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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Prototype Development and Pilot Deployment of Ground-Based Intelligent Infrastructure for Resilient Positioning, Navigation, and Timing</title>
      <link>https://trid.trb.org/View/2696990</link>
      <description><![CDATA[Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), form the backbone of modern positioning, navigation, and timing (PNT) services. However, these space-based systems are inherently vulnerable to cyberattacks such as jamming, spoofing, as well as unintentional interference, including signal blockage, particularly in dense urban areas, indoor environments, and adversarial environments. The growing dependence on GNSS, driven by the rapid adoption of autonomous and connected systems, has exposed a single point of failure in the global PNT infrastructure. GPS signals are extremely weak at the Earth’s surface, enabling low-cost jammers or spoofers to easily disrupt receivers. In response to the 2020 Executive Order on strengthening national resilience through responsible use of PNT services signed by President Donald J. Trump, US DOT, the Department of War (DoW), and the Department of Homeland Security (DHS) have jointly emphasized the need for complementary and backup PNT capabilities that are interoperable and independently capable of sustaining precision timing and navigation for critical infrastructure during GNSS outages or cyberattacks. The research goal is to develop and demonstrate a prototype ground-based, GPS-compatible, cyber-secure PNT architecture that can generate, synchronize, and broadcast authenticatable GPS-like signals from a network of ground-based nodes, allowing existing GPS receivers to obtain valid PNT solutions without hardware modification. This goal will be achieved through the following specific research objectives: (1) Design and generate authenticable GPS-compatible terrestrial signals that replicate the L1 C/A (coarse/acquisition) waveform while embedding virtual ephemeris and adjusted clock-offset parameters to enable accurate and PNT computation from ground transmitters. (2) Develop intelligent terrestrial nodes (at least four nodes) equipped with chip-scale atomic clocks, edge computer, and transmitters to establish a distributed ground-based PNT architecture. (3) Synchronize terrestrial nodes with a master clock using precision timing distribution techniques to maintain consistent and reliable time alignment across the network. Real-Time Kinematic (RTK) positioning and differential methods will also be explored using the GEODNET hub within the UA network. (4) Demonstrate that an off-the-shelf GPS receiver can deliver a valid PNT solution using terrestrial signals through software-only modifications, thereby validating the practicality, backward compatibility, and deployment readiness of the proposed system.
]]></description>
      <pubDate>Wed, 29 Apr 2026 16:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696990</guid>
    </item>
    <item>
      <title>Towards Robust Global VINS: Innovative Semantic-Aware and Multi-Level Geometric Constraints Approach for Dynamic Feature Filtering in Urban Environments</title>
      <link>https://trid.trb.org/View/2659107</link>
      <description><![CDATA[In real-world scenarios with predominant dynamic objects, achieving robust and accurate positioning using Visual-inertial navigation systems (VINS) poses a challenge because these objects dislocate visual features, resulting in degraded feature tracking accuracy, pose deviations, and trajectory drift. Thus, static scene assumption, as proposed in some existing studies, fails in such scenarios. Meanwhile, directly removing potential dynamic objects (either stationary or moving) using deep learning methods degrades accuracy in low-texture scenes, while using image geometric constraints poses challenges when moving objects dominate the scene. To address this, we introduce a real-time global VINS that incorporates an innovative semantic-aware and multi-level geometric constraint approach for better handling of moving objects. Precisely, a feature grading module combining the power of scene cognition and spatial feature extraction is developed to categorize tracked features. This module integrates multi-geometric constraints and semantic information to effectively eliminate dynamic features and employs VI-based motion consistent constraint to eliminate missed detection of the moving objects. Then, a descriptor-matching tracker module is applied to reject mismatches and enhance feature-matching reliability. Uneven feature distribution issue resulting from intensive dynamic feature elimination is addressed by a proposed geometry feature distribution-based auto-adaptive covariance estimation Algorithm. The backend handles long-term pose estimation drift by developing an adaptive multilayer VI-GNSS optimization framework that integrates a subsystem failure detection mechanism. The system performance demonstrates efficient identification and exclusion of dynamic features while retaining static ones. Experimental validation conducted on various datasets in urban dynamic areas reflects the superiority of the proposed method in accuracy and robustness.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659107</guid>
    </item>
    <item>
      <title>MS-SLAM: Multiple Input Multiple Output Synthetic Aperture Radar Simultaneous Localization and Mapping</title>
      <link>https://trid.trb.org/View/2659149</link>
      <description><![CDATA[In this paper we propose a radar-only simultaneous localization and mapping algorithm based on multiple input multiple output synthetic aperture radar images. The algorithm distinguishes itself from others by depending only on radar data for generating synthetic aperture radar images for estimating traversed trajectory and building a visual representation. In our algorithm, ego-velocity (estimated using only radar data) is used for generating synthetic aperture radar images. The generated radar images are used for rotation estimation in the odometry step as well as for place recognition by exploiting the Fourier-Radon image registration approach. After the trajectory is optimized, we combine coherent and incoherent processing over the radar data for generating a map of the traversed area. The proposed concept was evaluated over multiple sequences comprising heterogeneous and dynamic environments. The results show high performance of the algorithm in terms of place recognition, attaining a balanced f-score in the range of 0.86–0.96. Moreover, the algorithm also achieves good results in terms of simultaneous localization and mapping. For example, it achieves an absolute trajectory error of 0.11 m for a trajectory of length 340 m, and 0.43 m for a trajectory of length 1092 m. Finally, we also include a case study in which we show the capability of the radar-only localization and mapping solution in operating under scenarios that are challenging for global navigation satellite systems.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659149</guid>
    </item>
    <item>
      <title>Omni Point Air: LiDAR and Point Cloud Map-Based Place Recognition and Pose Estimation for Advanced Air Mobility in GNSS-Denied Environments</title>
      <link>https://trid.trb.org/View/2659148</link>
      <description><![CDATA[For Advanced Air Mobility (AAM) systems operating in diverse environments, redundant localization techniques are essential to ensure continuous and safe mission execution. In this study, we propose a 3D place recognition and pose estimation method for AAM using a hemispherical light detection and ranging (LiDAR) sensor. The proposed approach includes a feature extraction method that leverages height differences in surrounding objects, a method for generating local and global descriptors from feature distances, and an efficient geometric verification and localization process through correspondence calculation. Additionally, the method incorporates a process to create a virtual descriptor database using a point cloud map, enabling robust localization in unvisited areas. All procedures are handcrafted, and the performance of the proposed method is validated through comparison with state-of-the-art methods using datasets generated in a simulator. The proposed method achieved over 99.16% average precision (AP) and a 99.99% F1 score in loop closure detection. In pose estimation, it achieved a root mean square error (RMSE) of 0.836 meters or less for position and 0.195 degrees or less for heading. Furthermore, a time analysis on both a general PC and an embedded device confirmed the real-time capability of the proposed method, with an average pose estimation time of 21.70 milliseconds on the embedded device, demonstrating its feasibility for real-time localization in low-power environments.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659148</guid>
    </item>
    <item>
      <title>FCO based localization using vehicle environment data</title>
      <link>https://trid.trb.org/View/2682764</link>
      <description><![CDATA[To facilitate advanced driver assistance systems and autonomous driving, it is important to know the current position of a vehicle relative to the street network the vehicle is driving on. The default method for determining the current position of a vehicle is through the use of Global Navigation Satellite Systems. Especially in dense urban settings, satellite signals can be damped or reflected by buildings, leading to degradation in the quality of the position estimation. In this paper, we present a method to increase the accuracy of satellite positioning by utilizing vehicle sensor data. This can be accomplished by matching the position and orientation of known vehicles relative to the observer onto a digital map of the street lanes. By determining the most likely position of the constellation of all known vehicles on the map, the current position of the observer can be estimated. To test our method, we simulated floating car observers (FCO) in traffic with different traffic volumes and lane geometries. The results of our method were compared to a simple satellite positioning map matching algorithm.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682764</guid>
    </item>
    <item>
      <title>Secure UAV-AV Communications Architecture in Non-Terrestrial Networks: A Review</title>
      <link>https://trid.trb.org/View/2686840</link>
      <description><![CDATA[Non-terrestrial networks (NTNs), integrating satellites and unmanned aerial vehicles (UAVs), have become integral to next-generation communication systems, particularly in supporting mission-critical autonomous vehicle (AV) applications. UAV-assisted NTNs enhance AV operations by providing extended coverage, real-time responsiveness, and resilience in dynamic, infrastructure-limited environments. However, integrating UAVs, AVs, and NTNs introduces unique security challenges due to their heterogeneous, mobile and distributed nature. To the best of our knowledge, this paper provides the first comprehensive review of secure UAV-AV communication architectures within NTNs. First, it introduces a systematic taxonomy of security requirements and threat models specific to UAV-NTN-enabled AV ecosystems. This distinguishes our work from prior UAV-AV or NTN-only studies. Second, it consolidates and critically analyzes existing countermeasures including cryptographic techniques, blockchain mechanisms, and intrusion detection systems to assess their effectiveness, scalability, and limitations in resource-constrained, latency-sensitive contexts. Third, we propose a novel classification framework for security solutions that bridges architectural and operational perspectives. Furthermore, this work identifies unexplored challenges in resource management, network interoperability, and adaptive security in heterogeneous NTN infrastructures. It outlines future research directions involving quantum-safe cryptography, AI-enabled anomaly detection, and federated learning for privacy-preserving threat response. The finding and critical analysis provided in this paper serves as a foundational resource that guides the design of secure, scalable, and resilient UAV-NTN-AV communication architectures.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686840</guid>
    </item>
    <item>
      <title>Trial of Training Method for Seafarers on GNSS Interference</title>
      <link>https://trid.trb.org/View/2669600</link>
      <description><![CDATA[GNSS relies on weak radio signals from distant satellites in space and has been noted to be vulnerable to positioning failures caused by radio jamming and the output of erroneous position information due to spoofed signals. GNSS jamming and spoofing can render a vessel’s positioning impossible or display incorrect locations, potentially causing confusion among seafarers. Therefore, effective education and training on countermeasures are important for vessel safety. However, effective methods for providing demonstration-based education on GNSS jamming and spoofing, or experiential training using ship-handling simulators, have not yet been established. This study developed and validated a training method aimed at enhancing the awareness and understanding of seafarers operating vessels and maritime students regarding the risks of GNSS displaying incorrect positions due to intentional interference signals or spoofing signals, and the countermeasures against them. The results show that instances of maneuvering based on erroneous positions were observed, and positive feedback regarding the training was received from participants.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:23:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669600</guid>
    </item>
    <item>
      <title>A Train Positioning Method Based on Satellite Navigation System and Electronic Map</title>
      <link>https://trid.trb.org/View/2113861</link>
      <description><![CDATA[Due to the characteristic that the measurement error diverges with the distance increases, the train positioning method based on the odometriche sensor often needs to arrange more physical balise along the railway to achieve the convergence of the distance measurement error. The train positioning method based on satellite navigation system and electronic map can ensure the real-time convergence of distance measurement error, and can greatly reduce the laying of physical balise. While improving the real-time accuracy of train positioning, it reduces the labor of maintenance personnel in harsh environments. However, in the train positioning application of satellite navigation system, since only a straight-line distance can be calculated between two points, there is a large error in calculating the train position at the curved track. To solve this problem, this paper proposes a train positioning method based on satellite navigation system and electronic map. This method can calculate the precise position of the train at the curved track based on the information of the curved track, thereby solving the precise positioning of the train at the curved track.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113861</guid>
    </item>
    <item>
      <title>Global Navigation Satellite System-Aided Post-Simultaneous Localization and Mapping Correction of Lidar Maps: Modular Pipeline for Enhanced Global Accuracy</title>
      <link>https://trid.trb.org/View/2691030</link>
      <description><![CDATA[Lidar-based simultaneous localization and mapping (SLAM) enables the generation of detailed 3D maps for such applications as autonomous navigation and infrastructure monitoring. However, SLAM systems are prone to drift accumulation, especially in the absence of a tightly integrated global navigation satellite system (GNSS) or inertial measurement unit, leading to degraded global accuracy. This paper presents a modular, post-processing correction pipeline that leverages high-accuracy GNSS real-time kinematic (RTK) data to correct lidar SLAM outputs in high-drift scenarios. The pipeline operates in three stages: (1) segment-based SLAM trajectory correction through alignment with time-synchronized GNSS RTK points using a singular value decomposition and Iterative Closest Point (SVD-ICP) method; (2) propagation of these corrections to the aggregated point cloud through timestamp-aligned delta application, generating a non-rigid intermediate reference map; and (3) conditional map refinement using either the SVD-ICP method for globally rigid alignment in low-drift scenarios or a Random Sample Consensus (RANSAC)-progressive Iterative Closest Point (ICP) method for robust registration in cases with significant residual drift, outliers, or local inconsistencies. The final output is a globally corrected high-fidelity point cloud in LAS file format. Experimental results demonstrate sub-meter global accuracy and strong visual consistency with satellite basemaps and GNSS control points, confirming the pipeline’s effectiveness for post-SLAM correction in environments with limited sensor integration.]]></description>
      <pubDate>Mon, 13 Apr 2026 08:41:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691030</guid>
    </item>
    <item>
      <title>Physical Parameters Estimation Using Roadside Monocular Vision</title>
      <link>https://trid.trb.org/View/2686163</link>
      <description><![CDATA[Roadside sensing is an important part of intelligent traffic management systems (ITMSs) for collecting and processing information. In order to better assess and maintain the stability and safety of objects in traffic scenes, all types of basic information are required. This paper proposes a monocular vision-based object parameter measurement and geolocation method to address the problems of high cost and limited information dimension of traditional roadside sensors. Object detection and geometric transformation mapping are combined to achieve efficient estimation of key physical parameters with input of monocular images, and global navigation satellite system (GNSS) information is further incorporated to obtain geolocation of the target. In the method, after the key target is recognized by the neural network-based object detection algorithm, the pixel-level 2D image information is mapped to a series of 3D spaces based on the construction of a geometric model, which leads to further computation of various physical parameters, realizing multi-parameter estimation under one method. The method overcomes the dependence on fixed environments or known references and is highly applicable to non-cooperative scenes. The effectiveness of the method is shown via the experiments in multiple real scenes.]]></description>
      <pubDate>Fri, 03 Apr 2026 12:13:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686163</guid>
    </item>
    <item>
      <title>Passive data for active policy: Evidence-based insights from GNSS and CDR applications in transport planning</title>
      <link>https://trid.trb.org/View/2681351</link>
      <description><![CDATA[This paper presents inherent characteristics of data collection methods and provides a systematic literature review, guided by the PRISMA methodology, to evaluate the potential of Global Navigation Satellite System (GNSS) and Call Detail Record (CDR) data as tools for supporting transportation policy, with a focus on their capacity to complement or replace traditional travel surveys. The topic is of growing importance due to the increasing availability of passively collected mobility data and the limitations of conventional survey methods, such as high costs, infrequent updates, and respondent fatigue. The review highlights the added value of GNSS and CDR data in providing real-time, large-scale, and high-resolution insights into travel behaviour, capabilities not attainable through traditional means. While these data sources currently fall short in capturing subjective and socio-demographic information intrinsic to travel diaries, the paper underscores their utility in dynamic travel analysis, demand modelling, and policy evaluation when integrated into hybrid data collection frameworks. The findings emphasise that GNSS and CDR data are not outright replacements, but valuable complements to traditional surveys, with significant potential to enhance evidence-based transport planning and decision-making.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:36:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681351</guid>
    </item>
    <item>
      <title>Outlier-Resistant Cooperative Positioning Method Using Robust Factor Graph Optimization</title>
      <link>https://trid.trb.org/View/2610692</link>
      <description><![CDATA[Cooperative positioning (CP) is able to improve the vehicular positioning performance by introducing the data of multiple vehicles into the position estimation. However, CP methods are vulnerable to measurement outliers in dense urban areas. The existing outlier-resistant CP methods are easy to trap in local optimum and may wrongly reject the outliers when the ratio of outliers to inliers is relatively high. To deal with this problem, a factor graph optimization (FGO) based CP method using Graduated Non-Convexity (GNC) Welsch cost is proposed in this paper. The state-of-art FGO algorithm is used to integrate multi-node and multi-epoch measurements including the Global Navigation Satellite Systems (GNSS) pseudoranges, inter-epoch baselines estimated by GNSS time-differenced carrier phase (TDCP), and inter-vehicle ranging measurements in a centralized framework. The least-square cost in traditional FGO is replaced with the GNC-based Welsch cost so as to enhance the robustness of the proposed method to any kind of outliers in our CP system. The use of GNC can reduce the risk of local optimum by gradually increasing the non-convexity of the Welsch cost. The proposed method can de-weight the outliers correctly even if a large number of outliers exist. The experimental results show the superiority of the proposed method over the existing CP methods in resisting multiple outliers.]]></description>
      <pubDate>Fri, 27 Mar 2026 17:03:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610692</guid>
    </item>
    <item>
      <title>Feature Map Aided Robust High Precision GNSS Positioning in Harsh Urban Environments</title>
      <link>https://trid.trb.org/View/2610684</link>
      <description><![CDATA[In this contribution, we propose the GNSS Feature Map-aided robust extended Kalman filter, which can provide centimeter-to-decimeter-level GNSS RTK position accuracy in urban environments without the need of additional sensors, city model information or computational intensive ray tracing methods. In this approach, the information on the predicted observation error magnitudes from the generated GNSS Feature Map is combined with the concept of robust estimation. The RTK positioning performance comparison for a dynamic experiment under harsh signal propagation conditions reveals that GNSS Feature Map-aided weighting using the Geman-McClure loss function shows the best overall performance. The RMS of the horizontal position error is improved by 17% compared to C/N0 weighting, while 3DMA NLOS exclusion even degrades the solution. Furthermore, the combination of Feature Map information with the robust Geman-McClure loss function is effectively enhancing the float solution and reducing the number of falsely fixed ambiguities.]]></description>
      <pubDate>Fri, 27 Mar 2026 17:03:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610684</guid>
    </item>
    <item>
      <title>Feasibility of GNSS independent harbour geo-localization via LiDAR SLAM and prior maps</title>
      <link>https://trid.trb.org/View/2685331</link>
      <description><![CDATA[This paper presents a feasibility study of a LiDAR SLAM based prior map fitting pipeline that yields map-referenced position estimates in known harbour environments, independent of GNSS. To address the challenge of precise positioning in unstructured and dynamic environments, a LiDAR sensor provides accurate measurements of the surroundings, which are fitted to a prior map using rotational initial alignment and G-ICP detail registration. The initial alignment process improves the accuracy of subsequent registration by leveraging corner features of the environment to address the limitations associated with large transformations in ICP registration. The resulting position estimates enable redundant positioning estimated by an Unscented Kalman Filter sensor fusion algorithm together with GNSS positioning. Results demonstrate map-referenced positioning accuracy of 3-6 m and end-to-end system latencies of approximately 1.8 s, indicating the method’s practical feasibility for coastal and port operations and its potential role in improving the navigation safety and autonomy of maritime platforms. While likely not generalizable for all land structures, we argue the potential usefulness of the system for redundant positioning in pre-vetted, well structured harbour environments.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:14:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685331</guid>
    </item>
    <item>
      <title>A Low-Profile Circularly Polarized Spring-Shaped Quadrifilar Helical Antenna for Vehicle Satellite Navigation Systems</title>
      <link>https://trid.trb.org/View/2674317</link>
      <description><![CDATA[This work proposes a novel low-profile circularly polarized spring-shaped quadrifilar helical antenna (QHA) integrated on the car roof as a satellite navigation antenna to meet the practical needs of compact vehicles. The designed antenna consists of four helical radiating arms and a feed network. The antenna unit achieves low-profile characteristics mainly by folding and bending the radiating arms. Compared with the traditional QHA, the profile of the proposed antenna is reduced by 84.4%, with a height of only 13.4 mm (0.07λ). The designed feed network provides the QHA with equal amplitude and sequential 90° phase difference in the desired operating frequency band. The antenna prototype is realized by using 3D dielectric printing and post-metallization technology. The measured results show that the proposed QHA works in 1.558-1.644 GHz, fully covering the B1 band of the BeiDou satellite navigation. Typically, at 1.575 GHz, the antenna's RHCP 3-dB beamwidth is 122°, with a 3-dB axial ratio (AR) beamwidth of 144°, providing a wide signal coverage range in the upper hemisphere. The proposed spring-shaped QHA exhibits good circularly polarized radiation performance and has the advantages of low cost and low profile, which is a promising candidate to ease the antenna integration into a compact satellite navigation platform of vehicles.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674317</guid>
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