Agency Information Collection Activities; Submission to the Office of Management and Budget for Review and Approval; Examining Distraction and Driver Monitoring Systems To Improve Driver Safety, 37277-37280 [2024-09776]

Agencies

• National Highway Traffic Safety Administration
• DEPARTMENT OF TRANSPORTATION

[Federal Register Volume 89, Number 88 (Monday, May 6, 2024)]
[Notices]
[Pages 37277-37280]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2024-09776]


-----------------------------------------------------------------------

DEPARTMENT OF TRANSPORTATION

National Highway Traffic Safety Administration

[Docket No. NHTSA-2023-0026]


Agency Information Collection Activities; Submission to the 
Office of Management and Budget for Review and Approval; Examining 
Distraction and Driver Monitoring Systems To Improve Driver Safety

AGENCY: National Highway Traffic Safety Administration (NHTSA), 
Department of Transportation (DOT).

ACTION: Notice and request for comments on a request for approval of a 
new information collection.

-----------------------------------------------------------------------

SUMMARY: In compliance with the Paperwork Reduction Act of 1995 (PRA), 
this notice announces that the Information Collection Request (ICR) 
summarized below will be submitted to the Office of Management and 
Budget (OMB) for review and approval. The ICR describes the nature of 
the information collection and its expected burden. This document 
describes a new collection of information for which NHTSA intends to 
seek OMB approval titled Examining Distraction and Driver Monitoring 
Systems to Improve Driver Safety. A Federal Register Notice with a 60-
day comment period soliciting comments on the following information 
collection was published on July 14, 2023. Four comments were received 
during the comment period. This 30-day notice includes a summary of 
those comments, responses to the comments (no changes to the study are 
expected as a result of the comments), and an update to the estimated 
burden hours from the 60-day notice.

DATES: Comments must be submitted on or before June 5, 2024.

ADDRESSES: Written comments and recommendations for the proposed 
information collection, including suggestions for reducing burden, 
should be submitted to the Office of Management and Budget at 
www.reginfo.gov/public/do/PRAMain. To find this particular information 
collection, select ``Currently under Review--Open for Public Comment'' 
or use the search function.

FOR FURTHER INFORMATION CONTACT: For additional information or access 
to background documents, contact: Thomas Fincannon, Office of Vehicle 
Safety Research, Human Factors/Engineering Integration Division NSR-
310, West Building, W46-447, 1200 New Jersey Ave. SE, Washington, DC 
20590; [email protected].

SUPPLEMENTARY INFORMATION: Under the PRA (44 U.S.C. 3501 et seq.), a 
Federal agency must receive approval from the Office of Management and 
Budget (OMB) before it collects certain information from the public and 
a person is not required to respond to a collection of information by a 
Federal agency unless the collection displays a valid OMB control 
number. In compliance with these requirements, this notice announces 
that the following information collection request will be submitted 
OMB.
    Title: Examining Distraction and Driver Monitoring Systems to 
Improve Driver Safety.
    OMB Control Number: New.
    Form Numbers: NHTSA Form 1718 Online Eligibility Questionnaire, 
NHTSA Form 1719 Karolinska Sleepiness Scale, NHTSA Form 1799 
Appointment Reminder Confirmation Process, NHTSA Form 1720 Sleep and 
Food Intake, NHTSA Form 1721 End of Visit Release Agreement, NHTSA Form 
1730 Track A Consent Form, and NHTSA Form 1731 Track B Consent Form 
Track B.
    Type of Request: New information collection.
    Type of Review Requested: Regular.
    Length of Approval Requested: Three years from date of approval.

Summary of the Collection of Information

    NHTSA proposes to collect information from the public as part of a 
study to improve NHTSA's understanding of the differences in approaches 
to driver state detection and the potential safety impacts of driver 
monitoring systems (DMS). DMS refers to in-vehicle technology that can 
detect driver state and interact with the driver through the human-
machine interface (the user interface that connects the driver to the 
vehicle). For example, a DMS that detects drowsiness may display an 
icon on the dashboard, such as a coffee cup, accompanied by a sound to 
alert the driver that drowsiness is present.
    This study contains two tracks to assess DMS, and subjects may 
participate in Track A, Track B, or both. This allows for a balance 
between understanding how driver state detection changes within a 
diverse testing sample and within an individual across driver states. 
The overall sample will contain 80 data sets. Each track will have 40 
completed data sets. Thus, the total sample size is anticipated to be 
68 subjects and will include subjects that completed Track A only (n = 
28), Track B only (n = 28), and those that completed both tracks (n = 
12). Track A will evaluate the ability of the DMS to assess distraction 
and Track B will evaluate the ability of the DMS to assess both 
drowsiness alone and distraction while drowsy.
    NHTSA proposes to collect information from licensed drivers about 
their age, sex, driver license status, sleep and driving habits, and 
general health history to determine eligibility for the study. Those 
interested in participating will be asked about their ability to adhere 
to various requirements of the protocol (e.g., abstain from caffeine) 
and availability for a study appointment. Those who participate in the 
study will come to the University of Iowa Driving Safety Research 
Institute (DSRI), home of the National Advanced Driving Simulator 
(NADS). Both tracks involve a consent process, breath alcohol 
measurement, facial shape measurement, standing and seated height 
measurement, training presentation, a familiarization drive in the 
driving simulator, and sleepiness ratings before and after each study 
drive as well as approximately every 30 minutes during a waiting 
period. Both tracks also involve taking a digital image of the face so 
that researchers can obtain RGB values to assess skin tone variability. 
Track A only involves one study drive that occurs while the subject is 
alert and distracted. In Track B, subjects will be asked about their 
sleep and food intake (to confirm they have not consumed caffeine since 
1:00 p.m., that they were awake by 7:00 a.m., and that they have 
consumed no other substances that could influence driving) prior to an 
overnight driving session that involves three study drives. The first 
drive occurs while alert. The next two drives are counterbalanced and 
will occur while drowsy (at least 14 hours awake and having sleepiness 
ratings indicating drowsiness) and while drowsy and distracted. 
Simulator data will be used to evaluate the ability of the DMS to 
assess driver state.
    Respondents will volunteer for the study by responding to an 
internet ad or via solicitation for volunteers from the

[[Page 37278]]

DSRI subject registry. Only potential subjects in the registry meeting 
inclusion criteria will be contacted. Respondents will be asked a 
series of questions to determine eligibility to participate in the 
study. The questionnaire covers both Track A and Track B so respondents 
don't have to complete the questionnaire more than once and so 
researchers can ensure a subset of respondents meet criteria for both 
tracks. Criteria for both studies are largely the same; differences are 
related to ability to attend visits of a specified length, willingness 
to adhere to different protocol elements, and sleep habits (needed only 
for Track B). A research team member will answer all questions the 
respondent may have and schedule eligible respondents who wish to 
participate for a session at the DSRI.

Description of the Need for the Information and Proposed Use of the 
Information

    NHTSA was established by the Highway Safety Act of 1970 (Pub. L. 
91-605, 202(a), 84 Stat. 1713, 1739-40). Its mission is to reduce the 
number of deaths, injuries, and economic losses resulting from motor 
vehicle crashes on our nation's highways. To further this mission, 
NHTSA conducts research as a foundation for the development of traffic 
safety programs.
    In 2013, NHTSA published the final version of the Visual-Manual 
NHTSA Driver Distraction Guidelines for In-Vehicle Electronic Devices. 
In the decade since, vehicle technologies and interfaces have evolved 
and a substantial amount of new research on the topic of driver 
distraction has been conducted. As a result, NHTSA requires a rigorous 
and thorough review to update the current state of knowledge on driver 
distraction, attention management, and distraction/risk assessment. 
Driver monitoring systems (DMS) are currently deployed in many 
production vehicles. Current production systems use different data 
sources, including driver-facing cameras, vehicle inputs (e.g., 
steering wheel torque), driving performance (e.g., lane departures), 
and other measures (e.g., time on task). Future production systems are 
also likely to use physiological sensors (e.g., heart rate) as tools to 
identify driver state more accurately. DMS could play a variety of 
roles in vehicles, including detecting and alerting drivers to 
distraction, drowsiness, or impairment, and then adjusting the vehicle 
technology to meet the needs of the driver or providing support in 
particular situations. It is important for NHTSA to be able to discern 
the differences in approaches to state detection to understand the 
potential safety impacts of DMS. This requires a comparison of various 
sensor approaches to driver state monitoring and the development of a 
test protocol for different DMS methodologies. The overall objective is 
to develop and deliver a methodology that will assess the ability of 
DMS to accurately determine driver state by collecting data to support 
a full assessment of the factors associated with DMS and modeling 
driver state based on sensor data in a driving simulator.

60-Day Notice

    A Federal Register notice with a 60-day comment period soliciting 
public comments on the following information collection was published 
on July 14, 2023 (88 FR 45269). Four comments and one email were 
received in response to that notice. During the public comment period 
for the 60-day notice, NHTSA received four comments and one email. The 
first comment requested collection of data regarding circadian effects 
as related to school start times. This would involve subjects under the 
age of 18 and are not related to driver monitoring systems and is out 
of scope of the planned research project. The second comment expressed 
a dislike for driver monitoring systems as expressed the opinion that 
DMS are a disciplinary tool rather than a safety tool. NHTSA 
respectfully disagrees with this opinion and believes DMS may be able 
to improve motor vehicle safety.
    One email from Alliance for Automotive Innovation asked if the 
research was in response to Sec. 24209 of the Infrastructure Investment 
and Jobs Act, 2021 (H.R. 3684; Pub. L. 117-58, enacted on November 15, 
202 and commonly referred to as the Bipartisan Infrastructure Law or 
BIL). NHTSA responded by email to the Alliance for Automotive 
Innovation and noted that this project does include elements that were 
funded by the IIJA/BIL legislation. The email response also encouraged 
submission of comments to regulations.gov and noted that NHTSA would 
provide responses to comments in a 30-day notice published in the 
Federal Register (this document).
    Two of the comments received were relevant to the burden and design 
of the study. The following summarizes the points brought up in those 
comments and NHTSA's response.
    The American Academy of Sleep Medicine (AASM) commended NHTSA for 
planning the current information collection. They found the assessment 
of both drowsiness and distraction while drowsy to be a progressive and 
necessary step in determining the utility of DMS as a tool for road 
safety.
    The AASM commented that self-reported sleepiness may not always 
reflect an individual's true level of sleepiness and recommended the 
inclusion of other objective measures of alertness, such as 
electroencephalography (EEG) or the psychomotor vigilance task (PVT) to 
strengthen the accuracy of collected drowsiness data. Response: The 
research team has used both EEG \1\ and PVT \2\ as part of prior drowsy 
driving research. We included the review of this data as part of 
preliminary steps in this research study. Specifically, we found a 
strong relationship between the Observer Rating of Drowsiness (ORD) and 
the Karolinska Sleepiness Scale (KSS) (r = 0.682, p <0.001) and weak 
relationships between ORD and Psychomotor Vigilance Task (PVT) prior to 
the drive (r = 0.150, p <0.001) and after the drive (r = 0.244, p 
<0.001). Based on our prior published research, the inherent value of 
adding EEG is limited, but there are substantial increases to the 
burden (e.g., application/cleanup & driver distraction) that do not 
outweigh this benefit. Depending on the EEG system, applying the EEG to 
the participant's scalp can range from 45 minutes to 120 minutes. The 
EEG may also interfere with the driver and cause additional 
distraction, discomfort, or prevent them from becoming immersed in the 
driving scenario, further reducing ecological validity. Recently, other 
researchers have investigated the associations between KSS, ORD, 
vehicle-based measures, and metrics from electrooculogram (EOG) and 
EEG.\3\ KSS

[[Page 37279]]

was associated with ORD, standard deviation of lateral position (SDLP), 
percentage of eyelid closure over the pupil over time (PERCLOS), EEG 
alpha power, EEG theta power, and percentage of time with slow eye 
movement. Interestingly, measures from the physiological sensors (i.e., 
EEG and EOG) displayed only weak and moderate associations. Given these 
considerations, we maintain that the KSS will produce sufficiently 
accurate data to support the goals of the data collection while 
minimizing participant burden. The KSS will be used to determine when 
drivers have achieved a certain level of drowsiness and thus, they will 
begin the drowsy drive. We anticipated participants will complete the 
KSS nine times prior to the drive. Drowsiness will be defined based on 
a combination of the participant being awake for a minimum of 14 hours 
and the KSS. The KSS will not be administered during the drive as this 
may influence driver's levels of drowsiness. Drowsiness during the 
drive will be captured by measures derived from eye closures over the 
course of the drive (e.g., PERCLOS). Given that each approach to 
measuring drowsiness comes with inherent flaws, we are using a 
combination of measures to infer drowsiness based on a sleepiness scale 
to bookend drowsiness during the drive and use of eye measures (i.e., 
PERCLOS) to elucidate changes in drowsiness levels during the drive.
---------------------------------------------------------------------------

    \1\ Brown, T., Johnson, R., & Milavetz, G. (2013). Identifying 
Periods of Drowsy Driving Using EEG. Annals of Advances in 
Automotive Medicine, 57, 99. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861841/; Brown, T., Lee, J., Schwarz, C., Fiorentino, 
D., McDonald, A., Traube, E., & Nadler, E. (2013). Detection of 
Driver Impairment from Drowsiness. 23rd International Technical 
Conference on the Enhanced Safety of Vehicles, Seoul, South Korea.; 
Brown, T., Lee, J., Schwarz, C., Fiorentino, D., & McDonald, A. 
(2014). Assessing the Feasibility of Vehicle-Based Sensors to Detect 
Drowsy Driving. (DOT HS 811 886). Washington, DC: National Highway 
Traffic Safety Administration Retrieved from https://www.nhtsa.gov/DOT/NHTSA/NVS/Crash%20Avoidance/Technical%20Publications/2014/811886-Assess_veh-based_sensors_4_drowsy-driving_detection.pdf.
    \2\ McDonald, A.D., Lee, J.D., Schwarz, C., & Brown, T.L. 
(2018). A Contextual and Temporal Algorithm for Driver Drowsiness 
Detection. Accident Analysis & Prevention.
    \3\ Uchiyama, Y., Sawai, S., Omi, T., Yamauchi, K., Tamura, K., 
Sakata, T., Nakajima, K., & Sakai, H. (2023). Convergent validity of 
video-based observer rating of drowsiness, against subjective, 
behavioral, and physiological measures. PLoS one, 18(5), e0285557.
---------------------------------------------------------------------------

    The AASM recommended that the information collection include an 
assessment of possible sleep disorders during the online eligibility 
questionnaire and advised excluding individuals with untreated sleep 
disorders from the study. Additionally, AASM recommended that the data 
collection include a measure of participant sleep quality in order to 
quantify contributing factors to drowsiness and driving performance; 
they suggested use of a participant sleep log and/or a three-day 
reporting of bedtimes, waketimes, estimate of the amount of time to 
fall asleep, number of awakenings, estimate of the amount of time awake 
during the awakenings, and daytime sleeping times and duration. 
Response: The proposed study procedures will capture wake and sleep 
time for the day preceding the study visit. We are not aware of any 
validated sleep log, and as additional measures would increase burden 
to participants, we have proposed to only ask targeted items that are 
known to influence drowsiness (i.e., wake time and sleep time) and can 
be used to provide measures for the analysis (i.e., hours of sleep and 
continuous time awake). The items that we ask participants are 
extracted from sleep logs and are variables that we could include in 
our statistical models. Since the sleep logs are not validated, we 
selected specific items, rather than using the entire log, as this 
reduces participant burden. Given that the focus of this research is on 
the manifestation of drowsiness (i.e., for the purpose of determining 
validity of DMS assessment) while driving in the general driving 
population, we did not propose collecting subjective evaluation of 
sleep quality in subjects which might be better addressed by NIH funded 
research, nor do we plan to exclude participation based on sleep 
disorders given that an estimated 9 to 15% of individuals have ongoing 
sleep disorders. A DMS will need to detect distraction and drowsiness, 
regardless of individual health conditions, and exclusion of these 
drivers could hinder the external validity of findings from this 
research. The presence of daytime drowsiness regardless of source will 
be collected using self-reported sleepiness via the KSS.
    The AASM also requested clarification on how the data obtained from 
the study would be protected, particularly as it related to prevention 
of consequences for participants who are distracted while driving. The 
AASM also asked whether a certificate of confidentiality would be 
provided. Response: The study has received approval from the University 
of Iowa Institutional Review Board, which requires us to protect the 
participants' anonymity. Respondents' performance in the driving 
simulator will be deidentified and separated from any personally 
identifiable information. Certificates of confidentiality are generally 
not sought unless we are collecting data that would put the 
participants at legal risk, which is not the case in this study.
    The National Association of Mutual Insurance Companies (NAMIC) 
commented that the use of the Fitzpatrick Skin Type Scale in the online 
eligibility questionnaire, which requires participants to self-rate, 
negates the uniformity of the scale. Further, NAMIC questions why the 
study intends to oversample participants who are rated higher on the 
scale (e.g., darker skin types). Response: The proposed self-rating of 
an applicant on the Fitzpatrick Skin Type Scale will be used to inform 
our study stratification and data collection logistics. The scale will 
be used to objectively quantify their skin pigmentation upon consenting 
and enrolling our study by a single rater. Additionally, the RGB values 
for skin tone will be captured during the visit via visual processing 
to provide an objective metric with greater gradation.
    NAMIC also requested additional clarification on which driver 
monitoring system(s) will be used in the study. Response: The team will 
implement a sensor suite to provide the same types of signals available 
to a variety of types of DMS including vehicle and driver data. DSRI 
has existing relationships with technology suppliers that will be 
leveraged to provide necessary data. We do not propose to evaluate the 
algorithms from any technology suppliers, but instead focus on the 
utility of the underlying signals in detection.
    Both AASM and NAMIC commented on the importance of recruiting 
participants from a large audience to ensure a sample that is 
representative and generalizable to a larger driving population. NAMIC 
noted their concerns related to the limited location (noting a 30-mile 
radius around Iowa City, IA), number of participants, and participant 
selection process. Response: A power analysis was conducted to estimate 
the sample size needed for the study. We agree that generalizability is 
important and must be balanced with the experimental aims of the 
research. Given that the research method utilizes a one-of-a-kind 
driving simulator, recruitment must be focused in the geographic area 
where it is housed. The plan is to maximize diversity of the sample 
within the limits of the proposed sample size through robust 
recruitment utilizing the existing registry which includes thousands of 
potential participants that includes the Cedar Rapids-Iowa City, IA 
CSA; Davenport-Moline, IA-IL CSA; Waterloo-Cedar Falls, IA MSA; 
Dubuque, IA MSA; Ottumwa, IA USA; Fort Madison-Keokuk, IA-IL-MO USA; 
Burlington, IA-IL USA; and Marshalltown, IA USA in addition to the 
surrounding rural areas. To expand the diversity of the overall sample, 
areas outside of Iowa City are being included in the recruitment 
approach. Additionally, participants who are not in the registry are 
not excluded from participating. No participants are excluded due to 
location so long as they are able to arrange safe transportation to/
from the facility for the overnight visit. Prior research has shown 
that this can be done effectively, particularly when the study includes 
within-subject comparisons, which is one reason why we are including a 
subset of the sample in both tracks. As Iowa is less ethnically diverse 
than the US population overall, targeted recruitment will be performed

[[Page 37280]]

to promote a more balanced sample based on the Fitzpatrick Skin Type 
Scale, which is also a crucial variable to include when assessing the 
capabilities of DMSs. The proposed self-rating of an applicant on the 
Fitzpatrick Skin Type Scale will be used to inform our study 
stratification and data collection logistics.

Affected Public

    Individuals aged 18+ from Eastern Iowa and the surrounding areas 
who have volunteered to take part in driving studies will be contacted 
for participation. They will be randomized evenly by sex, though some 
imbalance will be permitted to be inclusive of individuals who do not 
identify on the binary. Efforts will be made to enroll a diverse age 
sample that broadly represents the age of the driving population and 
includes those at greater risk of crashing (e.g., less than 25 years of 
age and greater than 65 years of age). Additional efforts will be made 
to enroll individuals with diverse skin tones, oversampling those who 
rate themselves higher on the Fitzpatrick Skin Type Scale.
    Estimated Number of Respondents: Varies by individual information 
collection. See Table 1 below.
    Frequency: Varies by individual information collection. See Table 1 
below.
    Annual Number of Responses: 626.
    Estimated Annual Burden Hours: 175 hours.
    The estimated annual burden for the study is 175 hours. Table 1 
provides estimates for the burden calculation across the study.

                                                            Table 1--Annual Burden Estimates
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                                                Annual
                                                   Annual  number                                               Cost per      estimated       Annual
                 Study component                         of         Frequency  of     Annual      Time per      response        burden      opportunity
                                                     respondents      response      responses     response    ($32.36/hour)   (rounded)        costs
                                                                                                                                (hrs)        (rounded)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Online Eligibility Questionnaire (Form 1718).....             200               1          200       10 min           $5.39           33          $1,078
Appointment Reminder Confirmation Process (Form                35            1.15           40            5            2.70            3             108
 1799)...........................................
Breathalyzer Measurement.........................              28            1.16           32            3            1.62            2              52
Facial Shape and Height Measurement..............              27            1.15           31            7            3.78            4             117
Karolinska Sleepiness Scale (Form 1719)..........              27            8.43          228            1            0.54            4             123
Track A Informed Consent (Form 1730).............              16               1           16           15            8.09            4             129
Track A Study Drive (includes Training                         16               1           16        81.25           43.82           22              22
 Presentation, Familiarization Drive and Study
 Drive)..........................................
Track B Informed Consent (Form 1731).............              16               1           16           15            8.09            4             129
Sleep & Food Intake (Form 1720)..................              16               1           16            5            2.70            1              43
Track B Study Drive (includes Training                         45               1           45       388.38          209.47           97           3,142
 Presentation, Familiarization Drive, Wait Time,
 Study Drives)...................................
End of Visit Release Agreement (Form 1721).......              16               1           16            2            1.08            1              17
                                                  ------------------------------------------------------------------------------------------------------
    Total Burden.................................  ..............  ..............          626  ...........  ..............          175           5,159
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Estimated Total Annual Burden Cost: $0.
    The respondents are not expected to incur any reporting or 
recordkeeping cost from the information collection. The only costs 
associated with any of the information collections is the cost for 
travel to and from DSRI, which is associated with each of the study 
drives. We estimate that 83 respondents will travel to DSRI for each of 
the two tracks, though 13 respondents will travel for both tracks 
resulting in 96 round trips. We expect most subjects to be traveling 
locally, within 30 miles from the test facility. Maximally, we estimate 
a round trip distance from subjects' starting destination to DSRI to be 
60 miles. The standard mileage rate for business-related driving in 
2023 is 65.5 cents per mile driven, or $39.30 for 60 miles driven. 
Therefore, we estimate the maximum travel costs associated with Track A 
Study Drive to be $1,886 (48 respondents x $39.30 = $1,886.40). We 
estimate that the total transportation costs will be higher for 
subjects in Track B, who will not be permitted to walk, bike, or drive 
when leaving the test facility. Previous overnight studies conducted at 
DSRI have shown that $70 compensation for transportation expenses was 
sufficient to limit subject attrition and offset costs of third-party 
transportation. Accordingly, we estimate the travel costs associated 
with Track B Study Drive to be $3,360 (48 respondents x $70 = $3,360). 
The total costs for this ICR are estimated to be $5,246 ($1,886 + 
$3,360). These transportation costs are offset by subject compensation. 
For subjects in Track B, who will not be permitted to walk, bike, or 
drive when leaving the test facility, an additional $70 will be 
provided to offset the costs of finding alternative transportation. 
Table 1 provides an estimate for the opportunity cost of the 
collection; however, there is no direct cost to the respondents for 
this collection.
    Public Comments Invited: You are asked to comment on any aspects of 
this information collection, including (a) whether the proposed 
collection of information is necessary for the proper performance of 
the functions of the agency, including whether the information will 
have practical utility; (b) the accuracy of the agency's estimate of 
the burden of the proposed collection of information, including the 
validity of the methodology and assumptions used; (c) ways to enhance 
the quality, utility and clarity of the information to be collected; 
and (d) ways to minimize the burden of the collection of information on 
respondents, including the use of appropriate automated, electronic, 
mechanical, or other technological collection techniques or other forms 
of information technology, e.g., permitting electronic submission of 
responses.
    Authority: The Paperwork Reduction Act of 1995; 44 U.S.C. chapter 
35, as amended; 49 CFR 1.49; and DOT Order 1351.29A.

Cem Hatipoglu,
Associate Administrator, Vehicle Safety Research.
[FR Doc. 2024-09776 Filed 5-3-24; 8:45 am]
BILLING CODE 4910-59-P