|
|---|
|
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 Publication: Federal Register Agency: National Highway Traffic Safety Administration Byline: Cem Hatipoglu Date: 6 May 2024 Subject: American Government  , Safety, Software
|
[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; thomas.fincannon@dot.gov.
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. http://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 http://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