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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>Mixed Virtual Reality as an Aid in Advancing the Reliability and Robustness of Connected and Automated Vehicle Applications</title>
      <link>https://rip.trb.org/View/2675998</link>
      <description><![CDATA[The rigorous evaluation of safety critical Connected and Automated Vehicle (CAV) scenarios, faces some significant hurdles. Physical testing of scenarios (including edge-cases) presents risk and cost challenges as it is inherently dangerous, cost-prohibitive, and often non-reproducible. Additionally, purely virtual simulation lacks the real-world complexity of communication latency, interference, sensor noise profiles, and realistic representation of physical vehicle dynamics. To address this, the research team proposes using Mixed Reality (MR) co-simulation on a closed-course test track. This powerful alternative merges the real-world fidelity of a physical test platform (live sensor data, vehicle kinematics, real wireless communication channels) with the reproducible complexity of a virtual environment. This enables the safe and rigorous testing of otherwise impractical edge cases. The MR testbed facilitates comprehensive evaluation, addressing critical challenges for example: (1) Robustness and Reliability: It allows for precise injection of sensor degradation faults and failures and enables V2X reliability stress-testing in real-world communication and interference. (2) Cybersecurity and PNT Resilience: The platform safely simulates False Data Injection (FDI) and Denial of Service (DoS) attacks into the V2X communication channel, testing the Vehicle Under Test's Intrusion Detection Systems. Furthermore, it assesses system reliability when Position, Navigation, and Timing (PNT) data is compromised (e.g., via GNSS spoofing), evaluating the system's ability to use V2X data for positioning correction or safe mode transition. This framework leverages the validated utility of Hardware-in-the-Loop (HiL) platforms to rigorously evaluate the real-time performance and resilience of V2X protocols and sensor data fusion architectures on embedded edge computers. The project will leverage the existing highly-instrumented vehicle platform previously developed through the U.S. DOE ARPA-E NEXTCAR Program, which will serve as the Vehicle Under Test (VUT). Collaboration with TRC will be leveraged to facilitate the setup and validation of the MR testbed.]]></description>
      <pubDate>Mon, 02 Mar 2026 18:57:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675998</guid>
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    <item>
      <title>Immersive AR/VR learning to enhance pedestrian safety</title>
      <link>https://rip.trb.org/View/2663606</link>
      <description><![CDATA[Pedestrian injuries remain one of the leading causes of death among children in the United States and globally. Safe crossing behavior depends on cognitive and perceptual skills such as attention, hazard recognition, and gap judgment that are still maturing in younger populations. Traditional classroom instruction offers limited opportunities to practice these skills in realistic traffic contexts, highlighting the need for controlled, repeatable, and engaging training environments that can bridge the gap between knowledge and real-world decision making.
This project develops, tests, and disseminates an integrated Augmented Reality (AR) and Virtual Reality (VR) learning platform designed to improve pedestrian safety among children. The immersive simulator replicates crosswalks, intersections, and near-miss zones identified through the District Department of Transportation (DDOT) crash database and the DC Traffic Safety Data Portal. VR modules enable users to experience controlled crossings at high-risk intersections, while AR modules project digital traffic cues and guidance into real environments through tablets or mobile devices. Three-dimensional environments are constructed in the Unity or Unreal Engine platform and configured for both mobile devices and VR headsets to support flexible deployment across educational settings.
The methodology proceeds through four tasks: scenario design using DDOT crash data and Vision Zero reports to identify high-risk child pedestrian corridors, prototype development of immersive environments with realistic vehicle motion, environmental conditions, and compliant signal timing, controlled evaluation sessions with K-12 and university participants to assess realism, usability, and learning effectiveness, and dissemination including open-source release, a collaborative workshop with DDOT and school partners, and preparation of a deployment-ready package with simulation files, user manuals, and integration guides. Success is measured by improvements in hazard detection, gap judgment, and safe crossing decisions, with a target of at least 30 percent gain relative to baseline performance. The project also provides initial estimates of the number of crashes and injuries that could potentially be avoided if the toolkit were adopted more broadly in Washington, D.C.
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:36:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663606</guid>
    </item>
    <item>
      <title>Augmented Reality-Assisted Quality Control for Structural Component Placement in Bridge Construction </title>
      <link>https://rip.trb.org/View/2646964</link>
      <description><![CDATA[Construction quality control is an important part of building reliable infrastructure. This process starts with proper fabrication and depends heavily on how well the components are installed in the field. Construction requirements include surveying, documentation, inspection, and other means to control the quality of component placement during bridge construction. Making sure everything is placed and assembled correctly is key for the performance of the intended structural design over time. For instance, in bridge construction, even when components are fabricated within tolerance, improper placement during assembly can lead to alignment errors that compound over time, potentially affecting structural integrity, safety, and durability. This challenge has been observed in ongoing collaborations with New Mexico Department of Transportation (NMDOT) and Castillo Precast, where the transition from fabrication to field placement may introduce uncertainties that current quality control workflows lack to quantify. There are two main challenges: (1) planning properly given the tight schedules and different teams between the fabrication and installation on time and space, making it difficult to coordinate with all parties (precaster, inspector at the precast plant, truck driver, crane operator, field contractor, consultant at the site, owner); (2) recording, accessing and sharing the construction sequence over the life of the bridge when needed, for example 10-20 years later. 

To address these challenges, this project proposes a digital inspection and verification system that combines 3D scanning and Augmented Reality visualization to support quality control for structural component placement in bridge construction. The goal is to compare the as-built configuration of structural components with the design intent in real time, helping engineers detect deviations early and minimize the risk of cumulative construction errors. By engaging directly with active construction sites in New Mexico, the research takes into account practical challenges such as limited working space, variable lighting, irregular ground surfaces, weather exposure, and the fast-paced nature of construction schedules, all of which can affect the usability and reliability of digital tools in the field. Through this system, field personnel can visualize discrepancies between what was designed and what was built, directly overlaid on the structure without relying only on traditional tape measures, 2D plans, or surveying. The project also develops a QR code installed on each element that provides long-term access to critical data from fabrication and construction to be always at the bridge and accessible by scanning, supporting future inspections and maintenance activities by allowing users to retrieve component information directly on-site using Augmented Reality. ]]></description>
      <pubDate>Tue, 06 Jan 2026 17:16:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646964</guid>
    </item>
    <item>
      <title>Enhancing Structural Safety and Promoting Equity in Infrastructure Maintenance through Human-Centered Bridge Inspection empowered by Artificial Intelligence and Augmented Reality
</title>
      <link>https://rip.trb.org/View/2627937</link>
      <description><![CDATA[Bridges are crucial civil infrastructure, but their deterioration over time poses significant safety risks. Traditional human visual inspections are limited in accuracy and efficiency, leading to challenges in maintaining the inventory of bridges in the United States, particularly in economically disadvantaged communities. Leveraging recent advancements in computer vision (CV), artificial intelligence (AI), and augmented reality (AR), the team proposes a novel human-centered approach to enhance the accuracy and efficiency of concrete bridge inspections and promote equity in infrastructure maintenance. By automating detection and documentation of damage in concrete bridges, and empowering human inspectors by overlaying real-time detection results onto bridges thereby enabling human-machine collaboration, the project aims to improve inspection effectiveness and efficiency, promote equity in infrastructure maintenance, and enhance public safety.
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:16:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627937</guid>
    </item>
    <item>
      <title>ACRP Insight Event: Enhancing Operational Efficiency Through Extended Reality Technologies

</title>
      <link>https://rip.trb.org/View/2588325</link>
      <description><![CDATA[The airport industry faces increasing pressure to enhance operational efficiency due to growing passenger demands, heightened security measures, and airspace constraints. Traditional training methods and operational workflows often rely on outdated systems, leading to inefficiencies and increased costs. Building on the Airport Cooperative Research Program (ACRP) Project 07-26, “Extended Reality Possibilities in the Airport Environment,” which identifies categories of extended reality (XR) technologies, their benefits, best-use applications, and airport “readiness” to implement them, further research is needed to explore how XR technologies can be applied to enhance operational efficiencies at airports.  

OBJECTIVE: The objective of this project is to conduct an in-person ACRP Insight Event (see Special Note A) for airport-industry practitioners, relevant stakeholders, and subject matter experts (SMEs) to discuss XR technologies to determine how these technologies can be integrated into the airport environment.]]></description>
      <pubDate>Tue, 12 Aug 2025 10:26:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2588325</guid>
    </item>
    <item>
      <title>Augmented Reality for Highway Bridge Element Inspection</title>
      <link>https://rip.trb.org/View/2582913</link>
      <description><![CDATA[Condition assessment for bridges is a methodical task which requires substantial time, attention to detail, and precise documentation. As such, a single bridge inspection requires several hours of lane closures and multiple trained personnel to complete. Importantly, the recent transition to element level condition ratings has increased the details necessary to compile bridge inspection reports. While allowing more effective evaluation of bridge deterioration and prioritization of maintenance tasks, this has also increased the time and effort required from inspectors. It has also led to less consistency in how information is documented by bridge inspectors. These challenges present an opportunity for the New Mexico Department of Transportation (NMDOT) (and other agencies) to leverage augmented reality (AR) techniques to assist in specific inspection tasks and reduce the cognitive load on inspectors, helping them to execute faster while maintaining high performance. As bridge inspection guidelines continue to evolve, AR can also provide a platform to quickly integrate new automated inspection technology and changing requirements into the workflow.

Specifically, NMDOT is expecting the following from AR research to:

Facilitate routine tasks accelerating the inspection process and reducing mental fatigue.
Provide heads-up access to relevant element and defect codes from the American Association of State Highway and Transportation Officials (AASHTO) manual; Produce historical overlays of previous inspections, thus allowing better judgements on the progression of deterioration; and Leverage artificial intelligence (AI) to automatically label and measure defects (with a focus on concrete cracking and spalling), thus automating the generation of condition reports and well as providing additional insights.

 Importantly, the development of the AR interface will follow a user-centered design strategy to ensure that the interface is easy to use, easy to train for, and inspectors can interact with AI elements in order to ensure consistent performance.]]></description>
      <pubDate>Tue, 05 Aug 2025 11:39:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2582913</guid>
    </item>
    <item>
      <title>Statewide augmented information in the driver environment study</title>
      <link>https://rip.trb.org/View/2570738</link>
      <description><![CDATA[The purpose of this project is to increase lifelong independence and safe vehicle operation by improving access to environmental information while driving, particularly among older adults and people with visual impairments. By simulating a new framework for computer-vision assisted augmented reality (CVAR) in the driving environment, the research team intends to study how augmenting roadway elements (e.g., lines on the road, lane markers, and obstructions) improves overall operational performance. It is predicted that the universal design approach used in this work will not only substantially benefit older adult drivers but will also benefit all drivers. This is because the University of Maine (UMaine) CVAR solution can be used to improve access to roadway elements during situations of reduced visibility (e.g., at night or during inclement weather) while also highlighting an eventual suite of potential hazards (e.g., downed limbs, wildlife, and pedestrians).  

The work will expand UMaine's Virtual Environments and Multimodal Interaction Laboratory (VEMI Lab)’s current autonomous vehicle simulator (MOISIN: Multimodal Omnidirectional Immersive Simulator for Inclusive Navigation) to include a manual driving operational mode. Related software will also be developed to simulate new inclusive CVAR approaches that combine multisensory feedback with augmented visual information to expand access to a wide range of potential drivers. The resulting UIs will be tested in a series of user studies examining the impact on driving performance across various driving scenarios.]]></description>
      <pubDate>Wed, 02 Jul 2025 13:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2570738</guid>
    </item>
    <item>
      <title>Bridges to the Future: Training Inspectors With Augmented Reality Intelligent Interactive Storytelling</title>
      <link>https://rip.trb.org/View/2505724</link>
      <description><![CDATA[This project will develop an Augmented Reality Intelligent Interactive Storytelling (ARIIS)-based training simulation for new or future bridge inspectors. Work in Stage 1 will focus on developing the ARIIS-based scenarios. Semi-structured interviews will be conducted with experienced bridge inspectors from the Kentucky Transportation Cabinet to obtain their tacit knowledge on selected high-risk inspection activities with focus on (1) key decision/branching points, (2) communication skills in relaying applicable decision points with bridge crews and other constituencies, (3) circumstances surrounding the decision points, and (4) integration of knowledge, skills, and abilities in making decisions in those circumstances. The data collected from semi-structured interviews and focus groups will form the basis for creating bridge inspection interactive narratives for the ARIIS-based training. Experienced bridge inspectors will outline branching interactive stories focusing on the high-risk activities identified above. Digital content will be provided to visually support the narratives across all those activities, utilizing iTwinCapture, LiDAR, and Skydio drones to generate digital twins of typical bridges for the virtual environment for training. The platform will be developed in the Unity Game Engine. The Augmented Reality interface will be provided by the Microsoft HoloLens 2 headset. The branching interactive narratives developed in earlier tasks will be implemented using the Sabre Narrative Planner. A pilot test of the ARIIS-based training simulation will be conducted with domain experts who contributed to the development of the content of the simulation. In Stage 2, the ARIIS prototype will be evaluated through an experimental study with bridge inspectors in the state of Kentucky to assess the effectiveness of the innovation for bridge inspection training. At least 30 new or entry-level bridge inspectors will be recruited and randomly assigned to a control group and an intervention group to ensure unbiased comparison. Both groups will receive the same training content and evaluation, with the control group receiving traditional in-person, instructor-led training and the intervention group ARIIS-based training. Participants will be provided with an assessment based on Kirkpatrick’s model and the Learning Transfer Evaluation Model to evaluate the effectiveness of the innovation. Hypothesis testing will involve pairwise comparisons, correlation analysis, cluster analysis, and multivariate analysis. A non-parametric analysis will be employed, and statistical significance will be computed at 95% confidence level. The final report will provide all relevant data, methods, models, and conclusions along with guidance on how to develop ARIIS scenarios and use the platform within a state DOT. ]]></description>
      <pubDate>Tue, 04 Feb 2025 16:41:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505724</guid>
    </item>
    <item>
      <title>Automated Quality Assessment of Precast Members using Lidar and Augmented Reality </title>
      <link>https://rip.trb.org/View/2480359</link>
      <description><![CDATA[Prefabrication practices have been developed to manufacture concrete components of infrastructures in factories, transporting them to construction sites, and ultimately assembling them as final products onsite. However, there are a few problems with these processes, such as inaccuracy in benefit evaluation methods, and difficulties in the quality management of prefabricated construction projects. If we could track the quality in the fabrication process, then it is easier to keep a permanent record from cradle to grave for future implementations. 
Construction, fabrication, and final product tolerances describe the dimensional relationships of each precast component that make up a whole structure at the different stages of production which will ultimately affect their durability and service life. It is crucial that the form’s dimensions are correct so that the manufactured product has the correct tolerances. Also, it is important to have a clear understanding of the appropriate product tolerances before a project begins, along with the correct interfacing tolerances and erection tolerances. This creates clear expectations for the project and ensures that everyone is working within the same agreed tolerances. This research project will develop an AR tool and interface as well as scanning in the field that enables easy information access, automatic data collection, and real-time data analysis by providing a dimensional verification of precast members inspection using 3D scanning. The focus will be prestressed I-beams that are cast in New Mexico, but it can be used for box girders, and other beams of interest in other transportation infrastructure. 
This research aims to develop an innovative framework utilizing 3D point cloud data to provide insights into production variations. The objective is to empower factory operators with actionable information for enhancing quality control and optimizing processes with an interface both with the data and the product itself so it can be rectified in the field. To facilitate efficient decision-making by inspectors, a solution will be developed to expedite the identification of incorrect precast strands, rebar, stirrups, chairs, spacers, plates, and eventually the concrete dimensions of the casted members. AR Visualization technology will be employed to overlay holographic representations of precast concrete elements onto real-world objects. These holographic representations will be color-coded to indicate which errors are found during construction, and which members should not be utilized in construction, aiding inspectors in quickly identifying deviations from quality standards during fabrication and at the precast plant. They can display the design and error and keep it to compare later.
The tasks involved in this project include the following: Task 1: Adapt the AR application to PC inspection requirements; Task 2: Enhance dimensional measurement accuracy across varying lighting conditions; Task 3: Establish a standardized procedure for PC element inspectors utilizing AR and LiDAR.
]]></description>
      <pubDate>Wed, 01 Jan 2025 17:01:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2480359</guid>
    </item>
    <item>
      <title>Extended Reality Possibilities in the Airport Environment</title>
      <link>https://rip.trb.org/View/2226008</link>
      <description><![CDATA[Extended reality (XR) is the umbrella term for immersive technologies such as augmented reality (AR), virtual reality (VR), and mixed reality (MR). AR blends the real world with a digital world, virtual reality immerses the user in a digital world, and MR is a mix of both. XR is a great way to put written concepts into some type of visualization. The military has been using XR in certain applications to improve efficiency, safety, and productivity. Airlines are using XR to help train flight attendants and aviation mechanics and as an entertainment option for their passengers. The number of applications and industries where XR can provide benefits is growing.

XR used in other industries may also apply in the airport environment for airport operators. The benefits can be internal (e.g., used by employees in their day-to-day work or in training) or external (e.g., used by passengers and other stakeholders). Further research is needed to identify what immersive technologies could be most useful to airport operators. 

The objective of this project is to develop a report on the potential applications of XR at airports for airport operators.]]></description>
      <pubDate>Tue, 08 Aug 2023 06:59:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2226008</guid>
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