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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.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>
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      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Investigating Driver Behavior Under Cyberattacks in Connected Vehicle Environments: Phase II</title>
      <link>https://rip.trb.org/View/2696964</link>
      <description><![CDATA[Phase II will examine driver behavior and decision-making under cyberattacks in connected-vehicle contexts using high-fidelity, human-in-the-loop driving simulators at Morgan State University (urban) and Clemson University (suburban). The team will develop reusable Cyberattack Injection Modules (CIMs) for UCWinRoads and SimCreator and validate one vehicle-centered attack (false blind spot warning) and two infrastructure-centered attacks (falsified signal phase-and-timing and “phantom” signal-ahead information). The study will leverage IRB-approved human-subject testing, conduct a Maryland MVA pilot demonstration, and curate a Standardized Attack Scenario Library (ASL) for replication and training use.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696964</guid>
    </item>
    <item>
      <title>Incident Management Coordinator Vehicle Red Lights Completion Project</title>
      <link>https://rip.trb.org/View/2622012</link>
      <description><![CDATA[In 2022, the Virginia Department of Transportation (VDOT) gained approval to install flashing red lights on certain incident management coordinator (IMC) vehicles. Previously, only amber lights were permitted. In a prior phase of this project, the Virginia Tech Transportation Institute identified IMC vehicles that were candidates for inclusion and worked with district operations personnel to install data acquisition system (DAS) systems. A total of eight IMC vehicles were instrumented, including two from each of the Salem, Northern Virginia, Hampton Roads, and Richmond districts. Unanticipated delays with the red lights training program and the final procurement and physical installation of red lights delayed final approval to use the red lights in actual operations, so only baseline data were collected in that initial phase of work. VDOT recently announced that red light operations would begin in November 2025, so a 2nd phase of this research will be conducted to determine how red lights impact operations of IMCs. Specifically, the research will seek to answer five major research questions: (1) What is the impact of red lights on incident response times? (2) What is the impact of red lights on interactions with other vehicles while enroute to incident? (3) What is the impact of red lights on compliance with Standard Operating Procedures while enroute to incident? (4) What is the impact on overall IMC driving performance and safety when red lights are used? (5) Do red lights have impact on passing driver’s approach and Move Over Law behavior compliance observed when parked on shoulder? Data collected during this 2nd phase of work with red lights in operation will be compared to the prior baseline data collection to assess any impacts of the red lights.]]></description>
      <pubDate>Tue, 11 Nov 2025 06:59:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2622012</guid>
    </item>
    <item>
      <title>Telematics Technology and Distracted Driving </title>
      <link>https://rip.trb.org/View/2447243</link>
      <description><![CDATA[The current project aims to develop an understanding of how smartphone-based telematics technologies can be used to measure information on individual driver behavior and performance in general and in terms of distracted driving.  This study will help the 
National Highway Traffic Safety Administration (NHTSA) understand the limitations of this kind of data for research data collection in comparison to other kinds of data collection such as vehicle-based systems.  This study will also look at age and sex differences in a group of drivers in terms of distracted driving and other risky driving behaviors, with a particular view of how the data collection systems differ.  This project will involve a literature review and scan of technologies that can be used for studying data collection of driver behavior and distracted driving engagement.  Next, the project will focus on different forms of data collection, e.g., focusing on NDS DAS and smartphone-based telematic apps in terms of accuracy at providing information, resources required for utilizing each technology, and ease of use and distribution (Study 1). Last, the project will involve data collection and analyses using these kinds of technologies in terms of general driving and distracted driving engagement according to the identified forms of technology (Study 1) and according to participant sex and age, which will be analyzed using the current project’s data sets (Study 2).  The intent of this project is to create two (2) final reports, one for each study, a journal article, and data sets from each study and the raw data sets that can be analyzed in the future. This project will support NHTSA’s efforts to study distracted driving engagement, giving the agency information regarding what measurement techniques are effective for different forms of distracted driving, and behavior, in general, as well as providing the agency with prevalence estimates for distracted driving engagement.

]]></description>
      <pubDate>Fri, 01 Nov 2024 12:24:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447243</guid>
    </item>
    <item>
      <title>Evaluation of the Impact of App-based Feedback and Monetary Incentives on Teen Driver Safety (extension)</title>
      <link>https://rip.trb.org/View/2244370</link>
      <description><![CDATA[Teen drivers will participate in a six-month driving study that will yield comprehensive data on several driving performance measures. This data will provide the basis for driving feedback that will be provided to teens and their parents throughout the study. The study will consist of 4 experimental phases: Pre-feedback baseline (1 month), website feedback only (2 months), website feedback + monetary incentives (2 months), and post feedback baseline (1 month). Driving feedback will be available on a website that is accessible via mobile device. In addition to the provision of app-based feedback, participants will receive monetary incentives based on safe driving performance. The effectiveness of both the feedback app, and monetary incentives will be measured by examining teen driver safety (e.g., kinematic risky driving events, speeding, etc.). The usability and acceptability of this feedback will also be measured by analyzing the number of website logins and duration of time spent on the website. Participant intake and exit questionnaires will also be conducted to obtain qualitative data on user preferences and acceptability. Focus groups with both parents and teens regarding the feedback and monetary incentives will be conducted and the data analyzed. ]]></description>
      <pubDate>Wed, 13 Sep 2023 13:24:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2244370</guid>
    </item>
    <item>
      <title>Mapping comprehension of ADAS across different driving and road user populations</title>
      <link>https://rip.trb.org/View/2189263</link>
      <description><![CDATA[While there is a significant percentage of the general population that has interacted with or heard of advanced vehicle technologies, there is still a large percentage of the population that is unsure exactly how these systems work (McDonald et al., 2016). Previous research conducted for the AAA Foundation for Traffic Safety has shown that many drivers have incomplete, and, in some cases, very inaccurate understanding of the intended function, capabilities, and limitations of these technologies and the level of understanding varies across different types of road users (McDonald, Carney & McGehee, 2018; Gaspar & Carney, 2019; Gaspar, Carney, Shull & Horrey, 2021). More recent work has identified a subset of drivers who not only exhibit a poor mental model but exhibit a level of confidence in their understanding that does not match their level of understanding (Carney, Gaspar, Roe & Horrey, under review; Lenneman, Mangus, Jenness & Petraglia, 2020). This misalignment between confidence and understanding may lead to unsafe driving behaviors.]]></description>
      <pubDate>Wed, 31 May 2023 19:34:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2189263</guid>
    </item>
    <item>
      <title>Behavioral Investigation of Temporary and Permanent Pedestrian Infrastructure</title>
      <link>https://rip.trb.org/View/2188168</link>
      <description><![CDATA[Agencies may treat pedestrian crossing sites initially with temporary treatments (i.e., removable flexible delineators or bollards) to test efficacy, but the pedestrian safety effects of temporary infrastructure are not well understood. This study tested the impact of temporary and permanent pedestrian infrastructure such as curb extensions/bump-outs and pedestrian medians/refuges as it relates to pedestrian and driver behavior. Twelve intersections in Minneapolis and Saint Paul, MN, comprising a mixture of intersection traffic control types and pedestrian infrastructure were examined before and after installation of the infrastructure. Staged pedestrian crossings by research staff at crosswalks examined driver stopping behavior and analyzed video data from installed traffic cameras to examine natural pedestrian behavior. A computer algorithm processed the video data to estimate vehicle speeds at the sites. For signalized intersections, the use of flexible delineators as a curb extension led to a reduced likelihood of drivers stopping for pedestrians during staged crossings. Little to no effect was found for permanent curb extensions at signalized intersections, perhaps due to the role that increased physical separation and visual complexity plays in the likelihood of drivers stopping for pedestrians. For unsignalized intersections, both temporary and permanent curb extensions were found to have similar and mixed effects for pedestrian safety when considering average driver speed, stopping likelihood, and pedestrian behavior. Both permanent and temporary medians had generally positive effects for the pedestrian safety measures, especially for average driver speed. Future research should closely examine the relative pedestrian safety impacts of temporary and permanent refuges at signalized intersections.]]></description>
      <pubDate>Tue, 30 May 2023 13:42:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2188168</guid>
    </item>
    <item>
      <title>The Impact of Co-Administration of Alcohol and Cannabis on Impairment</title>
      <link>https://rip.trb.org/View/2083613</link>
      <description><![CDATA[It is the intent of the FAA Functional Genomics Team to analyze samples and data from the project to research molecular biomarkers associated with cannabis use, alone and concomitant with alcohol consumption.  This may include biomarkers associated with cognitive performance.

In this study subjects will undergo screening and then will complete 7 double-blind, double-dummy outpatient sessions in randomized order. In each session, participants will self-administer placebo (0 mg THC) or active oral cannabis (10 or 25 mg THC, via a chocolate brownie) and a placebo drink (BAC 0.0%) or alcohol drink calculated to produce a breath alcohol concentration (BAC) of 0.05%. Participants will also complete a positive control session with placebo cannabis and alcohol at a target BAC of 0.08%.

As this is a biomarker discovery project, it is expected there will be multiple computational analyses performed to assess factors that may include molecular and physiological changes among the condition groups, differences in cognitive and driving performance tests, and differences associated with screening and other data collected. A hypothesis is that there are genetic biomarkers that can be used to predict cognitive and/or driving simulator performance deficits due to cannabis and alcohol consumption, and the FAA Functional Genomics Team anticipates testing this via bioinformatics analyses of gene expression changes. 

For the FAA Functional Genomics Team, a key objective is to provide foundational knowledge that could be later validated and applied to improve the ability to detect risks to safe operations and performance associated with cannabis and alcohol consumption. A key aim is to provide knowledge that improves aviation/transportation safety.]]></description>
      <pubDate>Mon, 12 Dec 2022 09:23:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2083613</guid>
    </item>
    <item>
      <title>ADS On Road Evaluation Methods for Medium and Heavy Trucks (Approved in 2020)</title>
      <link>https://rip.trb.org/View/2050304</link>
      <description><![CDATA[The primary objectives of this project are to develop, test, and validate a methodology for evaluating driving performance of a heavy truck (whether by a human or an automated driving system) during on-road operations.]]></description>
      <pubDate>Tue, 25 Oct 2022 10:24:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2050304</guid>
    </item>
    <item>
      <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>
    </item>
    <item>
      <title>Development of a Monitoring System for Driver Readiness in Prolonged Automated Driving</title>
      <link>https://rip.trb.org/View/2008004</link>
      <description><![CDATA[Vehicle automation technology is being designed to handle driving tasks for human drivers. However, this technology is not expected to handle all possible driving conditions successfully in the foreseeable future. The system can fail anytime and may require drivers to take over control within a short period 
of time. Additionally, automation can induce boredom, daydreaming, and drowsiness due to driver inactivity and can worsen driver readiness to take over control of the vehicle. Driver readiness can be measured using their postural data, gaze behaviors, and emotional expressions. Specific thresholds for these measures can be used to alert drivers to be ready to take over from automated driving or avert them from driving, when necessary. This proposal aims to develop a driver readiness monitoring system to improve their takeover performance using a driving simulator study. The objectives are to identify effective measures to define driver readiness and assess the association between divers’ takeover performance and their readiness measures. Researchers will measure (i) driver readiness using postural data, gaze and head orientations, and emotional status from video recordings analyzed with Face Reader software; (ii) drivers’ categorical subjective responses on readiness; and (iii) takeover 
performance using reaction time, collision rate, and driving behavior. Different machine learning algorithms will be applied for (i) feature extraction, (ii) feature selection, and (iii) developing classification and prediction models for readiness monitoring. The rationales for this research are to: (a) inform policy makers about an effective driver-readiness monitoring system for prolonged automated driving; (b) provide safer driving conditions and address equity during prolonged driving for older adults, and occupational drivers (Uber, taxi, or city transportation) driving long hours; and (c) enhance educational and research infrastructures combining human-computer interactions and machine learning. Stakeholder involvement from the city, state, and automobile manufacturer will strengthen our tech-transfer and project outcome dissemination.
]]></description>
      <pubDate>Tue, 16 Aug 2022 18:15:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2008004</guid>
    </item>
    <item>
      <title>Incorporating Driver Behavior and Characteristics into Safety Prediction Methods</title>
      <link>https://rip.trb.org/View/1996243</link>
      <description><![CDATA[Driver behavior and characteristics are influential contributing factors to traffic crashes, but current safety analysis tools primarily incorporate infrastructure-related factors affecting crashes. The lack of behavior and characteristic information can create a problem when considering safety applications since some of the most important factors are not included. This could lead to safety solutions that may not work as well as intended. In the AASHTO Highway Safety Manual (HSM), the measures of driver characteristics are divided into several categories such as attention and information processing, vision, perception-reaction time, and speed choice. However, these characteristics are provided at a very high level. Police officers usually report driver characteristics such as gender, age, speeding, blood alcohol content, seat belt use, and distracted driving. While several studies have evaluated the impact of these factors on crash severity, there is a need to incorporate these factors in crash prediction methods to achieve a better picture of the true potential effects on crash severity and frequency for decisions in planning, design, and operations. Research is needed to develop a methodology to incorporate a variety of factors related to driver behavior and characteristics into crash prediction methods to allow for a more comprehensive assessment of existing and expected safety performance, for use in design and operational decision-making, and incorporation into the AASHTO HSM and other safety tools and guidelines.

The objective of this research is to continue and complete the work begun under NCHRP Project 22-47 to develop a methodology to incorporate driver characteristics and behavior into safety prediction methods to estimate the expected crash frequency and severity related to infrastructure features for use in planning, design, and operational decisions.]]></description>
      <pubDate>Tue, 19 Jul 2022 14:24:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/1996243</guid>
    </item>
    <item>
      <title>Teen Driving Performance Associated with Distraction, ADHD, and Other Risk Factors



</title>
      <link>https://rip.trb.org/View/1996244</link>
      <description><![CDATA[BTSCRP Research Report 15: provides insights into traffic safety risks for teen drivers with different levels of attention-deficit/hyperactivity disorder (ADHD) screen scores. The report uses naturalistic driving data to assess the incidence of eyes off road (EOR) and crash risk for teen drivers with and without ADHD. The report concludes that teen drivers with ADHD tend to look away from the road more frequently and may be at an elevated risk for missed hazards. This report will be of interest to state highway safety offices (SHSOs) and other stakeholders concerned with young-driver safety.

Young drivers with neurodevelopmental disabilities may be at more risk for motor vehicle crashes due to behavior characteristics commonly associated with these conditions. In recent years, a growing body of research has examined driving risks for teens with autism and those with ADHD. Research has identified concerns about the driving skills of teenagers with ADHD, as well as their increased tendencies to become distracted while driving and to drive at higher speeds. Determining the role of distracted driving in crashes is difficult and inexact for many reasons, including a general lack of evidence. 

Under BTSCRP Project BTS-28, “Teen Driving Performance Associated with Distraction, ADHD, and Other Risk Factors,” Virginia Polytechnic Institute and State University was asked to (1) gauge the association between confirmed instances of distracting behaviors and inattention to the driving task by teen drivers with crash and near-crash (CNC) involvement, in relation to their incidence during baseline events; (2) determine whether these instances contribute to CNCs, and if and how these relationships change with increasing driving experience; and (3) compare exposure-based CNC involvement rates, and self-reported risky driving behaviors, for teen drivers with different levels of ADHD screen scores, taking into account the potential influence of other behavioral and demographic factors captured in naturalistic driving study (NDS) data. 

]]></description>
      <pubDate>Tue, 19 Jul 2022 12:47:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1996244</guid>
    </item>
    <item>
      <title>Identification and Assessment of Preventative Methods to Mitigate Cognitive and Physical Declines Which Influence Driving Performance of Older Drivers</title>
      <link>https://rip.trb.org/View/1917684</link>
      <description><![CDATA[The objectives are to: (1) provide additional guidance on the efficacy of the coaching app to improve older driver performance and safety over a prolonged exposure period; (2) determine the strength of mindfulness meditation training on improving attention and driving performance; and (3) determine how the two treatments combined may result in additional gains in performance over each individual treatment.]]></description>
      <pubDate>Wed, 16 Feb 2022 11:24:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1917684</guid>
    </item>
    <item>
      <title>Effects of Cognitive Load on Takeover Requests in Conditionally Automated Driving</title>
      <link>https://rip.trb.org/View/1855169</link>
      <description><![CDATA[With increases in vehicle automation, drivers can engage in non-driving related tasks while trusting automation to maintain driving control. When the vehicle issues a takeover request (TOR), drivers must disengage attention from their non-driving task to direct attention toward the task of driving. Attentional disengagement takes time, making takeover requests limited by drivers’ attentional control. In the current project, the research team investigates how the cognitive complexity, or cognitive load, of the non-driving task impacts the time to disengage attention and respond to a TOR. Specifically, is the cost in disengaging attention from a non-driving task and switching to regaining vehicle control affected by the difficulty, or cognitive load, of the non-driving related task? Participants will perform a simulated automated drive while performance a secondary non-driving task under either a high- or low-cognitive load. The team will measure the impact of the cognitive load on driving parameters (e.g., lane position) and the time and quality of the driver’s takeover.]]></description>
      <pubDate>Fri, 28 May 2021 15:21:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1855169</guid>
    </item>
    <item>
      <title>State of Knowledge on Novice Drivers</title>
      <link>https://rip.trb.org/View/1747334</link>
      <description><![CDATA[This project will produce a report in which each chapter contains a comprehensive systematic literature review that summarizes, evaluates, and synthesizes research published since 1999 on topics related to novice driver safety. The National Highway Traffic Safety Administration (NHTSA) has previously published periodic “State of Knowledge” reports on a variety of behavioral safety topics. However, the current project represents the first on issues related to novice driving. The current project will apply systematic review procedures to comprehensively identify and evaluate the quality of studies on a selection of topics related to novice driver safety. The project also will convene an expert panel discussion on the implications of automation for novice drivers. Each chapter of the Novice Driver State of Knowledge report will describe the selected studies, aggregate findings across studies, and weigh study quality to provide a synthesis of the best evidence to date on the topic. The Novice Driver State of Knowledge report is intended to be a reference document for both in-house use and use by other Federal agencies, State Highway Safety Offices, and other partners. The report will also be of use to researchers interested in novice driver safety, driver and traffic safety education professionals and stakeholder groups, and non-profits involved in novice driver program development and administration.]]></description>
      <pubDate>Tue, 27 Oct 2020 13:06:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1747334</guid>
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