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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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      <title>Study of Parameters Influencing the Accuracy of the SDF Method Localization</title>
      <link>https://trid.trb.org/View/2624155</link>
      <description><![CDATA[Modern military operations increasingly use unmanned aerial vehicles (UAVs) not only for observation and reconnaissance, but also for active localization of radio emission sources. One of the methods used for this purpose is the signal Doppler frequency method (SDF), based on the analysis of the frequency of the signal received by the moving sensor. The paper presents a theoretical analysis and simulation studies aimed at determining the effect of selected parameters on the accuracy of emitter localization using the SDF method. In particular, the factors such as data acquisition time, accuracy of Doppler frequency estimation, carrier frequency and the velocity of the moving sensor were considered. The aim of the work is to indicate which of these parameters are crucial for the quality of localization. We formulate conclusions that can support the development of the resistant to interference and more precise localization systems based on the SDF method. The presented approach can be used both in real armed conflicts and in work on autonomous electronic reconnaissance systems.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624155</guid>
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    <item>
      <title>Evaluation of the field survey procedure and three-dimensional modelling of a building using drone-captured images</title>
      <link>https://trid.trb.org/View/2569584</link>
      <description><![CDATA[]]></description>
      <pubDate>Thu, 26 Jun 2025 13:31:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569584</guid>
    </item>
    <item>
      <title>Leveraging bird eye view video and multimodal large language models for real-time intersection control and reasoning</title>
      <link>https://trid.trb.org/View/2563052</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 10 Jun 2025 14:47:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563052</guid>
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    <item>
      <title>Superpoint Transformer–Based Bridge Component Recognition Using UAV-Mounted LiDAR and Synthetic Point Cloud Generation</title>
      <link>https://trid.trb.org/View/2543204</link>
      <description><![CDATA[Given the rapid rise in aging bridges, accurate and efficient inspection and maintenance have become increasingly critical. Point cloud data, rich in geometric details, plays a vital role in this process. Identifying semantic information for each bridge component is essential for effective analysis. This paper presents a novel Superpoint Transformer–Based method for automatically identifying bridge components from unmanned aerial vehicle (UAV)-mounted light detection and ranging (LiDAR) and simultaneous localization and mapping (SLAM)-acquired point cloud data. The method consists of two stages: acquisition and registration of point cloud data, followed by three-dimensional (3D) semantic segmentation using the Superpoint Transformer. Additionally, a new strategy simulates a virtual UAV equipped with LiDAR to generate synthetic data, reducing the domain gap between real and synthetic data. This simulation-based approach outperformed traditional sampling methods, significantly enhancing model performance. By fine-tuning the Superpoint Transformer for bridge component recognition, the proposed method achieved a mean intersection over union (mIoU) of 86.125%. This approach offers an effective solution for bridge component recognition, facilitating efficient inspection and maintenance of aging bridge infrastructure.]]></description>
      <pubDate>Sat, 31 May 2025 15:16:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543204</guid>
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    <item>
      <title>Integrating autonomous aircraft into the healthcare supply chain for remote communities in the Northern Territory</title>
      <link>https://trid.trb.org/View/2509087</link>
      <description><![CDATA[The project objective was to investigate the technological, budgetary, and regulatory obstacles associated with flying Remotely Piloted Aircraft System (RPAS) Beyondvisual-line-of-sight (BVLOS) in the Northern Territory and using them to transport medical items between remote communities.  This initiative marked the first application of RPAS for healthcare delivery in Australia, the inaugural trial of freight delivery via RPAS outside of an urban centre in Australia, and the first attempt to operate RPAS in and around Aboriginal land and communities. There was no blueprint or guidelines from which the research team could work in developing this project. This report comprises our learnings and findings from extensive consultations with Aboriginal Corporations, Traditional Owners, Healthcare professionals, Technologists, RPAS Manufacturers, and Australia’s Civil Aviation Safety Authority (CASA), as well as the results of field trials using several different RPAS types. This Final Report offers a high-level summary of significant project accomplishments, challenges encountered, and potential solutions. It outlines future directions to enable a safe and reliable RPAS integration into health care delivery for remote communities in the Northern Territory. The critical take-home finding was that the technology and protocols used for RPAS healthcare delivery operations in developing countries could not be readily integrated into the healthcare delivery supply chains for remote communities in the Northern Territory due to the broad distances the RPAS needed to travel, the poor mobile network coverage, and strict Australian aviation regulation. This project developed bespoke infrastructure and protocols to overcome these hurdles and the findings generated from this project will inform the advancement of remote community RPAS healthcare delivery operations in the NT and streamline the process for future initiatives nationwide. Offering a transformative, low-emissions approach to healthcare access in remote regions across Australia.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:02:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509087</guid>
    </item>
    <item>
      <title>Remotely piloted aircraft (drones) for safe road bridge inspections</title>
      <link>https://trid.trb.org/View/2431524</link>
      <description><![CDATA[The use of drones in road bridge inspection is an emerging technology with potential to revolutionise the way that road bridge inspections are carried out. This paper explores the advantages of using drones for bridge inspection, including increased safety for bridge inspectors, improved efficiency, and enhanced data collection. Transport for New South Wales (TfNSW) has been at the forefront of exploring the use of drones for bridge inspection, including the testing of various types of drones for different applications, such as confined space and water-resistant drones. TfNSW has taken a proactive approach to scope and develop a standard for the use of drones in the bridge industry. In a separate initiative, drone pilots are being trained to operate these drones safely and effectively. The adoption of drones will lead to improved safety and efficiency in road bridge inspections and maintenance, and efforts in this area demonstrate a commitment to safe and effective use of the technology to improve workplace safety for bridge inspectors.]]></description>
      <pubDate>Tue, 17 Sep 2024 14:49:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431524</guid>
    </item>
    <item>
      <title>Improving active mobility safety at signalised intersections using drone footage</title>
      <link>https://trid.trb.org/View/2431395</link>
      <description><![CDATA[The intersection of George Street and Wellington Street, West Perth, is signalised with pedestrian/bicycle crossings. Analysis of crash data revealed that cyclists are particularly at risk at this intersection. To gain a deeper understanding of user behaviours at this intersection, drone video footage was processed to produce telemetric data (direction of travel and speed) for vehicles and vulnerable road users (VRUs). By subsequently synchronising the footage with signal phases, it was possible to analyse several metrics that are difficult or costly to accurately collect by other means, including VRU crossings on red or green aspects and waiting times. The analysis revealed a high proportion of VRUs crossing on a red aspect, which was tied to the signals not being sufficiently optimised for VRUs or vehicles. The results were presented to the relevant roads authority which is now actively considering actions to improve safety for all road users at this intersection.]]></description>
      <pubDate>Tue, 17 Sep 2024 14:47:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431395</guid>
    </item>
    <item>
      <title>Evidence based safety assessment applying UAV and exposure principles</title>
      <link>https://trid.trb.org/View/2431371</link>
      <description><![CDATA[This paper outlines an approach to quantify risk-based driving behavior observations using a tethered unmanned aerial vehicle (UAV). Video provided bidirectional operating speeds, through the site as well as vehicle lane positioning and near miss conflicts. Applying known biomechanical tolerances to the observed conflict types and speeds, weighted by the observed frequency of occurrence, enabled us to derive the overall safety priority and likely causation. The recorded crash type for all crashes at this site was loss of control on a curve. This approach demonstrated 12% of southbound vehicles left their lane when negotiating the curve. Potentially a major causation factor leading to a loss of control and or head on crash. The overall findings enabled robust recommendations for safety treatments that corresponded with the highest risks that had the greatest potential to result in death and serious injury crashes. Specific recommended interventions were only available through UAV technology.]]></description>
      <pubDate>Tue, 17 Sep 2024 14:46:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431371</guid>
    </item>
    <item>
      <title>Air vehicle classification system and speed alert for the prevention of accidents on flat and curved roads</title>
      <link>https://trid.trb.org/View/2427427</link>
      <description><![CDATA[]]></description>
      <pubDate>Mon, 09 Sep 2024 17:01:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2427427</guid>
    </item>
    <item>
      <title>Route optimization of autonomous vehicles in the last-mile delivery operations</title>
      <link>https://trid.trb.org/View/2335112</link>
      <description><![CDATA[This thesis investigates collaborative human-robot systems for delivery and pickup tasks. Chapter 2 proposes a novel Mixed Integer Linear Programming (MILP) model for couriers and Autonomous Mobile Lockers (AMLs) in delivery tasks, with a heuristic algorithm for large instances, demonstrating cost savings. Chapter 3 introduces a delivery system using trucks and drones, presenting a Mixed Integer Programming (MIP) model and efficient heuristic algorithms for vehicle routing in large instances, highlighting optimal drone flight range and load capacity. Chapter 4 presents an MIP model for e-grocery delivery with multiple time windows, combining truck and drone deliveries. A hybrid heuristic algorithm and Constraint Programming approach offer quick quality solutions, outperforming traditional platforms. Sensitivity analysis reveals benefits of customer choice in multiple time slots. In summary, this thesis offers innovative models and algorithms for collaborative human-robot delivery systems, emphasizing efficiency, cost savings, and adaptability to diverse network setups and customer preferences.]]></description>
      <pubDate>Tue, 06 Feb 2024 09:05:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2335112</guid>
    </item>
    <item>
      <title>Measurement-based fading characteristics analysis and modeling of UAV to vehicles channel</title>
      <link>https://trid.trb.org/View/2304619</link>
      <description><![CDATA[With the rapid development of unmanned aerial vehicle (UAV) and autonomous driving technology, wireless communication between UAV and vehicles has become one of the hotspots in the research of intelligent transportation systems (ITS). Particularly, link-level UAV-based communication requires correlation characteristics of propagation channel. In the current channel measurement, the transmitter or receiver of the ground is fixed, which ignores the high dynamics of the vehicle and the complexity of the environment in the ITS scene. Therefore, it is necessary to conduct a dynamic channel measurement and analysis for UAV. In this paper, the authors carry out an UAV-to-Vehicle (U2V) measurement campaign in S- and C-band for multiple scenarios of low-altitude UAV and mobile vehicles propagation and provide a comprehensive investigation of channel fading characteristics. Based on the measurement data, the statistics of large-scale fading (path loss, shadow fading and its autocorrelation) and small-scale fading (amplitude distribution) for several typical measurement scenarios are extracted first, which are compared with other air-to-ground (A2G) and standard terrestrial propagation scenarios to analyze the U2V propagation characteristics in various scenarios. A comprehensive analysis and comparative study of all considered channel parameters extracted is then performed to reflect the physical laws behind the measurements. The analysis results reveal that the Log-distance model outperforms the considered typical models in terms of predicting the path loss, and the proposed autocorrelation model shows better performance than traditional models. The quantitative results are essential for modeling and realizing reliable communications in U2V wireless systems and analyzing the performance for UAV-enabled ITS.]]></description>
      <pubDate>Fri, 22 Dec 2023 08:46:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2304619</guid>
    </item>
    <item>
      <title>UAV-based multi-layered data collection methods and defect detection algorithms for Predictive Analytics and Bridge Asset Management</title>
      <link>https://trid.trb.org/View/2306871</link>
      <description><![CDATA[Bridge asset owners pay a high price per inspection and are sometimes unable to inspect their structures more frequently. The authors present an improved defect detection and quantification algorithm paired with a novel Unmanned Aerial Vehicle (UAV)-based data collection technology to detect and quantify surface and subsurface defects such as delamination, voids, and cracking. The data is collected in terms of Light Detection and Ranging (LiDAR), optical images, infrared images, and acoustic signatures and combined to quantify surface and subsurface defects in concrete bridges. Furthermore, the condition assessment on a time scale allows owners to make cost-efficient business decisions on what to repair and when to repair using the risk modelling methods. This approach also allows for building predictive deterioration models using historical performance, which can be utilised for asset management. This methodology was applied at a small scale on multiple bridge structures located in Canada, USA, and Australia. The multi-layered data helped create a baseline model of the structures that can be tracked over a period. The concrete deterioration, such as cracking, spalling, and delamination, were detected, quantified, and mapped to feed into the risk model. With this model, the asset owner can objectively determine whether further checking of a specific element, material research or a complete detailed re-examination is necessary, thereby leading to considerable savings in time and social costs.]]></description>
      <pubDate>Thu, 07 Dec 2023 14:55:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2306871</guid>
    </item>
    <item>
      <title>Integrated single target sensing and multiuser communications based on zero-forcing beamforming</title>
      <link>https://trid.trb.org/View/2214058</link>
      <description><![CDATA[Integrated sensing and communication (ISAC) is a promising approach that utilizes a single waveform for both information transmission and target object acquisition. In this study, the authors focus on the challenges posed by inter-user and inter-ISAC-antenna interference, specifically the self-interference from ISAC transmit antennas to receive antennas. Further, the benefit of a multi-functional ISAC scheme is clarified by comparing it with single function-aware schemes: sensing- and communication-aware approaches. To address these issues, they investigate the application of a zero-forcing (ZF)-based beamforming and power allocation (PA) technique. A sum rate maximization problem with a target object sensing signal-to-noise (SNR) threshold is formulated and split into two subproblems for improved tractability. To tackle the first subproblem of precoder design, they develop a constraint-aware greedy algorithm that employs communication SNR ordering. Subsequently, they propose a feasibility test-based algorithm to solve the second subproblem of PA.Through an extensive performance evaluation, encompassing computational complexity, sum rate, and ISAC received SNR, they verify that relying solely on a single function-aware scheme is insufficient for accommodating ISAC's multifunctional capabilities. Additionally, they demonstrate that a naive switching strategy between two single function-aware schemes can result in inefficient communication performance. Considering both sensing and communication functions, they identify the need for an adaptive balance between sensing and communication capabilities in a multi-functional ISAC system. This can be achieved by dynamically adjusting the target object sensing SNR threshold within the optimization problem's constraint. The proposed ZF-based ISAC with the designed PA scheme offers an optimal strategy when the transmit power of the ISAC is low. Conversely, an equal PA to multiple users emerges as the best strategy when the transmit power of the ISAC is sufficiently high. In conclusion, the authors' study highlights the significance of integrating sensing and communication functionalities in ISAC systems. Moreover, by providing insights into the interplay between target object sensing, beamforming, and power allocation, they contribute to the development of efficient and adaptable ISAC architectures.]]></description>
      <pubDate>Wed, 26 Jul 2023 15:59:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2214058</guid>
    </item>
    <item>
      <title>Validating the benefits of increased drone uptake for Australia: geographic, demographic and social insights</title>
      <link>https://trid.trb.org/View/2189273</link>
      <description><![CDATA[The final report for this project investigates Australia’s emerging drone sector, summarising the current state of development and lessons learnt from other countries. It also assesses the demographic and geographic determinants of increased drone uptake in Australia and identified key benefits from and challenges to increased drone uptake from the perspective of different communities and sub-populations.]]></description>
      <pubDate>Thu, 01 Jun 2023 14:55:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2189273</guid>
    </item>
    <item>
      <title>A review study on unmanned aerial vehicle and mobile robot technologies on damage inspection of reinforced concrete structures</title>
      <link>https://trid.trb.org/View/2145617</link>
      <description><![CDATA[]]></description>
      <pubDate>Mon, 03 Apr 2023 16:37:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2145617</guid>
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