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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>Evaluating the Effectiveness of Drivers' Education Modules on Safety </title>
      <link>https://rip.trb.org/View/2714400</link>
      <description><![CDATA[There is mixed evidence as to the effectiveness of drivers’ education courses. It is also unknown whether drivers’ education can be used to train drivers on how to effectively use driving automation. The objective of this research is to examine: How differences in drivers' education program delivery affects novice drivers' behavior, crashes, and citations within 12 months of licensure; How differences in novice drivers' pre-license behaviors affect crashes and citations within 12 months of licensure; How driver education and training programs can help improve novice drivers' use and understanding of advance driver assistance systems (ADAS).]]></description>
      <pubDate>Mon, 15 Jun 2026 15:59:07 GMT</pubDate>
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      <title>Examining the Impact of Vehicle Automation Levels on Road Safety in Rural Areas</title>
      <link>https://rip.trb.org/View/2265843</link>
      <description><![CDATA[Motor vehicle crashes are a prominent and distressing cause of fatalities in the United States and globally. Addressing this issue, the integration of partially automated vehicle technologies, notably Advanced Driver Assistance Systems (ADAS), emerges as a promising avenue for enhancing safety on highways. These systems become even more critical to older adult drivers, who face increased risks of fatality and crashes due to age-related declines in physical, health, and cognitive abilities. ADAS has the potential to decrease the sensory cognitive load of the driving task, and many automated safety features can decrease crash severity. The ADAS vary widely in complexity and scope which can mainly be classified into three major groups: collision warning, collision intervention, and driving control assistance. Examples of collision warning technologies that are common in vehicles include forward collision, lane departure, and blind spot warnings. For collision intervention, automated emergency braking, blind spot intervention, and rear automatic braking are examples of available technologies. On the other hand, driving control assistance includes adaptive cruise control and lane-keeping or centering assistance.
Several researchers have investigated these in-vehicle technologies to learn older drivers’ perceptions of safety and interaction with the ADAS. However, little is known about the role of these technologies and their impact on crash injuries. It will be beneficial to the community to understand the role of ADAS technologies in the safe mobility of drivers in rural areas.
]]></description>
      <pubDate>Thu, 19 Oct 2023 16:56:36 GMT</pubDate>
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      <title>Examination of How the Duration of Secondary Task Engagement Changes Over Time in Lower Levels (L0 to L2) of Driving Automation</title>
      <link>https://rip.trb.org/View/2050285</link>
      <description><![CDATA[Limited available evidence suggest that drivers may engage in secondary tasks both in L0 and L2 at approximately equal rates. However, anecdotal evidence suggests that the length of task engagement is longer with L2 systems activated. The goal of this study is to quantify both the number and length of secondary task engagement across levels of driving automation (i.e., L2 active vs. inactive) in a naturalistic setting. Baseline and L2 systems may vary (e.g., conventional cruise control and lane centering), atypical applications of Advanced Driver Assistance Systems (ADAS) are not necessarily excluded]]></description>
      <pubDate>Tue, 25 Oct 2022 10:24:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2050285</guid>
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      <title>Tool to estimate the safety impact of vehicle levels of automation on Minnesota roads</title>
      <link>https://rip.trb.org/View/1764575</link>
      <description><![CDATA[Vehicle automation is attracting great interest both commercially and research-wise, and it is expected to bring disruption in transportation in the years to come. Currently, an increasing number of commercially available vehicles offer Advanced Driver-Assistance Systems (ADAS). Even if commercially available self-driving vehicles are not going to be available for several decades, the safety, mobility, and environmental benefits envisioned can be achieved with technological advancements already available to the public. 
Minnesota Department of Transportation (MnDOT) and other state DOTs as well as the federal government has supported research investigating the operational and environmental effects adoption rates of vehicles with various levels of automation. Recent findings caution that the resulting future may not be as rosy as we thought (Soteropoulos et al., 2019, Kröger et al., 2018). Unfortunately, very little research has been focused on the net effect of the biggest CAV selling point, the potential benefits from reduction in crashes and general increase in road safety. Although the proposed effort will, by necessity, perform a thorough investigation of the different flavors of vehicle automation already commercially available as well as explore their near-future improvements, the main focus is on developing a planning tool to quantify the statewide net safety effect of policies, market, and technology forces affecting the proliferation and actual use of individual ADAS. The proposed tool will utilize records of actual crashes in Minnesota combined with probabilistic models relating facts like vehicle model and age, driver age and other demographic information, with the potential of owning and having activated a specific combination of ADAS features as well as, given the prevailing road, traffic, and environmental conditions, the probability this particular ADAS changing the outcome of the event.]]></description>
      <pubDate>Thu, 21 Jan 2021 16:03:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1764575</guid>
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      <title>Administration of Highway and Transportation Agencies. Connected Road Classification System (CRCS) Development</title>
      <link>https://rip.trb.org/View/1445802</link>
      <description><![CDATA[The objective of this project was to develop a consensus Connected Road Classification System (CRCS) for state and local transportation agencies and metropolitan planning organizations implementing connected and automated vehicle-compatible infrastructure. Feedback from transportation asset owners, vehicle original equipment manufacturers, and other private-sector stakeholders was solicited for the development of a flexible Connected Roadway Classification System (CRCS) Framework to help assess infrastructure that will support connected and automated vehicles. The final contractor's report and summary presentation are available for download and immediate use.]]></description>
      <pubDate>Thu, 19 Jan 2017 09:14:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1445802</guid>
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