies as spread measures, and a scale-bandwidth proxy. A lightweight rule-based classifier—expressed in a few thresholded relations—maps features to jammer types, enabling transparent tuning and operator diagnosis via compact plots (PSD, scalogram, time envelope). On synthetic I/Q sampled at 5 MHz using ∼54 ms windows, the classifier was evaluated on a balanced dataset of 2500 windows (500 realizations per jammer type). Coarse single-scenario thresholds yielded approximately 56% overall accuracy, while systematic threshold refinement on the full dataset increased accuracy to approximately 94%, without altering feature definitions or decision logic. The approach is deployable for on-device monitoring, provides explainable decisions for operators, and forms a compact feature substrate for future ML back-ends when real RF datasets become available.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713891</guid>
    </item>
    <item>
      <title>A Comprehensive Approach of Fingerprint Localization with Irregular Grid Division and Multi-Level Prediction</title>
      <link>https://trid.trb.org/View/2730724</link>
      <description><![CDATA[The development of Intelligent Transportation Systems (ITS) has created new opportunities for urban mobility. In areas where satellite signals are denied, Global Navigation Satellite Systems (GNSS) often fail to meet the localization accuracy requirements of ITS. To address this challenge, this paper presents a high-precision fingerprint localization technique based on cellular networks as an effective complement to GNSS. First, an innovative irregular grid division method is introduced, which adaptively generates variable-resolution grids according to the complex layout of urban streets, thereby reducing the imbalance in fingerprint quantities across grids. Second, a multi-level prediction model is designed to progressively narrow the fingerprint prediction range through collaboration among multiple models, significantly improving localization accuracy. Finally, a dual-stage error correction algorithm is incorporated into the fingerprint localization process, enabling real-time detection and correction of abnormal localization results. Experimental results in urban environments demonstrate that the proposed method achieves a median localization error of 4.74 m and an average localization error of 10.15 m. Compared with state-of-the-art outdoor fingerprint localization techniques, the proposed approach exhibits superior performance.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730724</guid>
    </item>
    <item>
      <title>Integrated Resonant Beam and Vio for Aav Position and Attitude Sensing in Air-Ground Vehicular Network</title>
      <link>https://trid.trb.org/View/2730708</link>
      <description><![CDATA[In air-ground vehicular networks, autonomous aerial vehicles (AAVs) often serve as airborne communication relays or sensing nodes, where accurate position and attitude estimation is critical for ensuring stable beam alignment, reliable link establishment, and precise mission execution. However, in GNSS-denied or infrastructure-free environments, conventional visual-inertial odometry (VIO) systems suffer from cumulative drift over time and distance. To address this challenge, we propose a blueresonant beam sensing (RBS)-aided pose estimation method that leverages spatially separated laser cavities to extract relative positions and attitudes between AAVs. Specifically, we estimate the angle of arrival (AoA) of beam spots through resonant beam (RB) mapping and perform time-of-flight (ToF) ranging to compute relative poses, where pitch and yaw are directly obtained from AoA, and roll is estimated via a nonlinear optimization decoupled from VIO attitudes. Furthermore, we integrate RBS and VIO using a factor graph optimization framework, where sensor fusion is adaptively weighted based on measurement uncertainty and error models. Simulation results demonstrate that our method significantly improves relative attitude estimation in yaw and pitch dimensions, and enhances overall swarm localization accuracy by 22% to 39% with different swarm scales compared to VIO-only methods. Additionally, it outperforms existing VIO-UWB-Visual fusion approaches, achieving an accuracy gain of nearly 10%.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730708</guid>
    </item>
    <item>
      <title>Lidar Inertial Odometry for Vehicle Positioning Based on Multimodal Attention Fusion Network</title>
      <link>https://trid.trb.org/View/2727853</link>
      <description><![CDATA[Multi-sensor information fusion is one of the effective methods to improve the accuracy of odometry estimation, which can help reduce the cumulative error. In recent years, the deep learning method has been extensively applied in this field due to its ability to automatically extract the features related to the vehicle's motion from sensor data. However, how to effectively fuse IMU data and LiDAR data using supervised learning remains a challenge. In this paper, a LiDAR inertial odometry (LIO) estimation method based on multimodal attention fusion network (MAFNet) is proposed for vehicle positioning. In the MAFNet, two feature extraction modules, i.e., spatio-temporal feature extraction module (STFE) and multi-motion feature extraction module (MMFE) are constructed. The motion features of the vehicle are indirectly extracted by STFE through the collection of change information in point clouds at different moments. The complex correlations and time dependencies of rotations and translations in different directions are innovatively learned by the MMFE, improving the model's understanding of the complex pose changes of the vehicle. Feature association guided learning mechanism (FAGLM) is then designed to guide the fusion of heterogeneous modal features by learning the correlation among multimodal features and reducing the redundant information in the features. Finally, the proposed MAFNet is applied to the Kitti odometry dataset and ablation experiments are designed to verify the effectiveness of the proposed method. The experimental results show that MAFNet outperforms the existing methods and achieves excellent estimation performance.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727853</guid>
    </item>
    <item>
      <title>Lightweight and Computationally Efficient Direct Localization Based on Subspace Reconstruction</title>
      <link>https://trid.trb.org/View/2727829</link>
      <description><![CDATA[Direct position determination (DPD) outperforms traditional two-step methods in accuracy. However, its practical application is hindered by two primary challenges: (1) the necessity to transmit raw data to the fusion center, which imposes significant demands on bandwidth and hardware resources, and (2) the absence of a closed-form solution for DPD, necessitating exhaustive search techniques and resulting in high computational complexity. To overcome these challenges, we introduce a subspace reconstruction-based DPD approach designed for an uncrewed aerial vehicle (UAV) with a mounted array. This method requires a data transmission amount equivalent to that of two-step localization, specifically angle-of-arrival (AOA) localization while achieving performance comparable to the existing DPD method based on subspace data fusion (SDF). Furthermore, we introduce a numerically convergent solution based on majorization-minimization (MM) that guarantees convergence to a stationary point, thereby significantly reducing computational complexity and grid quantization errors (GQE) by eliminating the exhaustive search process. We validate the proposed method through computer simulations and real-world measurements conducted with a rotary-wing UAV, demonstrating its effectiveness and advantages.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727829</guid>
    </item>
    <item>
      <title>TCDRL: Two-Stage Cooperative Dimensionality-Reduced Localization Framework for UAVs in GNSS-Poor Environments</title>
      <link>https://trid.trb.org/View/2727827</link>
      <description><![CDATA[In collaborative scenarios such as swarm warfare and post-disaster rescue, achieving high-precision localization and maintaining robust communication among uncrewed aerial vehicle (UAV) swarms is crucial. However, in environments lacking Global Navigation Satellite System (GNSS) signals, traditional positioning methods suffer from significant degradation due to signal obstruction and electromagnetic interference. This paper proposes TCDRL, a two-stage cooperative localization framework designed to balance communication overhead and positioning accuracy for UAV swarms operating in GNSS-poor environments. In the first stage, a novel dynamic game-theoretic clustering algorithm is proposed to partition the swarm into multiple clusters, effectively reducing communication and computational burdens. In the second stage, an enhanced Weighted Spectral Multidimensional Scaling (W-SMDS) algorithm is devised to perform intra-cluster dimensionality reduction. Concurrently, Extended Kalman Filtering (EKF) integrates onboard sensor data with Ultra-Wideband (UWB) measurements to enable high-precision relative localization through multi-source data fusion. To enhance robustness in the transformation from relative to absolute coordinates, a Huber loss-based optimization is introduced to adaptively downweight the influence of outliers. Simulation results demonstrate that the proposed framework reduces computational complexity and enhances localization accuracy compared to existing methods. The approach also maintains robust performance under varying communication rates and measurement noise levels, offering an effective solution for UAV swarm localization in GNSS-poor scenarios.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727827</guid>
    </item>
    <item>
      <title>Cooperative Vehicle Localization in VANETs Subject to Sensing and Transmission Failures</title>
      <link>https://trid.trb.org/View/2727780</link>
      <description><![CDATA[This paper investigates a cascaded robust Kalman filter framework for cooperative localization of vehicles subject to unreliable information from onboard sensors and vehicular ad hoc networks (VANETs). Specially, an adaptive-threshold outlier-robust local filter is first introduced that using a machine learning-based dynamic parameter selection approach. Then, a three-stage weighting function, and K-means clustering to classify residuals into reliable and outlier sets, with the maximum residual from the reliable set used to adaptively determine the threshold. Finally, by employing a dual-statistics joint test to extract two-dimensional statistical features from residual matrices, an unreliable information isolation algorithm with low computational complexity is developed. Experimental results demonstrate that the method outperforms the state-of-the-art results with ensured accuracy and robustness for cooperative vehicle localization in urban environments.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727780</guid>
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