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    <title>Research in Progress (RIP)</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>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Ensuring PNT Resiience</title>
      <link>https://rip.trb.org/View/2676001</link>
      <description><![CDATA[With CARMEN+ support the research team has characterized the timing properties of modulation from the Starlink constellation in order to assess its suitability for providing opportunistic pseudorange-based positioning, navigation, and timing (PNT) as a backup to Global Navigation Satellite System (GNSS). With the same purpose, the team has also uncovered key features of the OneWeb signal structure and has demodulated its data for the first time. The findings have indicated that opportunistic pseudorange-based PNT is not feasible using Starlink signals without aiding from a network of ground receivers. But given such a network, the team has achieved 10-meter-level positioning and 30-ns timing using Starlink signals. The next phase will extend this project along several lines: (i) characterize the modulation timing stability of OneWeb as the team has done with Starlink, (ii) deploy a network of 2 or 3 reference stations so that all ephemeris and transmission time modeling errors may be eliminated, (iii) employ super-resolution techniques to more precisely estimate modulation (e.g., Starlink frame) time of arrival, and (iv) analyze the pattern of assigned beams and side beams from Starlink satellites to predict how many unique satellites would typically be available for a PNT solution, and with what dilution of precision. For these studies, the team will capture and analyze broadband Starlink, OneWeb, and Kuiper data with their own RF equipment from multiple stations. The team believes that the outcome of this work will be of great importance, namely, a backup PNT system with global reach, decimeter positioning, nanosecond timing, inherent signal authentication (via cross-checking unpredictable broadband payload data and against a reference network), and improved resistance to jamming compared to traditional GNSS. Furthermore, the team aims to transfer this technology to their project partners for commercialization.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:15:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676001</guid>
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    <item>
      <title>Rapid Detection of Track Changes from In-Motion Data Acquisition Records: Lab Setup and Field Implementation – Year 3
</title>
      <link>https://rip.trb.org/View/2573191</link>
      <description><![CDATA[Track stiffness is a critical parameter influencing infrastructure integrity, safety, and maintenance efficiency. Track stiffness variations over time and space lead to uneven load distribution, track degradation, and increased risk of failure, necessitating continuous monitoring and timely intervention. Current technologies determine stiffness under loaded or unloaded conditions at discrete locations, or through continuous measurements. They are either costly, labor-intensive, or limited in spatial and temporal resolution. The proposed work is a four-year effort to develop an in-motion system that detects track stiffness and stiffness changes in real-time that is free of the shortcomings of existing techniques. The proposed system is an acceleration-based system that uses hybrid signal processing techniques and machine learning for classification. The system consists of three modules: (1) Data acquisition using onboard vibration sensors; (2) Hybrid signal processing on the edge for feature identification and data compression; and (3) Classification and decision support, utilizing machine learning algorithms for characterization of track conditions in predictive maintenance. This proposal is for Year 3 of the research team's current University Transportation Center for Railway Safety (UTCRS) sponsored effort. Year 1 focused on the development of a track stiffness monitoring concept and produced a feasibility study that led to Year 2 work on method development, and validation through simulations and laboratory small-scale testing. Spurred by the findings of Years 1&2, this proposal focuses on the development of an experimental prototype system and its validation through high-fidelity laboratory testing. In addition, the team proposes to develop a digital twin of the experimental prototype to facilitate extensive validation, calibration, and sensitivity studies to enhance accuracy and scalability. The project will enhance track safety, reduce maintenance costs, and improve railway infrastructure reliability by enabling continuous, cost-effective, and scalable monitoring. The research directly aligns with UTCRS’s strategic goals by advancing infrastructure monitoring technologies and contributes to the United States Department of Transportation (USDOT)’s objectives in safety and economic competitiveness.]]></description>
      <pubDate>Mon, 14 Jul 2025 20:04:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573191</guid>
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    <item>
      <title>Towards GNSS-less Navigation: Exploiting Terrestrial and LEO Satellite Signals of Opportunity</title>
      <link>https://rip.trb.org/View/2321512</link>
      <description><![CDATA[Today’s vehicular navigation systems extract position information from a suite of diverse and complementary onboard sensors. For example, a global navigation satellite system (GNSS) receiver provides stable absolute position information and an inertial measurement unit (IMU) and other dead reckoning sensors (e.g., wheel encoders) provide short-term accurate information. After prolonged periods of GNSS signal unavailability, the position solution degrades to unsafe levels as error-corrupted dead reckoning information is integrated without correction from an absolute position information source. Vehicle-mounted sensors (e.g., cameras or lidar) can reduce IMU drift during GNSS unavailability by tracking features in the environment (e.g., walls, light poles, trees, etc.) and then inferring the vehicle’s relative motion with respect to the features via a simultaneous localization and mapping (SLAM) framework. However, after extended periods of time without GNSS aiding corrections, the vehicles’ position estimate will still drift due to the accumulation of sensor errors (e.g., camera scale factor and lidar range errors due to dust and water particles). Over the past decade, signals of opportunity (SOPs); such as AM/FM radio, cellular, digital television, and low Earth orbit (LEO) satellite signals; have been studied and demonstrated as an effective backup or alternative source of absolute positioning information, providing corrections to an inertial navigation system (INS) in the absence of GNSS signals. SOPs possess several desirable characteristics for vehicular navigation: (1) available in most environments of interest; (2) difficult to jam all SOPs, since their signals are scattered throughout the spectrum; (3) produce low geometric dilution of precision, since their transmitters are geometrically diverse; (4) signal reception with carrier-to-noise ratio that is often tens of decibels (dBs) higher than that of GNSS signals; (5) free to use with SOP navigation receivers that do not require network subscriptions; and (6) no deployment cost, since their infrastructure is already operational and maintained by service providers. This project will study the achievable performance of GNSS-less navigation with SOPs, with a focus on cellular 5G and LEO. The study will compare the performance as a function of: (1) number of utilized transmitters (terrestrial 5G alone, LEO alone, and a fusion of both); (2) differential versus non-differential frameworks; (3) fusion with other onboard sensors; and (4) sensitivity to model mismatch.
]]></description>
      <pubDate>Thu, 11 Jan 2024 16:07:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321512</guid>
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      <title>Assisting Vision-Impaired Pedestrians to Cross Streets: An Innovative Accoustic Ranging Approach</title>
      <link>https://rip.trb.org/View/1635493</link>
      <description><![CDATA[This project aims to leverage innovative techniques to develop an intelligent system that assists blind pedestrians to decide when it is safe to cross streets, especially at the uncontrolled crossing locations, where neither traffic lights nor STOP signs are available. While great social resources have been designated to install the Accessible Pedestrian Signals (APS) at intersections to provide guidance instructions to people with visual impairments, it is economic infeasible to equip all intersections/walkways across the nation with such infrastructures. Currently, blind pedestrians mainly rely on themselves to decide if an uncontrolled street is clear to cross. The proposed system consists of two parts: vehicle-side acoustic source and pedestrian-side detection App. The source is designed to emit acoustic signals ranging from 16KHz to 20KHz that are out of human hearing. With the signals captured by the pedestrian’s smartphone, the App then estimates the moving speed, direction, and pedestrian-car distance for each vehicle nearby, so as to decide if any of them would cause collision to the pedestrian. The research team is going to leverage advanced signal processing and localization techniques as well as algorithm design to ensure the detection accuracy and efficiency. As deliverables to this project, the team will prototype the entire system, conduct in-field experiments, publish the results in top conferences, and prepare proposals for external funding opportunities. The success of this pilot project will largely enhance blind and vision impaired pedestrians’ experience on daily commute and bring significant social impact on transportation equality. ]]></description>
      <pubDate>Thu, 04 Jul 2019 10:30:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1635493</guid>
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