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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>AI-Enabled Vision System for Intersection Analytics </title>
      <link>https://rip.trb.org/View/2673053</link>
      <description><![CDATA[Phase I of this project revealed limitations of using a single camera per intersection to automatically extract key traffic performance and safety information from video feeds. To overcome these limitations and enhance data accuracy, the Phase II approach will deploy a second camera at selected high-impact intersections. By fusing the views from two different camera angles, the system can establish a true spatial relationship of objects in the intersection, essentially achieving a more complete 3D understanding of vehicle and pedestrian trajectories.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:00:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673053</guid>
    </item>
    <item>
      <title>Pavement-Maintenance-GPT: Optimizing Pavement Maintenance Decisions Using Generative AI </title>
      <link>https://rip.trb.org/View/2652179</link>
      <description><![CDATA[Pavement maintenance has significant impacts on transportation safety, mobility, and asset management. However, current decision-making for prioritizing pavement repairs often relies on subjective assessments, manual reviews, or disparate datasets, causing inefficiencies, increased vehicle emissions, and occupational health risks. This project proposes “Pavement-Maintenance-GPT,” a large language model designed to optimize repair prioritization decisions using high-fidelity Ground Penetrating Radar (GPR) video log data. 
 
Pavement-Maintenance-GPT leverages advanced generative AI to simulate expert decision-making, synthesizing pavement condition data into actionable maintenance strategies. The LLM will be trained on historical data, expert judgments, and GPR metrics to replicate and enhance human diagnostic capabilities. By providing precise and efficient repair recommendations, the model significantly improves mobility efficiency by reducing unnecessary lane closures while decreasing vehicle idling and associated fuel consumption. 
 
Aligned with CHEM’s focus areas of “Occupational Health and Efficient Mobility,” this research addresses occupational hazards by minimizing workers’ exposure to construction-related risks through optimized maintenance schedules. Additionally, it promotes efficient mobility by lessening disruptions, thereby improving overall roadway mobility. 
 
The anticipated outcomes include improved resource allocation, reduced vehicle emissions, enhanced safety for workers and road users, lower lifecycle costs, and greater transportation system performance. Pavement Maintenance GPT represents a transformative advancement, providing transportation agencies with a robust, scalable, and sustainable solution for optimized infrastructure maintenance, directly contributing to safer, healthier, and more efficient mobility. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:10:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652179</guid>
    </item>
    <item>
      <title>Explaining Visual Attention for Autonomous Vehicle Controllers</title>
      <link>https://rip.trb.org/View/2640186</link>
      <description><![CDATA[End to end deep learning controllers can produce strong driving performance, but their internal decision processes are difficult to interpret. This lack of clarity makes it harder for engineers to diagnose failures and can reduce public confidence in automated systems. This project will create a counterfactual explanation framework that identifies which elements in camera images, such as vehicles, pedestrians, or traffic control devices, guide actions like braking or steering. The research will apply generative video inpainting to remove or alter specific visual elements and then observe how the autonomous controller responds to these modified scenarios.

The study will integrate this method with the ADAPT architecture and evaluate it using benchmark datasets and both real and simulated environments, including the QCar testbed. The goal is to provide clear, intuitive explanations for controller decisions that support transparency and improve safety analysis. The framework will help engineers understand system behavior, locate potential weaknesses, and develop autonomous vehicle (AV) technologies that behave in ways that can be evaluated and verified.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:37:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640186</guid>
    </item>
    <item>
      <title>Obstacles to the Widespread Implementation of Autonomous Vehicles in Vermont</title>
      <link>https://rip.trb.org/View/2640182</link>
      <description><![CDATA[Autonomous vehicle (AV) technology continues to progress, yet many regions face significant challenges that slow real-world deployment. Vermont provides a clear example of this problem because its rural road network, variable pavement quality, and severe winter weather affect how sensors interpret the roadway environment. Snow, fog, potholes, and non standard intersections can interfere with optical and sensor-based systems, which can reduce navigation accuracy and vehicle decision making. These obstacles demonstrate the need to understand how environmental and infrastructure conditions influence automated systems in rural settings.

This project will document AV performance challenges through field observation and video recordings that capture roadway conditions and driver behavior. Researchers will categorize the effects of surface deterioration, poor markings, weather events, and irregular geometry on system reliability and object detection. The study will also evaluate opportunities for roadway design changes, sensor redundancies, and maintenance practices that can support more consistent AV operation. The findings will guide planners and engineers as they prepare for gradual adoption of automated vehicles in Vermont and other regions with similar conditions.]]></description>
      <pubDate>Thu, 11 Dec 2025 12:58:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640182</guid>
    </item>
    <item>
      <title>Shared Space Safety: A Study of Campus Travel and Mixed Mode Interactions</title>
      <link>https://rip.trb.org/View/2625580</link>
      <description><![CDATA[A study of campus travel and mixed-mode interactions will develop a data-driven baseline of safety conditions on Hilltop Way at San Diego State University—a steep roadway where pedestrians, skateboarders, scooter users, cyclists, and vehicles frequently converge, creating conflicts during class transitions. Video data collected from both ground-level cameras and aerial drone footage will capture user behaviors, travel speeds, yielding patterns, and near-miss events. Analytical techniques such as post-encroachment time (PET) and computer-vision–based variable extraction will be applied to quantify the frequency and severity of potential conflicts. The resulting dataset and safety assessment framework will enable rigorous before–after evaluations of future countermeasures introduced by the university, allowing their effectiveness to be measured in terms of changes in near-crash indicators and interaction dynamics. The project’s outputs—including annotated datasets, analysis tools, and methodological guidelines—will provide a transferable model for studying multimodal safety on shared streets, advancing United States Department of Transportation priorities in safety, innovation, and data-driven decision-making.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:50:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625580</guid>
    </item>
    <item>
      <title>AI-Powered Infrastructure Monitoring and Decision Support for Transportation Safety</title>
      <link>https://rip.trb.org/View/2606407</link>
      <description><![CDATA[This research enhances transportation safety through large-scale deployment of intelligent video analysis and artificial intelligence techniques for real-time infrastructure monitoring, building on two years of previous development. The Year 3 initiative expands the proven framework beyond bridges to include urban infrastructure including traffic signals, light poles, pedestrian bridges, and other structural components essential for safe transportation operations. The methodology leverages existing surveillance infrastructure to capture displacement and vibration signals through computer vision techniques including feature tracking, optical flow, and motion magnification, eliminating the need for extensive physical instrumentation. Advanced signal processing methods integrate with video analytics to quantify dynamic behavior and detect structural changes over time. Inverse modeling algorithms estimate key structural parameters including stiffness, mass distribution, and damage locations from measured displacement histories. The research develops an AI-based decision support system combining analysis results with predictive capabilities to help agencies prioritize maintenance work based on risk assessment and structural condition evaluation. A pilot program implementation with District of Columbia Department of Transportation demonstrates real-world applicability in urban settings, serving as a platform for practical validation and stakeholder engagement.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:13:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606407</guid>
    </item>
    <item>
      <title>Video Summary Report (VSR) Production</title>
      <link>https://rip.trb.org/View/2593193</link>
      <description><![CDATA[The research team will produce Video Summary Reports (VSRs) that promote technology transfer of concluding Texas Department of Transportation (TxDOT) research and implementation projects. The research team will produce short videos (2–5 minutes) informing TxDOT staff and university researchers, as well as others nationwide, about the research and implementation results. The research team will plan and produce an annual Video Summary Report (VSR) production list in cooperation with TxDOT, track the detailed status of each VSR on the production list, schedule and conduct interviews (live and virtual), travel when requested and necessary to obtain B-roll for enhanced video experience, compose and create the VSRs, coordinate all reviews and approvals, and oversee the final publication and distribution of the final products. The research team will also produce other outreach materials as requested.]]></description>
      <pubDate>Tue, 26 Aug 2025 12:49:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593193</guid>
    </item>
    <item>
      <title>Development of a Prototype Turnkey Artificial Intelligence Aided Automated Trespassing Detection Solution Based on Stationary Cameras



</title>
      <link>https://rip.trb.org/View/2572329</link>
      <description><![CDATA[This Type II IDEA project will develop and test a prototype turnkey artificial intelligence aided trespassing detection system.  The system consists of integrated hardware (solar security trailer, networking equipment, etc.) and software that was proven in an earlier project funded by the Federal Transit Administration and Federal Railroad Administration. This system will be developed and tested in collaboration with the industry partner, SunRail, a commuter rail system in the greater Orlando, Florida area. The system hardware will be assembled and installed at selected locations. Data will be collected in those locations for 12 months, and the information will be analyzed to provide actionable safety data to SunRail, the industry collaborator. SunRail will install fencing along their right-of-way. This system could be used to gather trespassing data before and after the fencing installation to evaluate the effectiveness of the solution. At grade crossings, violation data could be used to justify upgrades like the installation of quad gates, gate skirts, or dynamic envelopes based on the types of violations observed. This data can improve trespassing mitigation decision making and support grant applications for further actions. Following this task, sample video data will be collected and analyzed to ensure system accuracy and data quality. The developed system will benefit railroad industry by enabling the collection of previously unavailable trespassing and grade crossing violation information.  It is rather unfeasible to have railroad staff manually annotate video feeds to acquire trespassing data.  This system, on the other hand,  will automatically watch and understand trespass behavior from video feeds at remote locations. Trespass and grade crossing violation information will be aggregated in a trespasser database, presenting users with a video clip of the trespassing event and corresponding metadata (time, weather, type: person, car, motorcycle etc.). Trends and common behaviors can be determined once enough of these events are aggregated.]]></description>
      <pubDate>Tue, 08 Jul 2025 16:55:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572329</guid>
    </item>
    <item>
      <title>Enhancing Road Safety Through Video Analytics and Connected and Automated Vehicles (CAV)</title>
      <link>https://rip.trb.org/View/2562267</link>
      <description><![CDATA[This project develops and demonstrates an end-to-end prototype that integrates roadside video analytics with connected vehicle (CV) technology to enable real-time safety warnings for drivers. Building upon the MSight roadside perception platform, the system detects, tracks, and predicts vehicle and vulnerable road users (VRU) trajectories using infrastructure-mounted cameras, then transmits safety-critical information to vehicles via C-V2X communication. A vehicle-side onboard application processes received messages and delivered timely, intuitive warnings to drivers. Rather than focusing on productization alone, the work uses a prototype-and-field-test approach at Mcity to quantify current technical performance, identify key technology gaps and integration barriers (e.g., detection reliability for VRUs, end-to-end latency, communication constraints, and driver warning usability), and translate findings into prioritized, actionable recommendations. The outcome is a practical assessment of what today’s video analytics + V2X stack can and cannot deliver, and a roadmap of high-impact next steps for DOTs to advance toward deployable, scalable crash-prevention applications.]]></description>
      <pubDate>Fri, 06 Jun 2025 14:55:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2562267</guid>
    </item>
    <item>
      <title>Managing and Sharing Traffic Management Systems Video

</title>
      <link>https://rip.trb.org/View/2558409</link>
      <description><![CDATA[Traffic management systems (TMSs), which integrate advanced technologies, software, and data, are essential tools for enhancing the safety, efficiency, and reliability of surface transportation. These systems play a vital role in helping agencies meet the growing and evolving mobility needs of travelers, service providers, partner agencies, and the general public.

Traditionally, TMSs provided only static images of roadway conditions, but technological advancements have transformed this practice into 24/7 live-streaming video feeds of traffic conditions. Increasingly, individuals and private companies are capturing, scraping, or archiving these video feeds, and often repackaging and selling the data to public or private customers, raising legal, technical, and operational challenges for transportation agencies.

Most TMSs do not record or archive video feeds due to concerns over legal obligations and public information requests, risks of releasing sensitive or personally identifiable information (PII), potential liability from unintended uses, and technical burdens of video management. The rising expenses of data storage and telecommunications add complexity to video management.

Research is needed to help agencies evaluate the implications, benefits, and risks of sharing TMS video.

The objective of this research is to develop a guide for transportation agencies on managing and sharing access to TMS video. The research will identify current practices, challenges, unintended consequences, and opportunities for improvement.]]></description>
      <pubDate>Tue, 27 May 2025 20:58:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558409</guid>
    </item>
    <item>
      <title>A Diffusion Model for Generating Safety-Critical Rural Driving Video Data</title>
      <link>https://rip.trb.org/View/2556692</link>
      <description><![CDATA[In the United States, about 20% of the population lives in rural areas covering 97% of the land. Due to the lower density of population in rural areas, auto, which consists of car, SUB, pickup truck, and rental car, is still the dominating transportation mode there. Statistics further shows that 40% of fatalities occurred in rural areas although only 31% of the total vehicle miles of travel (VMT) there. Consequently, the fatalities rate per 100 million VMT in rural areas is 1.5 times of that in urban areas .
Addressing the traffic safety concern is among the required efforts to provide rural communities with access to resources via transportation. Autonomous vehicle technologies can be effective in reducing crash rates in rural areas, especially for vulnerable users such as senior drivers. In the current stage, level 2 self-driving technologies are becoming more mature and affordable than before, rapidly diffusing in the market. Level 2 self-driving vehicles are equipped with some Advanced Driving Assistance Systems (ADAS) that can control both the steering and acceleration/deceleration of vehicles, but drivers still need to remain engaged and be ready to take over the control at any time. Adaptive cruise control, lane keeping, and lane centering are representative ADAS features. Given that 66% of rural fatalities were in roadway-departure crashes, those ADAS features should be helpful in reducing fatalities in rural roads.
Self-driving automobile manufacturers tend to prioritize densely populated urban centers where there is a higher demand for transportation solutions and greater potential for profitability. By targeting urban markets, car manufacturers aim to capitalize on the immediate and foreseeable opportunities presented by urban mobility needs. As the market is not oriented toward rural areas where only 20% of the population lives, Level-2 self-driving technologies are biased toward urban driving contexts, not sufficiently adapting to rural areas. For example, the perception module in ADAS is an integration of sensors and machine perception models. Training perception models require labeled sensory data about driving scenes. Acquiring training data about rural driving scenarios is an expensive investment, considering that 68% of the nation’s lane-miles are in rural areas. With insufficient training data collected from rural areas, performance of Level-2 self-driving vehicles, such as safety, comfort, and energy efficiency, have not reached satisfying levels there. How to obtain training and testing data about driving in rural areas in a cost-effective manner has been an urgent need for Level-2 self-driving technologies.
In this project, a machine learning model that can generate synthetic driving video data in a cost-effective manner for leveraging up the safety of Level-2 self-driving technologies in rural areas will be developed. Particularly, the project focuses on generating data of the driving environment where the imperfect natural environment and/or transportation infrastructure fail the current Level-2 systems. Examples of such driving scenarios include suddenly encountering fast-moving, wide animals or livestock at dawn and dusk, roads with deteriorated or temporarily removed/occluded lane markings, and others.
]]></description>
      <pubDate>Wed, 21 May 2025 12:31:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2556692</guid>
    </item>
    <item>
      <title>Develop Work Zone Training Video for Law Enforcement</title>
      <link>https://rip.trb.org/View/2505746</link>
      <description><![CDATA[This project will develop a work zone training video script focused on law enforcement officers who interact with work zones. The support, input, and content development from 
North Carolina Department of Transportation (NCDOT) will be essential for successfully completing this effort. NCDOT is currently compiling research on existing training related to this topic, including sources such as ATSSA, Federal Highway Administration, etc. and will share this information with ITRE to use for the development of the training that will improve safety for law enforcement in work zones. Law enforcement officers are commonly in work zones to respond to an incident, conduct targeted enforcement, provide traffic management support through a contract with a construction project, or flagging at a signalized intersection. This training will target the most common and active work zone involvement which is when law enforcement officers are contracted to support a construction project and as a secondary focus, for flagging activities. Essential content will include requirements for work zones and best practices for safe operations in work zones. This research will culminate in two deliverables: (1) a pre/post survey to evaluate the outcomes of the training and (2) a 10 to 15 minute script (for production by NCDOT Communications).]]></description>
      <pubDate>Tue, 04 Feb 2025 08:58:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505746</guid>
    </item>
    <item>
      <title>An Updated Capacity Model for Mini-Roundabouts</title>
      <link>https://rip.trb.org/View/2452918</link>
      <description><![CDATA[Mini-roundabouts, characterized by their fully traversable central islands, provide a compact and efficient alternative to traditional single-lane roundabouts. They are particularly beneficial in areas with spatial constraints, where larger roundabouts would necessitate unwanted right-of-way impacts. The typical inscribed circle diameter (ICD) of these mini-roundabouts is often under 90 feet, and they are best suited for areas where speeds are limited to 30 mph or lower.

One of the primary advantages of mini-roundabouts is their smaller footprint, making them an effective replacement for stop signs or signal controls at intersections with moderate traffic volumes. Their traversable central island design is pivotal, offering adaptability in mixed traffic scenarios, especially facilitating the movement of larger vehicles.

However, there are aspects that require further scrutiny. In the early 2010s, the Federal Highway Administration (FHWA) developed capacity models for mini-roundabouts with two different ICDs. These models assumed that such roundabouts would function as a series of independent T-intersections, an assumption that may not always be accurate, especially in the presence of larger vehicles. When comparing the capacities of mini-roundabouts with all-way stop-controlled (AWSC) intersections, it is vital to understand their potential advantages and shortcomings. If mini-roundabouts do not significantly exceed the capacity of AWSC intersections, their unique positioning in traffic management might be challenged, especially when considering the cost-effectiveness of AWSC intersections.

The main objective of this research is to develop new capacity models for mini-roundabouts based on field data collected at 25 mini-roundabouts in North Carolina and other states within the midatantic and southeast regions. Video data will be recorded at all sites from 25-30 ft elevation. The videos will be analyzed using the DataFromSky (DFS) service, which the team successfully utilized in previous NCDOT projects. Vehicle trajectories will be obtained and analyzed to estimate key capacity parameters, including the critical and follow-up headways and the effect of heavy vehicles. The team will utilize a calibrated microsimulation model only to fill out gaps when field data are not available.]]></description>
      <pubDate>Fri, 15 Nov 2024 16:06:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2452918</guid>
    </item>
    <item>
      <title>Communicating the Impacts of Research Projects in
North Carolina</title>
      <link>https://rip.trb.org/View/2452917</link>
      <description><![CDATA[The North Carolina Department of Transportation (NCDOT) Research & Development Unit’s research projects are multifaceted – focusing on safety, materials, operations, planning, mobility, human and natural environments, and more – and have a real impact on people and communities across the state. It’s imperative that the story of the value of these projects is told, and heard, by many. This goes beyond needing to understand how tax dollars are being spent; how can North Carolinians appreciate how NCDOT research directly benefits them when they use the state’s transportation system?

This request for proposal indicates that NCDOT prioritizes communicating the value of its research, and the UNC Highway Safety Research Center (HSRC) is excited to submit this proposal to lead an effort to launch an engaging, public-facing video series to do just that.

The Research Team will work closely with the NCDOT Research & Development Unit; Public Involvement, Community Studies & Visualization office; and Communications Office, to promote the use and impacts of NCDOT research projects and products through an informative video series, tentatively titled “NCDOT Research Reels.” These short videos will promote the results of completed NCDOT research projects, explain how these products can or have been applied, as well as promote the benefits of these products results to potential users. The Research team will conduct all steps of the video production process, creating up to twelve succinct videos showcasing different key projects and areas of NCDOT’s research portfolio with interviews, on-site footage of research implementation, discussions with community members, narration and editing, and distribution.

HSRC will conduct all steps of the video production process, creating up to twelve succinct videos showcasing different key projects and areas of NCDOT’s research portfolio with interviews, on-site footage of research implementation, discussions with community members, narration and editing, and distribution.

HSRC has a long history of working with NCDOT on a variety of projects designed to improve the state’s roadway system and engage communities across the state. HSRC's team of communications and design professionals is uniquely positioned for this type of video outreach work. With well-written scripts, knowledge of the field, and great understanding of the importance of all the different users of a transportation system, HSRC can create impactful and digestible videos for the NCDOT Research Reels project. HSRC also has a legacy of creating impactful videos that tell a wide variety of transportation stories; key examples include the HSRC Insights series, the video “Low-Cost Pedestrian Safety Zones,” and a highlight reel for the U.S. Department of Transportation University Transportation Center managed by HSRC. Said simply, research depth, technical expertise, and practical experience qualifies HSRC for success for this proposed work.

Thank you for this opportunity to express HSRC’s interest in managing the “Communicating the Impacts of Research Projects in North Carolina” video series.]]></description>
      <pubDate>Fri, 15 Nov 2024 15:55:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2452917</guid>
    </item>
    <item>
      <title>Development of Manual of Procedure Training Videos for Highway Technicians 
</title>
      <link>https://rip.trb.org/View/2431339</link>
      <description><![CDATA[The goal of this research is to develop technology transfer reference tools for Ohio Department of Transportation (ODOT) construction and maintenance crews on key job duties included in the Construction and Materials Specifications Manual, Construction Administration Manual of Procedures, and Maintenance Operations Manual. The primary focus would be on identifying or creating job aids (e.g., short tutorial videos) demonstrating activities performed by Subject Matter Experts. These aids could be used to help train new employees or serve as reminders for seasoned staff that have not recently performed those duties. It may also help encourage consistency in how routine activities are performed.  The Research Advisory Board (RAB) recognized the importance of having a variety of tools readily available to train ODOT's workforce. While information on requirements and guidance is available in written manuals, the methods in which individuals consume and retain this knowledge differs greatly. As more and more staff retire, ODOT is losing a wealth of knowledge. Developing a video library to augment training classes will help educate ODOT's workforce and develop experts in the field. This research is expected to be the first step in this process by identifying the key job duties to focus on, creating a process for identifying existing reference tools or developing new ones, and developing the first series of reference tools. This research project is not anticipated to create these tools for all of ODOT's manuals but serve as the framework for ODOT to use.               ]]></description>
      <pubDate>Tue, 17 Sep 2024 11:21:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431339</guid>
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