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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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      <title>Deskilling or Redefining? Drivers’ Hazard Anticipation with Automation</title>
      <link>https://rip.trb.org/View/2643018</link>
      <description><![CDATA[Hazard anticipation is a critical skill for crash avoidance, requiring drivers to detect, predict, and respond to potential roadway threats. As Advanced Driver Assistance Systems (ADAS) become more common, drivers increasingly shift from active vehicle control to supervisory roles, which may change visual scanning patterns, situational awareness, and response strategies. These changes are not yet fully understood and may have important safety implications.

This project investigates how automation affects hazard anticipation by examining driver gaze behavior, responses to traditional roadway hazards, and reactions to system-related cues in ADAS-equipped vehicles. The research integrates secondary data analysis, development of a hazard anticipation taxonomy, and experimental testing using a high-fidelity driving simulator. Results will be used to identify automation-related changes in hazard anticipation and to develop targeted training approaches aimed at maintaining effective driver oversight. Findings from this work will support safer human interaction with automated vehicle technologies as their use continues to expand.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:13:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643018</guid>
    </item>
    <item>
      <title>Gaze-directed UAV-UGV Coordination Framework for Onsite Quality Inspection of Precast Bridge Construction</title>
      <link>https://rip.trb.org/View/2314010</link>
      <description><![CDATA[Precast bridge components such as girders, decks, and columns facilitate accelerated bridge construction while offering improved construction quality due to the high quality control standards at the offsite precast plants. In the offsite precast plants, components need to go through rigorous dimensional and surface quality inspection, after which they are transported to the jobsite for final assembly. Contrastively, onsite quality inspection, which still largely relies on manual visual inspection on limited samples, is yet to match up with the standards of the offsite practices. Onsite quality inspection for precast bridge construction is crucial due to potential defects after the offsite construction phase. For example, damage and defects may occur during the component transportation process. The quality of onsite construction activities such as connection joint sealing, post-poured wet joints, and component localization and alignment, also significantly affect the overall structural integrity and durability. Recently, many sensing systems, such as laser scanning and vision-based systems have been developed for quality inspection of precast components. Most efforts have been dedicated to creating new data processing and analysis algorithms to improve accuracy, with very limited focus on improving the efficiency and accuracy of the data collection process using automated technology. There is a critical need to develop a robot-assisted platform to improve the efficiency and coverage of data collection and inspection for quality assurance/quality control (QA/QC) of onsite precast bridge construction. The objective of this project is to develop a novel gaze-directed unmanned aerial vehicle (UAV)-unmanned ground vehicle (UGV) coordination framework for onsite quality inspection of precast bridge construction. Specifically, UAV will provide global coverage for inspectors to quickly identify the components and construction activities for inspection while UGV will navigate to specific locations for close inspection following human guidance. A new gaze-directed human-machine interface will be developed, where inspectors can express their guidance via natural gaze movements, to reduce worker mental load. By establishing a new multi-robot-human coordination framework with natural and intuitive interactions, this project will develop an efficient and automated infrastructure inspection approach, thus improving the quality and durability and eventually extending the life of precast transportation infrastructure.]]></description>
      <pubDate>Sun, 24 Dec 2023 08:41:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2314010</guid>
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      <title>Rearview Video System Training for Older Drivers</title>
      <link>https://rip.trb.org/View/2042311</link>
      <description><![CDATA[The project will assess the effectiveness of the Rearview Video Systems (RVS) training video developed as a part of a previous project (Older Driver Rearview Video Systems) in improving safe backing performance of older drivers. Participants will include ‘young-old’ (60 participants age 60-69) and ‘old-old’ (60 participants age 70+) drivers, in a controlled field study on a closed course. Participants are to be active drivers with little or no experience using an RVS. Half of the participants from each age group will be assigned to a training group that views the RVS training video. The remainder, assigned to the control group, will view a similar-length traffic safety video unrelated to backing or RVS use. Sex equity will be considered by ensuring similar portions of males and females in all four groups. In addition, the plan will recruit participants from a wide cross-section of the population in the study area to ensure people of various demographics have equal opportunity to volunteer to participate. The study’s key research question is "to what extent does viewing an RVS training video that provides instruction on proper use of RVS that address errors observed in participants in the previous Older Driver Rearview Video Systems study improve older drivers’ backing performance when using an RVS?" The study will compare measures of backing performance (e.g., errors, contacts with obstacles) and of eye glance measures (e.g., the RVS, mirrors, or over their shoulder) during backing of the training and control groups. The results will be distributed to the public in a final report. If promising, the training video also will be distributed to the public. ]]></description>
      <pubDate>Fri, 14 Oct 2022 16:17:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2042311</guid>
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      <title>Driver and Pedestrian Behavior at Crosswalks in Modern Roundabouts using Virtual Reality</title>
      <link>https://rip.trb.org/View/1853638</link>
      <description><![CDATA[In the United States there are about 7,100 modern roundabouts (Kittelson & Associates, 2020). Roundabouts are generally associated with a reduction in crashes, but pedestrians face crossing difficulties at multi-lane roundabouts and at high volume locations (NASEM, 2010). De Brabander and Vereeck (2007) estimated a 28% increase in injury crashes with vulnerable road users (VRU) on roundabouts when replacing conventional intersections on 30-mph speed limit roads. Positive safety results for pedestrians on single-lane roundabouts and about the same safety level on multi-lane roundabouts as in other intersection types have been reported (Grana, 2011). Roadway lighting, speed limits over 25 mph, traffic controls other than a traffic signal, vehicle type, and pedestrian’s age, impairment, or action, are aspects found to increase pedestrian crash severity on roundabouts (Thomas et al., 2018). Virtual Reality (VR) is an emerging technology used in transportation research to address pedestrian safety issues.
The objective of this research project is to use Virtual Reality (VR) simulation equipment to analyze the behavior of drivers and pedestrians when approaching pedestrian crossing maneuvers on roundabouts. The simulation experiment will be designed in two phases. The first simulation phase will be related to the drivers and the second phase will be related to the pedestrians. Driver behavior will be recorded in the simulator when approaching the entry and exit crosswalks at a roundabout. Head and eye movements of the drivers in the simulator will be tracked to analyze their attention and gaze reactions. The drivers’ eye movements will be used to assess the visibility of pedestrians, as well as the driver reactions when interacting with the pedestrians on the roundabout approaches. The recorded speeds of drivers and their crosswalk yield rates from this experiment will be used to produce traffic behavior on the pedestrian scenarios in the second phase. The VR study in the second phase will analyze how pedestrians make the decision to cross at a roundabout approach when facing different traffic speeds and the location of the crossing. Walking speeds, time gap taken, and crossing success rates will be measured from the VR simulation study.]]></description>
      <pubDate>Mon, 24 May 2021 11:30:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1853638</guid>
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    <item>
      <title>Work Zone Safety III: Calibration of Safety Notifications through Reinforcement Learning and Eye Tracking</title>
      <link>https://rip.trb.org/View/1844340</link>
      <description><![CDATA[Despite increased regulations, restrictive measures, and devices used for warnings, work zone injuries and fatalities are still observed at highway construction projects with alarms/notifications being ignored. With a vision to reduce the number of injuries and fatalities, Phase 3 of the research team's worker safety project extends the original scope and adds two new main components, including the addition of eye-tracking for identifying worker attention under dangerous situations and a reinforcement learning model used to optimally send alarms to workers to maximize their attentions along with wide deployment and demonstration of the team's previous C2Smart research effort. This phase of the project aims to make sense of the biometric sensor data (i.e., heart rate and pupil movements while workers omit or accept safety notifications) through state-of-the-art reinforcement learning approaches. The outcomes of this research will bring an understanding to the unknowns of worker behaviors on why they decide to ignore/accept notifications for calibration of when and at what frequency to send notifications to workers for a better acceptance rate. Key questions this research answers are, at what conditions workers ignore/response to warnings at work zones? How we can calibrate notification systems for getting responsive actions from workers? What are the modalities, frequencies, and timings of pushing notifications in these calibrated systems? Through wearable sensors, hardware integrated realistic representations of work zones in virtual reality and eye tracking, in this phase of the project the team will widely have pilot demonstrations of the integrated platform to collect worker behavioral and biometric (heart rate, eye-tracking) responses to alarms/warnings/notifications issued under realistic scenarios and modalities of warning mechanisms (e.g., sensory, visual, audial) that were developed in earlier phases of this project, and mine these captured data towards understanding human behaviors in response to modalities of notifications. ]]></description>
      <pubDate>Thu, 01 Apr 2021 20:02:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844340</guid>
    </item>
    <item>
      <title>Safe by Design: Collecting Traveler Centric Data to Inform Safe Street Design</title>
      <link>https://rip.trb.org/View/1758049</link>
      <description><![CDATA[Studying Visual Experience to Inform Infrastructure Design Streets are designed for vehicle efficiency. Engineers reference vehicle-based codes, use detailed models to optimize vehicle flow, and conduct evaluations with driving simulators before implementing design. A constraint, rather than a design variable, is pedestrian and cyclist safety, as roadway designers currently lack the ability to quantify and test safety prior to construction. Collecting this missing data is at best challenging and expensive, and at worst, unsafe. Professor Megan Ryerson fills this gap by using eye tracking technology to collect and study the user-based data and perspective towards transforming how roadway designers understand, measure, and implement safety interventions.]]></description>
      <pubDate>Wed, 16 Dec 2020 14:40:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1758049</guid>
    </item>
    <item>
      <title>Quantifying the Impacts of Situational Visual Clutter on Driving Performance Using Video Analysis and Eye Tracking</title>
      <link>https://rip.trb.org/View/1593718</link>
      <description><![CDATA[Visual clutter and its impact on driving performance have been widely acknowledged. Visual clutter has been taxonomically categorized into three types, including 1) the “situational clutter” that is sourced from the interaction among the driver, the vehicle, other road users, and the road infrastructure; 2) the “designed clutter” that is sourced from the existing traffic control devices, e.g., signage, signal, work zone, etc., and 3) the “built clutter” that is sourced from other roadside and roadway objects, e.g., billboard, roadside landscapes, etc. The impacts of both the designed clutter and the built clutter have been investigated using both naturalistic driving measures (including driving clips, vehicle status, etc.) and driving simulator measures (including driving clips, vehicle status, eye tracking measures, etc.). Unfortunately, the situational clutter remains an open question, although such a clutter type is considered to play a more lasting and profound role in impacting the driver’s performance.
The challenges in investigating the situational clutter are sourced from its complicated constitution of different contributors (e.g., vehicle, other road users, the road infrastructures, etc.) and its dynamically changing manner (e.g., dashboard display, traffic conditions and outlooks of the vehicles, dynamic road, and roadside landscapes, etc.). Although the psychology and cognitive science communities have investigated the situational visual clutter, there lacks effort in studying it in the driving context. The proposed study aims to address such a gap. 
The objective of this proposed study is threefold: 1) to develop a new situational visual clutter model that objectively quantify the complex and dynamic driving scene based on eye tracking and video analysis; 2) to employ the developed model, and to quantify impact of the situational visual clutter on driving performance under an information searching scenario and a driving distraction scenario using driving simulation; and 3) to investigate the potential of employing the driving scene quantification to support other retrospective studies and data mining using the existing driving simulation data. 
]]></description>
      <pubDate>Wed, 20 Mar 2019 09:29:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/1593718</guid>
    </item>
    <item>
      <title>Using Simulation to Study Communication between Autonomous Vehicles and Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/1593712</link>
      <description><![CDATA[Autonomous vehicle (AV) technology is advancing at a rapid pace. One concern that has come to the forefront is how AVs will communicate and interact with vulnerable road users such as pedestrians and bicyclists. The goal of this project is to use a pedestrian simulator to examine how pedestrians learn to respond to visual cues about whether AVs acknowledge them at a crosswalk and intend to yield to them, in both daytime and nighttime conditions. The research team will use a cue-learning approach in which participants learn through experience about AV cues signaling awareness and intention. The task for participants is to stand at the edge of a crosswalk at a 4-way stop and cross without colliding with a vehicle. The team will use a between-participants design to examine how pedestrians respond to cues from autonomous vehicles. In the gaze-directing condition, the vehicle “eyes” will direct their gaze toward the participant to signal acknowledgement. In the attention-getting condition, the vehicle “eyes” will flash on and off to signal acknowledgement. The team expects that it will be easier for pedestrians to learn about the meaning of cues in the gaze-directing condition because the gaze-directing cues mimic eye contact between real drivers and pedestrians and the attention-getting cues do not clearly communicate “go” to the pedestrian. This research will fill a critical gap in understanding of communication between autonomous vehicles and vulnerable road users that can be used to increase pedestrian safety and inform vehicle design.]]></description>
      <pubDate>Wed, 20 Mar 2019 09:15:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/1593712</guid>
    </item>
    <item>
      <title>Driver Behavioral Situational Awareness System (DB-SAM)
</title>
      <link>https://rip.trb.org/View/1370016</link>
      <description><![CDATA[The research will attempt to automatically classify certain aspects of driver state (such as fatigue) or distraction due to a conversation using a handheld device (such as a cell phone). This will be done in part by continuously estimating the facial head pose of a driver to see if the driver is paying attention to the road and his/her surroundings. Other facial cues, such as mouth movement and eye movement, as well as whether the driver has both hands on the steering wheel, will be used to determine driver attentiveness. The proposed system will automatically detect “soft” biometric information about the driver, such as age, gender, ethnicity, glasses, etc.
]]></description>
      <pubDate>Wed, 23 Sep 2015 09:44:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1370016</guid>
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