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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
    <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>Research on Indoor Location Technology in Metro Station</title>
      <link>https://trid.trb.org/View/1975954</link>
      <description><![CDATA[In order to assist blind people to travel independently and solve the problems of jumping of location points and poor real-time location caused by traditional indoor location method, a method based on region classification and electronic map fusion is proposed. In this method, the idea of position fingerprint matching is adopted. In the off-line stage, the best Gauss filtering template is searched for RSSI sequence generated by Bluetooth sensor by iteration optimization. The filtered sequence mean is used to construct position fingerprint database, and the support vector machine model is used to classify the position fingerprint database at the first level. In the online stage, the idea of sliding window is used to classify the location area in two levels. In the window range, KNN algorithm based on Euclidean distance is used to calculate the position coordinates, and the path layer information of electronic map is used to correct the position coordinates, so as to further control the error range and improve the location efficiency. Experiments in the subway station hall show that the filtering method improves the positioning accuracy by nearly 4%, and the positioning accuracy can reach 1.59 m by using this positioning algorithm.]]></description>
      <pubDate>Mon, 26 Aug 2024 16:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/1975954</guid>
    </item>
    <item>
      <title>Accuracy Analysis of UAV Coordinates Using Egnos and Sdcm Augmentation Systems</title>
      <link>https://trid.trb.org/View/2417273</link>
      <description><![CDATA[SBAS systems are applied in precise positioning of UAV. The paper presents the results of studies on the improvement of UAV positioning with the use of the EGNOS+SDCM solutions. In particular, the article focuses on the application of the model of totaling the SBAS positioning accuracy to improve the accuracy of determining the coordinates of UAVs during the realisation of a test flight. The developed algorithm takes into account the position errors determined from the EGNOS and SDCM solutions. as well as the linear coefficients that are used in the linear combination model. The research was based on data from GPS observations and SBAS corrections from the AsteRx-m2 UAS receiver installed on a Tailsitter platform. The tests were conducted in September 2020 in northern Poland. The application of the proposed algorithm that sums up the positioning accuracy of EGNOS and SDCM allowed for the improvement of the accuracy of determining the position of the UAV by 82-87% in comparison to the application of either only EGNOS or SDCM. Apart from that, another important result of the application of the proposed algorithm was the reduction of outlier positioning errors that reduced the accuracy of the positioning of UAV when a single SBAS solution (EGNOS or SDCM) was used. The study also presents the effectiveness of the proposed algorithm in terms of calculating the accuracy of EGNOS+SDCM positioning for the weighted average model. The developed algorithm may be used in research conducted on other SBAS supporting systems.]]></description>
      <pubDate>Tue, 20 Aug 2024 16:17:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417273</guid>
    </item>
    <item>
      <title>High-Dimensional Multi-Objective Bayesian Optimization With Block Coordinate Updates: Case Studies in Intelligent Transportation System</title>
      <link>https://trid.trb.org/View/2325340</link>
      <description><![CDATA[Many transportation system problems can be formulated as high-dimensional expensive multi-objective problems. They are challenging for Gaussian process-based Bayesian optimization methods to find the Pareto fronts due to the curse of dimensionality and the boundary issue in the acquisition function optimization. This paper presents a multi-objective Bayesian optimization method with block coordinate updates, Block-MOBO, to solve high-dimensional expensive multi-objective problems. Block-MOBO first partitions the decision variable space into different blocks, each of which includes a low-dimensional multi-objective problem. At each iteration, one block is considered and the decision variables not in this block are approximated by context-vector generation embedded with the Pareto prior knowledge thus promoting convergence. To tackle the boundary issue, the authors present 𝜖-greedy acquisition function in a Bayesian and multi-objective fashion, which recommends candidates either from the exploitation-exploration trade-off perspective or with probability 𝜖 from the Pareto dominance relationship perspective. They verify the effectiveness of Block-MOBO by comparing it with other multi-objective Bayesian methods on two real-world optimization problems in transportation system and three multi-objective synthetic test suites. The experimental results show that Block-MOBO can find more evenly distributed and non-dominated solutions in the whole search space with lower complexity compared with other state-of-the-art approaches. Their analyses illustrate that block coordinate updates and 𝜖-greedy acquisition function contribute to computational complexity reduction and convergence-diversity trade-offs, respectively.]]></description>
      <pubDate>Thu, 27 Jun 2024 14:11:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2325340</guid>
    </item>
    <item>
      <title>Virtual Barriers for Mitigating and Preventing Run-Off-Road Crashes Phase II</title>
      <link>https://trid.trb.org/View/2378910</link>
      <description><![CDATA[This research study describes progress made during a multi-year evaluation of concepts to prevent vehicles from departing the roadway. During Phase 2, researchers identified potential methods of interpreting road coordinates using vehicle dynamics concepts. Current intelligent technology was investigated to learn about the current status of technology and evaluate potential system gaps and flaws to develop a supplementary system. The second phase of this project proposes a novel method to offer an extra level of redundancy to current vehicle guidance systems. The method is separated into three main modules denoted as: Local Path Generation, Local Positioning, and Vehicle Guidance/Warning. The Local Path Generation module explored techniques to wirelessly convey road data to a vehicle while requiring the minimum amount of data and transmission time. The guidance information is collected to develop a local road database and referenced locally, geospatially, and relative to other adjacent road segments. As well, the vehicle instantaneous position is identified using the Local Positioning module, in which the coordinates of the vehicle can be quickly related in terms of position, speed, and orientation with respect to the roadway with minimal lag. The Vehicle Guidance System module is the reaction system which compares data from Local Path Generation and Local Positioning modules to determine if the risk of roadside departure exceeds an unacceptable level of risk, and responds by notifying the driver and/or performing safety maneuvers to control the vehicle path. Feasibility and application of these modules and concepts were explored and further research recommendations were provided for the third and final year of MATC funding.]]></description>
      <pubDate>Tue, 21 May 2024 10:52:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2378910</guid>
    </item>
    <item>
      <title>Estimation of Three Mutually Orthogonal Vanishing Points from Edgelets in Road Scenes</title>
      <link>https://trid.trb.org/View/2329257</link>
      <description><![CDATA[Field-of-view calibration is essential for establishing the relationship between 2D image coordinates inside the camera and 3D real-world coordinates of the traffic scene. For many surveillance-based traffic applications, the field-of-view calibration involves precisely extracting the three orthogonal vanishing points. However, many traffic scenes lack parallel lines along all three dominant directions, making it difficult to successfully calibrate using the present methods. This study proposed a novel method for estimating the three mutually orthogonal vanishing points in traffic scenes. To determine the dominant directions of the real-world coordinate frame, this method exploits the visual features of both the road environment and moving vehicles and avoids the need for parameter tuning through trial and error in different scenarios. To evaluate the performance of the proposed method, laboratory tests were conducted. The outcomes demonstrated the potential of the method in traffic scenes with scarce parallel line features.]]></description>
      <pubDate>Fri, 29 Mar 2024 10:01:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2329257</guid>
    </item>
    <item>
      <title>Study on Location of Distribution Park in City Based On Coordinate Analysis Method</title>
      <link>https://trid.trb.org/View/2281662</link>
      <description><![CDATA[This paper establishes the coordinate analysis method to select from some projects. In the paper, firstly, the theories of distribution park location are introduced and the index selection is evaluated. Then the mathematical model of this method and list the viable computing steps are established. Finally, an example are given to test the model in the location of a city distribution park, and then, some proposals are presented.]]></description>
      <pubDate>Fri, 15 Mar 2024 16:36:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2281662</guid>
    </item>
    <item>
      <title>Vehicle trajectory data extraction from the horizontal curves of mountainous roads</title>
      <link>https://trid.trb.org/View/2259820</link>
      <description><![CDATA[Trajectory data is essential for understanding the driver-vehicle-road interaction which is crucial for safety and operational assessment. The video image-processing technique is useful to collect such data on the entire traffic stream. Existing techniques assume that the road is planar which limits the application of the video-based traffic data collection to the plain terrains. With the objective to collect trajectory data from mountainous terrain, the present study proposes an approach to convert the pixel coordinates into real-world coordinates. The hypothesis was that a non-planar scene could be converted into a sequence of piece-wise planar scenes with a separate projection matrix for each planar region. The analysis shows that the proposed method could effectively transform the pixel coordinates into the real-world coordinates. Comparison of the estimated and observed speed/path profiles indicate the adequacy of the proposed method in collecting video-based trajectory data from mountainous terrain.]]></description>
      <pubDate>Mon, 23 Oct 2023 16:52:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2259820</guid>
    </item>
    <item>
      <title>Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing</title>
      <link>https://trid.trb.org/View/2141011</link>
      <description><![CDATA[LiDAR odometry (LO) has gained popularity in recent years due to accurate depth measurement and robustness to illumination. Typically, the solutions based on scan-to-map matching mainly optimize current pose. To further reduce the accumulated error of pose estimation, the fixed-lag smoothing that optimizes fixed-size poses simultaneously by matching corresponding point features of multiple frames becomes necessary. The integration of fixed-lag smoothing with LO still needs further exploration. In this paper, a general fixed-lag smoothing module is proposed, which can be appended to existing LO framework to improve the consistency of trajectory. Also, a fast scan-to-map matching module based on sparse features is developed to guarantee the real-time performance. Besides, the feature-centric feature management strategy is adopted in both scan-to-map matching and fixed-lag smoothing modules, which makes the proposed LO efficient. On this basis, a hierarchical estimation-based LiDAR odometry is presented, where low-level scan-to-map matching estimates pose of each frame by aligning associated features in the frame and corresponding surrounding map with high efficiency, and high-level fixed-lag smoothing further optimizes keyframe poses in a sliding window by matching associated features among multiple frames with high accuracy. As a result, a fast and accurate pose estimation is achieved, which is verified by experiments on the KITTI dataset, Newer College dataset, and an actual outdoor scenario.]]></description>
      <pubDate>Wed, 31 May 2023 10:58:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2141011</guid>
    </item>
    <item>
      <title>Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving</title>
      <link>https://trid.trb.org/View/2140985</link>
      <description><![CDATA[In literature, Extended Object Tracking (EOT) algorithms developed for autonomous driving predominantly provide obstacles state estimation in cartesian coordinates in the Vehicle Reference Frame. However, in many scenarios, state representation in road-aligned curvilinear coordinates is preferred when implementing autonomous driving subsystems like cruise control, lane-keeping assist, platooning, etc. This paper proposes a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with an Unscented Kalman Filter (UKF) estimator that provides obstacle state estimates in curvilinear road coordinates. The authors employ a hybrid sensor fusion architecture between Lidar and Radar sensors to obtain rich measurement point representations for EOT. The measurement model for the UKF estimator is developed with the integration of coordinate conversion from curvilinear road coordinates to cartesian coordinates by using cubic hermit spline road model. The proposed algorithm is validated through Matlab Driving Scenario Designer simulation and experimental data collected at Monza Eni Circuit. The Experimental Dataset will be made publicly available upon the paper acceptance.]]></description>
      <pubDate>Wed, 31 May 2023 10:58:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2140985</guid>
    </item>
    <item>
      <title>Examination of Different Models of Troposphere Delays in Sbas Positioning in Aerial Navigation</title>
      <link>https://trid.trb.org/View/2166461</link>
      <description><![CDATA[This paper presents the results of a study on the use of different tropospheric correction models in SBAS positioning for air navigation. The paper, in particular, determines the influence of the Saastamoinen troposphere and RTCA-MOPS models on the determination of aircraft coordinates and mean coordinate errors in the SBAS positioning method. The study uses real kinematic data from a GPS navigation system recorded by an onboard GNSS satellite receiver as well as SBAS corrections. In the experiment, the authors include SBAS corrections from EGNOS and SDCM augmentation systems. The navigation calculations were performed using RTKLIB v.2.4.3 and Scilab 6.1.1 software. Based on the conducted research, it was found that the difference in aircraft coordinates using different troposphere models can reach up to ±2.14 m. Furthermore, the use of the RTCA-MOPS troposphere model improved the values of mean coordinate errors from 5 to 9% for the GPS+EGNOS solution and from 7 to 12% for the GPS+SDCM solution, respectively. The obtained computational findings confirm the validity of using the RTCA-MOPS troposphere model for SBAS positioning in aerial navigation.]]></description>
      <pubDate>Thu, 25 May 2023 17:41:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2166461</guid>
    </item>
    <item>
      <title>Vision-Based Lane Departure Detection Using Local Hough Transform and Particle Filter</title>
      <link>https://trid.trb.org/View/2000527</link>
      <description><![CDATA[Lane-departure detection is an interesting and challenging task in the field of computer vision, providing an efficient way to improve driving safety. In this paper, the authors present a vision-based lane-departure detection algorithm using local Hough transform and particle filter. A monocular vision-based experiment platform also has been designed for implementing the algorithm. There are two main contributions in this work: Firstly, a novel lane detection method using local Hough transform was proposed to obtain the lane marking coordinates location. This method was proved to apply to fast and dynamic detection results. Secondly, the authors designed a detecting and tracking strategy, which is implemented by a particle filter, to ensure the stability of the system. The actual roads experiments are used to test the performance of the algorithm, and the experimental results shows that the proposed method is of demonstrated feasibility and reliability on actual roads.]]></description>
      <pubDate>Tue, 28 Mar 2023 09:53:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2000527</guid>
    </item>
    <item>
      <title>Vision-GPS Fusion for High Precision Vehicle Localization in Urban Environment</title>
      <link>https://trid.trb.org/View/2000525</link>
      <description><![CDATA[In this paper, the authors proposed a method for vehicle localization with low-cost GPS and a monocular camera. Traditionally, the positioning accuracy of a single GPS can merely be reached at the metric-level both laterally and longitudinally. By integrating vision and GPS with the prior position database, which was generated by the bag-of-words method and improved by lane change detection, this method can achieve effective improvement of positioning accuracy, which is able to acquire the location of lane laterally and more precise vehicle coordinates than raw GPS data. In addition, by leveraging the complementary global information, the GPS-aided vision method has the advantages of avoiding inaccurate pose initialization of SLAM systems and obtaining absolute coordinates which can be deployed to navigation directly. The proposal was described by pseudocode in detail and was evaluated by simulation and a dataset collected by the authors’ team in an urban environment. The improvement and validity were verified by experiment based on the proposed evaluation criteria.]]></description>
      <pubDate>Tue, 28 Mar 2023 09:53:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2000525</guid>
    </item>
    <item>
      <title>Simulation Modeling for Evaluation of Efficiency of Observed Ship Coordinates</title>
      <link>https://trid.trb.org/View/1977364</link>
      <description><![CDATA[Simulation computer modeling was used to evaluate the efficiency of the vessel’s observed coordinates using the mixed laws of distribution errors of the first and second type for lines of position (LOP). Simulation modeling showed good convergence of evaluation of efficiency calculated by analytical expressions and obtained by simulation. A graphical depiction of the observed points’ deviation relative to the mathematical expectation in the case of distribution of LOP errors of both types according to mixed laws is obtained by the method of least squares and the method of maximum likelihood estimation.]]></description>
      <pubDate>Wed, 24 Aug 2022 15:02:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977364</guid>
    </item>
    <item>
      <title>Model of the Motion of a Navigation Object in a Geocentric Coordinate System</title>
      <link>https://trid.trb.org/View/1925463</link>
      <description><![CDATA[In this paper the author describes the creation of a model of the motion of a flying object in a geocentric coordinate system (ECEF - Earth-Centered, Earth-Fixed). Such a model can be used to investigate the accuracy and resistance of radio navigation systems to interference. The essence of the design of the model lies in the mathematical description of the motion of a flying object in a geocentric coordinate system. The flight trajectory of a flying object consists of one straight section and two turns. When creating a model, they assume a flight at a constant altitude. In this paper, they present one of the possible procedures for modelling the motion of a flying object in a geocentric coordinate system. The author chose the initial coordinates of the flying object according to flightradar 24. They used the Matlab software for computer simulation.]]></description>
      <pubDate>Wed, 25 May 2022 09:40:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1925463</guid>
    </item>
    <item>
      <title>Submarine pipeline tracking technology based on AUVs with forward looking sonar</title>
      <link>https://trid.trb.org/View/1932084</link>
      <description><![CDATA[Submarine pipelines have provided an important approach to transport marine oil and gas resources because of their simplicity and efficiency. However, natural or man-made causes can easily lead to pipeline damage and thus result in waste of resources and environmental pollution. At present, the manual routine inspection of submarine pipelines is mainly completed by the survey vessels, which requires a large workload and high cost. The use of autonomous underwater vehicles with multibeam sonar system for real-time surveys is a good solution to this problem. Yet, the accumulation of errors in the integrated navigation system of the vehicle over a long period of operation can cause relatively large position deviations, since the signals of global positioning system cannot be received in the deep sea. To deal with it, the forward looking sonar is adopted in this study to obtain the precise position of the submarine pipeline and a tracking algorithm is proposed to track it. Firstly, the software development kit of the forward looking sonar is used to continuously obtain the pseudo-color images of the submarine pipeline, which are converted into 8-channel gray-scale images for image segmentation and binarization. Secondly, the optimized Rosenfeld algorithm is applied to thin the binary images, after which the submarine pipeline is identified and the corresponding function is fitted to determine the location of the target point to be tracked. Finally, the geodetic coordinates of the target point are determined through coordinate conversion to position the submarine pipeline. A sea trial is carried out and the results prove that accurate and stable pipeline tracking can be achieved by combining the integrated navigation system and the proposed algorithm.]]></description>
      <pubDate>Wed, 25 May 2022 09:40:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1932084</guid>
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