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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <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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      <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>
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      <title>Development of Cooperative Automation Capabilities: CARMA Multimodal Development and Testing</title>
      <link>https://rip.trb.org/View/2062787</link>
      <description><![CDATA[Building upon the Federal Highway Administration (FHWA) Open Source CARMA Platform and Cloud Prototype, enhanced and new integrated L2/3 maneuvers and capabilities will be developed for several cooperative dynamic driving task algorithms supporting arterial and freeway operational strategies.  The vision for CARMA is to encourage collaboration and partnerships with university and industry stakeholders by developing in the open with the goal of understanding benefits for cooperative automation by improving transportation efficiency and safety through early industry adoption.  CARMA takes a multi-modal approach, focusing on integrating the CARMA platform on an Automated Driving System (ADS), for both cars and trucks, and develop a proof-of-concept for cooperative interaction with road infrastructure. This project will develop cooperative automated driving applications and improved algorithms to support TSMO use cases and capabilities leading towards deployment. The agile software development process and the use of GitHub to develop in the open is a key element for collaboration that will support early industry adoption. This project also will utilize a diverse team of national experts from both public and private sectors, through several established partnerships to accelerate the technology transfer and deployment of cooperative automated driving applications on state highways.]]></description>
      <pubDate>Thu, 17 Nov 2022 11:56:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062787</guid>
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    <item>
      <title>Research Key AV safety issues for Transportation Systems Management and Operations (TSMO)</title>
      <link>https://rip.trb.org/View/2062778</link>
      <description><![CDATA[This research is identifying key human factors safety issues associated level 2 and level 3 TSMO use cases, e.g., basic traveling (reoccurring congestion), work zones, weather, and Traffic Information Management (TIM). Four studies are being developed and executed based on the identification and prioritization of these key automation human factors safety issues. Potential human factors issues may involve driver engagement / disengagement, transfer of control, or other human performance, DVI (driver vehicle interface) interaction related issues that could impact safety. Results will be focused on helping to develop recommendations, guidelines, and/or standards related to design of cooperative automation of roadways and vehicles. The current ongoing study is exploring the potential for V2V communication to safely speed up drivers' response times to emergency vehicles in a TIM Use Case. The remaining 3 potential studies will examine the following; merging behavior, gap acceptance, and human factors issues when drivers are supported by SAE level 2 or 3 automation and cooperative merging technology, benefits of using V2I or specialized road signs to support automation technology performance and human-automation interaction in work zones, and driver trust in and interactions with SAE level 2 or 3 automation technology when following vehicles in a mixed fleet of traffic in adverse weather conditions.]]></description>
      <pubDate>Thu, 17 Nov 2022 11:56:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062778</guid>
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      <title>Advanced Driver Assistance Systems (ADAS) Education and Outreach</title>
      <link>https://rip.trb.org/View/1996241</link>
      <description><![CDATA[BTSCRP Research Report 18: Evaluating and Delivering Advanced Driver Assistance Systems (ADAS) Education: A Guide provides practical guidance for identifying, creating, and modifying ADAS materials to support specific goals and objectives related to education and training.

Proper use of ADAS —rapidly being introduced into the US vehicle fleet—offers the promise of reducing motor vehicle crashes and fatalities. ADAS features, however, can be confusing to drivers; include a wide variance of terminology; and have many differences in design and functionality. ADAS technology differs from previous vehicle safety enhancements for which a simple message or warning conveys direction to drivers. ADAS requires new models for messaging to help drivers understand and effectively use these complex new technologies. As ADAS technologies continue to advance and permeate the vehicle fleet, it is critical to ensure understanding of how the systems work and how to safely use them. 

Under BTSCRP Project BTS-26, “Advanced Driver Assistance Systems (ADAS) Education and Outreach,” the University of Iowa was asked to (1) assess the current state of ADAS education, training materials, and delivery methods; (2) identify key populations in need of ADAS education and training; (3) pinpoint gaps in existing educational content and instructional methods; and (4) determine effective strategies for delivering ADAS information and training to target audiences. 

In addition to this report, the following deliverables are available on the National Academies Press website (nap.nationalacademies.org) by searching BTSCRP Research Report 18: Evaluating and Delivering Advanced Driver Assistance Systems (ADAS) Education: A Guide: Conduct of research report that documents the entire research effort, published as BTSCRP; Web-Only Document 9: Advanced Driver Assistance Systems (ADAS) Education and Outreach; ADAS Information Source Tracker; Resource Identification Tool; Content Organization Tool; and PowerPoint Presentation.]]></description>
      <pubDate>Thu, 21 Jul 2022 12:56:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1996241</guid>
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    <item>
      <title>Advanced Driver Assistance Systems (ADAS) Technology and Distracted Driving</title>
      <link>https://rip.trb.org/View/1747464</link>
      <description><![CDATA[This project will design a study examining the effects of Advanced Driver Assistance System
(ADAS) technologies on distracted driving behaviors and driving performance. The project also will provide a feasibility assessment of the study design by conducting a pilot study with nine participants.
The design will assess whether drivers’ use of ADAS technologies increases their likelihood to engage in distracted driving behaviors, such as tuning the radio, talking to passengers, or engaging in hand-held cell phone use, as predicted by behavioral adaptation. If such engagement does increase, the design will determine whether it leads to more crashes/near crash events or dangerous driving behaviors such as swerving or braking hard. The study design would also observe when, where, and how often drivers engage in distracted driving both with and without ADAS technologies. ]]></description>
      <pubDate>Wed, 28 Oct 2020 15:45:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/1747464</guid>
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