Opportunities and Challenges of Artificial Intelligence (AI) in Transportation; Request for Information, 36848-36851 [2024-09645]
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Federal Register / Vol. 89, No. 87 / Friday, May 3, 2024 / Notices
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[FR Doc. 2024–09653 Filed 5–2–24; 8:45 am]
BILLING CODE 4910–81–P
DEPARTMENT OF TRANSPORTATION
[Docket No. DOT–OST–2024–0049]
Opportunities and Challenges of
Artificial Intelligence (AI) in
Transportation; Request for
Information
AGENCY:
Department of Transportation
(DOT)
ACTION:
Notice; Request for Information
(RFI).
The U.S. Department of
Transportation’s Advanced Research
Projects Agency—Infrastructure (ARPA–
I) is seeking input from interested
parties on the potential applications of
artificial intelligence (AI) in
SUMMARY:
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transportation, as well as emerging
challenges and opportunities in creating
and deploying AI technologies in
applications across all modes of
transportation. The purpose of this
Request for Information (RFI) is to
obtain input from a broad array of
stakeholders on AI opportunities,
challenges and related issues in
transportation pursuant to Executive
Order (E.O.) 14110 of October 30, 2023
entitled ‘‘Safe, Secure, and Trustworthy
Development and Use of Artificial
Intelligence’’.
DATES: Written submissions must be
received within 60 days of the
publication of this RFI.
ADDRESSES: Please submit any written
comments to Docket Number DOT–
OST–2024–0049 electronically through
the Federal eRulemaking Portal at
https://regulations.gov. Go to https://
regulations.gov and select ‘‘Department
of Transportation (DOT)’’ from the
agency menu to submit or view public
comments. Note that, except as
provided below, all submissions
received, including any personal
information provided, will be posted
without change and will be available to
the public on https://
www.regulations.gov. You may review
DOT’s complete Privacy Act Statement
in the Federal Register published on
April 11, 2000 (65 FR 19477) or at
https://www.transportation.gov/privacy.
FOR FURTHER INFORMATION CONTACT: For
questions about this RFI, please email
ARPA-I@dot.gov. You may also contact
Mr. Timothy A. Klein, Director,
Technology Policy and Outreach, Office
of the Assistant Secretary for Research
and Technology (202–366–0075) or by
email at timothy.klein@dot.gov.
SUPPLEMENTARY INFORMATION: Advances
in artificial intelligence (AI) bring
significant potential benefits and risks,
and they have the potential to transform
American society with deep
implications for safety, access, equity
and resilience in the transportation
sector. Virtually all aspects of
transportation and mobility—from the
design, construction, operation, and
maintenance of physical infrastructure
systems to the operation of the digital
infrastructure that underpins and
enables the movement of people and
goods—will likely be impacted by the
deployment of AI tools and
applications.Beyond the direct impact
of the technology itself, AI has the
potential to reshape how individuals,
communities, corporations,
governments, and other users interact
with the transportation network in ways
that are difficult to anticipate. In
recognition of AI’s rapidly evolving
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capabilities and implications across all
facets of government, society and our
economy, the Biden Administration
issued Executive Order (E.O.) 14110 on
Safe, Secure, and Trustworthy
Development and Use of Artificial
Intelligence on October 30, 2023. In
section 8, ‘‘Protecting Consumers,
Patients, Passengers, and Students’’,
under Sub-section (c), the E.O. directs
the U.S. Department of Transportation
to ‘‘promote the safe and responsible
development and use of AI in the
transportation sector, in consultation
with relevant agencies’’. Paragraph (iii)
under sub-section (c) further requires
that ARPA–I ‘‘explore the
transportation-related opportunities and
challenges of AI—including regarding
software-defined AI enhancements
impacting autonomous mobility
ecosystems’’.
This RFI seeks information that will
assist ARPA–I and the U.S. Department
of Transportation (DOT) in carrying out
their responsibilities under section 8
(c)(iii) of E.O. 14110 noted above.
About ARPA–I
The Advanced Research Projects
Agency—Infrastructure (ARPA–I) is an
agency within DOT (see https://
www.transportation.gov/arpa-i) that
Congress established ‘‘to support the
development of science and technology
solutions that overcomes long-term
challenges and advances the state of the
art for United States transportation
infrastructure.’’ (Pub. L. 117–58, section
25012, November 15, 2021; 49 U.S.C.
119). ARPA–I is modeled after the
Defense Advanced Research Projects
Agency (DARPA) within the U.S.
Department of Defense and the
Advanced Research Projects AgencyEnergy (ARPA–E) within the U.S.
Department of Energy. ARPA–I offers a
once-in-a-generation opportunity to
improve our nation’s transportation
infrastructure, both physical and digital,
and supports DOT’s strategic goals of
Safety, Economic Strength and Global
Competitiveness, Equity, Climate and
Sustainability, and Transformation.
ARPA–I focuses on developing and
implementing technologies, rather than
developing policies and processes or
providing regulatory support. ARPA–I
has a single overarching goal and focus:
to fund external innovative advanced
research and development (R&D)
programs that develop new
technologies, systems, and capabilities
to improve transportation infrastructure
in the United States.
The aims of ARPA–I include
‘‘lowering the long-term costs of
infrastructure development, including
costs of planning, construction, and
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maintenance; reducing the lifecycle
impacts of transportation infrastructure
on the environment, including through
the reduction of greenhouse gas
emissions; contributing significantly to
improving the safe, secure, and efficient
movement of goods and people;
promoting the resilience of
infrastructure from physical and cyber
threats; and ensuring that the United
States is a global leader in developing
and deploying advanced transportation
infrastructure technologies and
materials.’’ (Pub. L. 117–58, section
25012, November 15, 2021; 49 U.S.C.
119). Funding the development and use
of AI technologies to address these
challenges is expected to be a key future
activity of ARPA–I.
Federal Activities on AI Most Closely
Related to DOT’s Work
E.O. 14110 directs agencies all across
government, including the Department
of Transportation, to take a wide range
of actions that will help ensure the
United States leads the way in seizing
AI’s promise and managing its risks.
This work includes actions to manage
AI’s safety and security risks, promote
innovation and competition, advance
equity and civil rights, protect
Americans’ privacy, stand up for
consumers and workers, and more.
Beyond E.O. 14110, the Federal
Government has also fostered and
funded work to advance the responsible
development of AI and machine
learning (ML) for decades. Examples of
such work range from early work
conducted by the Department of
Defense’s Advanced Research Projects
Agency (now DARPA) to ongoing efforts
summarized in the 2023 Update to the
National Artificial Intelligence Research
and Development Strategic Plan, led by
the White House Office of Science and
Technology Policy (OSTP).
In general, Federal investments in and
other support for basic and applied
research in AI in transportation are
critical to achieving national priorities
and build on applied AI research across
the Federal government. Foundational
research into and application of AI has
been supported by the National Science
Foundation (NSF), the Department of
Defense (DOD), the Department of
Energy (DOE), the Department of
Homeland Security (DHS) Cybersecurity
and Infrastructure Security Agency
(CISA), the National Institute of
Standards and Technology (NIST), and
the National Aeronautics and Space
Administration (NASA). Ongoing AI
research at these agencies with high
relevance to DOT priorities include
developing effective methods for
human-AI collaboration, ensuring the
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36849
safety and security of AI-based systems,
developing shared public datasets and
environments for AI training and
testing, measuring, and evaluating AIbased systems through standards and
benchmarks.
DOT Activities on AI
AI approaches are being applied to a
range of activities and efforts across
DOT; this section provides a brief, noncomprehensive overview.
Operating administrations within
DOT have developed and implemented
many uses of AI. These range from use
of AI and ML technologies to streamline
transportation operations (e.g., weather
prediction, routing and scheduling,
transit automation), to research projects
addressing safety (e.g., driver behavior
classification, passenger safety, incident
risk assessment, grade crossing safety
video analytics), to tools for rapid
analysis of text and component
schematic data submissions, and to
perform real-time asset management to
maintain a state of good repair. AI and
ML tools may have applications across
all of DOT’s operating administrations,
with many actively exploring uses
including the Federal Aviation
Administration (FAA), Federal Highway
Administration (FHWA), Federal Motor
Carrier Safety Administration (FMCSA),
Federal Railroad Administration (FRA),
Federal Transit Administration (FTA),
Great Lakes St. Lawrence Seaway
Development Corporation (GLS),
National Highway Traffic Safety
Administration (NHTSA), Maritime
Administration (MARAD), and Pipeline
and Hazardous Materials Safety
Administration (PHMSA).
The Intelligent Transportation System
Joint Program Office (ITS JPO) within
DOT has established the AI for ITS
Program, recognizing the promise that
AI offers for achieving significant
benefits in transportation safety,
mobility, efficiency, equity,
accessibility, productivity, and
resilience, while achieving reductions to
individual and societal costs, emissions,
and other negative environmental
impacts. Currently, ITS JPO is
developing AI-enabled ITS Capability
Maturity Model and Readiness
Checklists, and the Application of the
NIST AI Risk Management Framework
for ITS. ITS JPO published a review of
AI for ITS in October 2022.
Two DOT initiatives that include the
application of AI to serve the
Department’s policy priorities are being
led by the Office of the Assistant
Secretary for Research and Technology
(OST–R). The U.S. DOT Intersection
Safety Challenge (https://its.dot.gov/
isc/) is a prize-based competition that is
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exploring how a combination of
advanced sensing, perception, path
planning and prediction, and AI-based
decision making can help to improve
intersection safety for vulnerable road
users. The Complete Streets Artificial
Intelligence (CSAI) Small Business
Innovative Research (SBIR) program
(https://its.dot.gov/csai/) is a multiphase effort to develop powerful new
decision-support tools for public
agencies to assist in the siting, design,
and deployment of streets and road
networks that prioritize safety,
efficiency, and connectivity.
Additional AI-related activities at
OST–R include extramural research
conducted at a number of University
Transportation Centers, work at the
Highly Automated Systems Safety
Center of Excellence, technology
demonstration projects through the
SMART Grants Program, and research at
the U.S. DOT Volpe Center.
Similarly, consistent with E.O. 14110,
the Department’s internal NonTraditional and Emerging
Transportation Technology (NETT)
Council has work underway to identify
use cases across the various operating
administrations and share observations
and potential implications for the use of
AI throughout the existing
transportation system. Finally, the
Transforming Transportation Advisory
Committee (TTAC) and the Advanced
Aviation Advisory Committee (AAAC)
have been directed by Secretary
Buttigieg to provide insights on the
Department’s approach to AI and make
recommendations for this technology’s
integration into operational
advancements, in a manner that
anticipates AI’s benefits, while
safeguarding against its negative
impacts.
Potential Development and Uses of AI
in Transportation
This section provides illustrative use
cases to help respondents to this RFI
consider the breadth of potential uses of
AI in transportation, including physical
infrastructure, digital infrastructure,
operations, and many other aspects.
Many of the fundamental components
of AI technologies and AI tools
developed in other domains will be
directly applicable to AI in
transportation, from algorithmic
advances, foundational model
development, machine learning, deep
learning techniques, and AI assurance
methods to methods for ensuring
cybersecurity, model transparency and
trustworthiness.
As the Federal government has
emphasized, there are substantial
ethical, legal, and societal risks and
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potential adverse effects surrounding
the application of AI across society.
Minimizing risks and adverse effects
through developing trustworthy AI and
enhancing trust in human-AI
interactions, reducing bias in data,
protecting privacy, and developing
robust AI systems, standards, and
frameworks will be integral to ensuring
the effective incorporation of these new
technologies into transportation and
mobility systems.
This RFI employs the meaning of
‘‘artificial intelligence’’ or ‘‘AI’’ as used
in E.O. 14110 and set forth in 15 U.S.C.
9401(3): ‘‘a machine-based system that
can, for a given set of human-defined
objectives, make predictions,
recommendations, or decisions
influencing real or virtual
environments. Artificial intelligence
systems use machine- and human-based
inputs to perceive real and virtual
environments; abstract such perceptions
into models through analysis in an
automated manner; and use model
inference to formulate options for
information or action.’’ ARPA–I defines
‘‘Digital Infrastructure’’ as the sensing,
computation, automation, networking,
connectivity, data management,
analysis, optimization, control and
virtual elements that underpin our
physical transportation infrastructure.
Beyond transportation-specific use
cases, AI also has the potential to
increase operational efficiencies for
DOT’s own internal core business,
regulatory, and permitting functions,
including such applications as
analyzing consumer complaints,
compiling and summarizing public
comments, streamlining permitting and
application processes and more.
Potential areas for funded AI research
and development at DOT will span all
modes of transportation and mobility
and could include:
• Enhancing the safety of pedestrians
and vulnerable road users at roadway
intersections through technologies such
as ML and deep learning for computer
vision, perception, sensor fusion, realtime decision making and warning
systems,
• Real-time AI-based decision
support tools, optimization and control
of wide area traffic systems and transit
operations,
• Autonomous mobility systems and
vehicles on roads and rails, in the air,
and on water (AI-intensive computation
hardware and its design are beyond the
scope of this RFI),
• Optimization of road traffic
management systems and signalized
intersections in cities and towns across
timescales from seconds or minutes to
hours, including such elements as
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variable speed limit control, queue
detection and prediction, and wrongway driving detection,
• Optimization of equitable curb
management in urban areas,
• Transportation systems
management and operations (TSMO)
optimization and control,
• Use of AI to assess traveler behavior
and preferences across modes,
• Real-time monitoring of transit rail
systems for maintenance assessment
and state of good repair,
• Real-time monitoring of transit
facilities for incident risk analysis,
• Air traffic control optimization for
large-scale aviation operations
facilitated by AI,
• Development and operation of
secure complementary position,
navigation, and timing (PNT) systems
using AI-based recognition and
utilization of signals of opportunity,
• AI assessment and assurance tools,
methods and frameworks, benchmarks,
testing environments, validation and
verification, and the creation of datasets
for AI and AI-enabled systems across all
modes of transportation,
• Automating and digitizing physical
infrastructure asset management
through AI to optimize planning,
design, operations, construction, and
maintenance, and end of life,
• Optimizing planning, design, build
and permitting for infrastructure
construction and repair, and reducing
construction costs by incorporating best
practices developed through generative
AI, including natural language
processing (NLP) and large language
model (LLM)-based processing of
existing knowledge and databases,
• Sensor output processing, sensor
fusion, data analysis, and ML for
analysis and control of large-scale
transportation networks and systems,
including remote sensing,
• Real-time control and optimization
of traffic networks and signalization
from the local scale to a full city or
region,
• Optimization of multimodal freight
and logistics networks and supply
chains nationally, including commercial
vehicle, marine, rail and aviation freight
and logistics systems,
• Safe operation of uncrewed air
systems (UAS) in emerging aviation
applications,
• Developing shared mobility-ondemand (MOD) services, from AI-based
dynamic route scheduling and fleet
optimization for city or region-wide
passenger demand using traveler
decision support tools,
• Offline analysis of traffic data,
transportation safety data, and
emissions inventories,
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• Enhancing mapping and spatial AI
for real-time automation and navigation
across all modes, as well as for
infrastructure design, maintenance, and
repair,
• AI-based robotic repair and
repurposing of pipeline infrastructure,
and
• AI-enhanced robotic mapping of
sub-surface infrastructure and utilities
for safe, efficient, and cost-effective ‘‘dig
once’’ construction.
Specific Questions
This RFI seeks information that will
assist ARPA–I and the U.S. Department
of Transportation in carrying out
responsibilities under section 8 (c)(iii)
of E.O. 14110, as noted above.
DOT is providing the following
specific questions to prompt feedback
and comments. DOT encourages public
comment on any of these questions and
seeks any other information commenters
believe is relevant.
DOT is requesting information from
all interested entities and stakeholders,
including innovators and technology
developers, researchers and universities,
transportation system and infrastructure
owners and operators, transportationfocused groups, organizations and
associations, and the public. Where
appropriate, responses should include
discussion of real-world applications
and actual examples of AI technologies,
tools, and methods currently being used
or contemplated for future use in the
transportation and mobility domain.
DOT is interested in receiving
succinct and relevant responses to some
or all of the following questions,
keeping in mind the current efforts and
potential use cases as described above:
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Question 1: Current AI Applications in
Transportation
What are the relevant current or nearterm applications of AI in
transportation? If applicable, describe
the mode(s) of transportation that these
applications cover, referencing DOT’s
stated priorities (including safety,
climate and sustainability, equity,
economic strength and global
competitiveness, and transformation)
that these applications support.
Question 2: Opportunities of AI in
Transportation
What are the future potential
opportunities in transportation that AI
can facilitate? Describe the mode(s) of
transportation that these opportunities
cover, referencing DOT’s stated
priorities (including safety, climate and
sustainability, equity, economic strength
and global competitiveness, and
transformation) as appropriate.
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Question 3: Challenges of AI in
Transportation
What are the current or future
challenges of AI in transportation,
including risks presented by the use of
AI in transportation and potential
barriers to its responsible adoption?
Describe the mode(s) of transportation
that these challenges cover, referencing
DOT’s stated priorities (including safety,
climate and sustainability, equity,
economic strength and global
competitiveness, and transformation) as
appropriate.
Question 4: Autonomous Mobility
Ecosystems
What are the opportunities,
challenges, and risks of AI related to
autonomous mobility ecosystems,
including software-defined AI
enhancements? Describe how AI can
responsibly facilitate autonomous
mobility, including specifically safety
considerations.
Question 5: Other Considerations in the
Development of AI for Transportation
Comment on any other considerations
relevant to the development, challenges,
and opportunities of AI in
transportation that have not been
included in the questions above. These
considerations may include ones such
as potential priorities in transportationspecific future AI R&D funding, access
to transportation datasets, the
development of AI testbeds, physical
and digital infrastructure needs and
requirements, and workforce training
and education.
Confidential Business Information
Do not submit information disclosure
of which is restricted by statute, such as
trade secrets and commercial or
financial information (hereinafter
referred to as Confidential Business
Information ‘‘CBI’’) to Regulations.gov.
Comments submitted through
Regulations.gov cannot be claimed as
CBI. Comments received through the
website will waive any CBI claims for
the information submitted.
Issued in Washington, DC, on April 26,
2024.
Robert C. Hampshire,
Principal Deputy Assistant Secretary for
Research and Technology and Chief Science
Officer.
[FR Doc. 2024–09645 Filed 5–2–24; 8:45 am]
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36851
DEPARTMENT OF THE TREASURY
Privacy Act of 1974; System of
Records
Internal Revenue Service,
Department of the Treasury.
ACTION: Notice of a new system of
records.
AGENCY:
In accordance with the
Privacy Act of 1974, as amended
(Privacy Act), the Department of the
Treasury, Internal Revenue Service
(IRS), proposes to establish a new
system of records entitled, ‘‘Treasury/
IRS 34.018, Insider Risk Management
Records,’’ within its inventory of
records systems subject to the Privacy
Act. The IRS will use this system to
identify potential threats to IRS
resources and information assets and
facilitate management of insider threat
investigations, complaints, inquiries,
and counterintelligence threat detection
activities. An ‘‘insider’’ is defined to
include current and former employees,
contractors, interns, visitors, and any
other individuals who have or who had
persistent authorized access to IRS
assets including any IRS facility,
information, equipment, network, or
system. An ‘‘insider threat’’ is the threat
that an insider will use his or her
authorized access, wittingly or
unwittingly, to do harm to the IRS
mission, resources, personnel, facilities,
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systems.
SUMMARY:
Comments must be received no
later than June 3, 2024. This new system
of records will be effective upon
publication in the Federal Register
unless the IRS receives comments
which would result in a contrary
determination. The routine uses will be
effective on June 3, 2024. The IRS
invites written comments on the routine
uses and other aspects of this system of
records prior to the proposed effective
date.
DATES:
Comments may be
submitted to the Federal eRulemaking
Portal electronically at https://
www.regulations.gov identified by
docket number TREAS–DO–2024–0003.
Comments can also be sent to the
Deputy Assistant Secretary for Privacy,
Transparency, and Records, Department
of the Treasury, 1500 Pennsylvania
Avenue NW, Washington, DC 20220,
Attention: New Privacy Act Systems of
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supporting documents, are part of the
public record and subject to public
disclosure. All comments received will
be posted without change to
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[Federal Register Volume 89, Number 87 (Friday, May 3, 2024)]
[Notices]
[Pages 36848-36851]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2024-09645]
-----------------------------------------------------------------------
DEPARTMENT OF TRANSPORTATION
[Docket No. DOT-OST-2024-0049]
Opportunities and Challenges of Artificial Intelligence (AI) in
Transportation; Request for Information
AGENCY: Department of Transportation (DOT)
ACTION: Notice; Request for Information (RFI).
-----------------------------------------------------------------------
SUMMARY: The U.S. Department of Transportation's Advanced Research
Projects Agency--Infrastructure (ARPA-I) is seeking input from
interested parties on the potential applications of artificial
intelligence (AI) in transportation, as well as emerging challenges and
opportunities in creating and deploying AI technologies in applications
across all modes of transportation. The purpose of this Request for
Information (RFI) is to obtain input from a broad array of stakeholders
on AI opportunities, challenges and related issues in transportation
pursuant to Executive Order (E.O.) 14110 of October 30, 2023 entitled
``Safe, Secure, and Trustworthy Development and Use of Artificial
Intelligence''.
DATES: Written submissions must be received within 60 days of the
publication of this RFI.
ADDRESSES: Please submit any written comments to Docket Number DOT-OST-
2024-0049 electronically through the Federal eRulemaking Portal at
https://regulations.gov. Go to https://regulations.gov and select
``Department of Transportation (DOT)'' from the agency menu to submit
or view public comments. Note that, except as provided below, all
submissions received, including any personal information provided, will
be posted without change and will be available to the public on https://www.regulations.gov. You may review DOT's complete Privacy Act
Statement in the Federal Register published on April 11, 2000 (65 FR
19477) or at https://www.transportation.gov/privacy.
FOR FURTHER INFORMATION CONTACT: For questions about this RFI, please
email [email protected]. You may also contact Mr. Timothy A. Klein,
Director, Technology Policy and Outreach, Office of the Assistant
Secretary for Research and Technology (202-366-0075) or by email at
[email protected].
SUPPLEMENTARY INFORMATION: Advances in artificial intelligence (AI)
bring significant potential benefits and risks, and they have the
potential to transform American society with deep implications for
safety, access, equity and resilience in the transportation sector.
Virtually all aspects of transportation and mobility--from the design,
construction, operation, and maintenance of physical infrastructure
systems to the operation of the digital infrastructure that underpins
and enables the movement of people and goods--will likely be impacted
by the deployment of AI tools and applications.Beyond the direct impact
of the technology itself, AI has the potential to reshape how
individuals, communities, corporations, governments, and other users
interact with the transportation network in ways that are difficult to
anticipate. In recognition of AI's rapidly evolving
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capabilities and implications across all facets of government, society
and our economy, the Biden Administration issued Executive Order (E.O.)
14110 on Safe, Secure, and Trustworthy Development and Use of
Artificial Intelligence on October 30, 2023. In section 8, ``Protecting
Consumers, Patients, Passengers, and Students'', under Sub-section (c),
the E.O. directs the U.S. Department of Transportation to ``promote the
safe and responsible development and use of AI in the transportation
sector, in consultation with relevant agencies''. Paragraph (iii) under
sub-section (c) further requires that ARPA-I ``explore the
transportation-related opportunities and challenges of AI--including
regarding software-defined AI enhancements impacting autonomous
mobility ecosystems''.
This RFI seeks information that will assist ARPA-I and the U.S.
Department of Transportation (DOT) in carrying out their
responsibilities under section 8 (c)(iii) of E.O. 14110 noted above.
About ARPA-I
The Advanced Research Projects Agency--Infrastructure (ARPA-I) is
an agency within DOT (see https://www.transportation.gov/arpa-i) that
Congress established ``to support the development of science and
technology solutions that overcomes long-term challenges and advances
the state of the art for United States transportation infrastructure.''
(Pub. L. 117-58, section 25012, November 15, 2021; 49 U.S.C. 119).
ARPA-I is modeled after the Defense Advanced Research Projects Agency
(DARPA) within the U.S. Department of Defense and the Advanced Research
Projects Agency-Energy (ARPA-E) within the U.S. Department of Energy.
ARPA-I offers a once-in-a-generation opportunity to improve our
nation's transportation infrastructure, both physical and digital, and
supports DOT's strategic goals of Safety, Economic Strength and Global
Competitiveness, Equity, Climate and Sustainability, and
Transformation. ARPA-I focuses on developing and implementing
technologies, rather than developing policies and processes or
providing regulatory support. ARPA-I has a single overarching goal and
focus: to fund external innovative advanced research and development
(R&D) programs that develop new technologies, systems, and capabilities
to improve transportation infrastructure in the United States.
The aims of ARPA-I include ``lowering the long-term costs of
infrastructure development, including costs of planning, construction,
and maintenance; reducing the lifecycle impacts of transportation
infrastructure on the environment, including through the reduction of
greenhouse gas emissions; contributing significantly to improving the
safe, secure, and efficient movement of goods and people; promoting the
resilience of infrastructure from physical and cyber threats; and
ensuring that the United States is a global leader in developing and
deploying advanced transportation infrastructure technologies and
materials.'' (Pub. L. 117-58, section 25012, November 15, 2021; 49
U.S.C. 119). Funding the development and use of AI technologies to
address these challenges is expected to be a key future activity of
ARPA-I.
Federal Activities on AI Most Closely Related to DOT's Work
E.O. 14110 directs agencies all across government, including the
Department of Transportation, to take a wide range of actions that will
help ensure the United States leads the way in seizing AI's promise and
managing its risks. This work includes actions to manage AI's safety
and security risks, promote innovation and competition, advance equity
and civil rights, protect Americans' privacy, stand up for consumers
and workers, and more. Beyond E.O. 14110, the Federal Government has
also fostered and funded work to advance the responsible development of
AI and machine learning (ML) for decades. Examples of such work range
from early work conducted by the Department of Defense's Advanced
Research Projects Agency (now DARPA) to ongoing efforts summarized in
the 2023 Update to the National Artificial Intelligence Research and
Development Strategic Plan, led by the White House Office of Science
and Technology Policy (OSTP).
In general, Federal investments in and other support for basic and
applied research in AI in transportation are critical to achieving
national priorities and build on applied AI research across the Federal
government. Foundational research into and application of AI has been
supported by the National Science Foundation (NSF), the Department of
Defense (DOD), the Department of Energy (DOE), the Department of
Homeland Security (DHS) Cybersecurity and Infrastructure Security
Agency (CISA), the National Institute of Standards and Technology
(NIST), and the National Aeronautics and Space Administration (NASA).
Ongoing AI research at these agencies with high relevance to DOT
priorities include developing effective methods for human-AI
collaboration, ensuring the safety and security of AI-based systems,
developing shared public datasets and environments for AI training and
testing, measuring, and evaluating AI-based systems through standards
and benchmarks.
DOT Activities on AI
AI approaches are being applied to a range of activities and
efforts across DOT; this section provides a brief, non-comprehensive
overview.
Operating administrations within DOT have developed and implemented
many uses of AI. These range from use of AI and ML technologies to
streamline transportation operations (e.g., weather prediction, routing
and scheduling, transit automation), to research projects addressing
safety (e.g., driver behavior classification, passenger safety,
incident risk assessment, grade crossing safety video analytics), to
tools for rapid analysis of text and component schematic data
submissions, and to perform real-time asset management to maintain a
state of good repair. AI and ML tools may have applications across all
of DOT's operating administrations, with many actively exploring uses
including the Federal Aviation Administration (FAA), Federal Highway
Administration (FHWA), Federal Motor Carrier Safety Administration
(FMCSA), Federal Railroad Administration (FRA), Federal Transit
Administration (FTA), Great Lakes St. Lawrence Seaway Development
Corporation (GLS), National Highway Traffic Safety Administration
(NHTSA), Maritime Administration (MARAD), and Pipeline and Hazardous
Materials Safety Administration (PHMSA).
The Intelligent Transportation System Joint Program Office (ITS
JPO) within DOT has established the AI for ITS Program, recognizing the
promise that AI offers for achieving significant benefits in
transportation safety, mobility, efficiency, equity, accessibility,
productivity, and resilience, while achieving reductions to individual
and societal costs, emissions, and other negative environmental
impacts. Currently, ITS JPO is developing AI-enabled ITS Capability
Maturity Model and Readiness Checklists, and the Application of the
NIST AI Risk Management Framework for ITS. ITS JPO published a review
of AI for ITS in October 2022.
Two DOT initiatives that include the application of AI to serve the
Department's policy priorities are being led by the Office of the
Assistant Secretary for Research and Technology (OST-R). The U.S. DOT
Intersection Safety Challenge (https://its.dot.gov/isc/ isc/) is a prize-
based competition that is
[[Page 36850]]
exploring how a combination of advanced sensing, perception, path
planning and prediction, and AI-based decision making can help to
improve intersection safety for vulnerable road users. The Complete
Streets Artificial Intelligence (CSAI) Small Business Innovative
Research (SBIR) program (https://its.dot.gov/csai/) is a multi-phase
effort to develop powerful new decision-support tools for public
agencies to assist in the siting, design, and deployment of streets and
road networks that prioritize safety, efficiency, and connectivity.
Additional AI-related activities at OST-R include extramural
research conducted at a number of University Transportation Centers,
work at the Highly Automated Systems Safety Center of Excellence,
technology demonstration projects through the SMART Grants Program, and
research at the U.S. DOT Volpe Center.
Similarly, consistent with E.O. 14110, the Department's internal
Non-Traditional and Emerging Transportation Technology (NETT) Council
has work underway to identify use cases across the various operating
administrations and share observations and potential implications for
the use of AI throughout the existing transportation system. Finally,
the Transforming Transportation Advisory Committee (TTAC) and the
Advanced Aviation Advisory Committee (AAAC) have been directed by
Secretary Buttigieg to provide insights on the Department's approach to
AI and make recommendations for this technology's integration into
operational advancements, in a manner that anticipates AI's benefits,
while safeguarding against its negative impacts.
Potential Development and Uses of AI in Transportation
This section provides illustrative use cases to help respondents to
this RFI consider the breadth of potential uses of AI in
transportation, including physical infrastructure, digital
infrastructure, operations, and many other aspects.
Many of the fundamental components of AI technologies and AI tools
developed in other domains will be directly applicable to AI in
transportation, from algorithmic advances, foundational model
development, machine learning, deep learning techniques, and AI
assurance methods to methods for ensuring cybersecurity, model
transparency and trustworthiness.
As the Federal government has emphasized, there are substantial
ethical, legal, and societal risks and potential adverse effects
surrounding the application of AI across society. Minimizing risks and
adverse effects through developing trustworthy AI and enhancing trust
in human-AI interactions, reducing bias in data, protecting privacy,
and developing robust AI systems, standards, and frameworks will be
integral to ensuring the effective incorporation of these new
technologies into transportation and mobility systems.
This RFI employs the meaning of ``artificial intelligence'' or
``AI'' as used in E.O. 14110 and set forth in 15 U.S.C. 9401(3): ``a
machine-based system that can, for a given set of human-defined
objectives, make predictions, recommendations, or decisions influencing
real or virtual environments. Artificial intelligence systems use
machine- and human-based inputs to perceive real and virtual
environments; abstract such perceptions into models through analysis in
an automated manner; and use model inference to formulate options for
information or action.'' ARPA-I defines ``Digital Infrastructure'' as
the sensing, computation, automation, networking, connectivity, data
management, analysis, optimization, control and virtual elements that
underpin our physical transportation infrastructure. Beyond
transportation-specific use cases, AI also has the potential to
increase operational efficiencies for DOT's own internal core business,
regulatory, and permitting functions, including such applications as
analyzing consumer complaints, compiling and summarizing public
comments, streamlining permitting and application processes and more.
Potential areas for funded AI research and development at DOT will
span all modes of transportation and mobility and could include:
Enhancing the safety of pedestrians and vulnerable road
users at roadway intersections through technologies such as ML and deep
learning for computer vision, perception, sensor fusion, real-time
decision making and warning systems,
Real-time AI-based decision support tools, optimization
and control of wide area traffic systems and transit operations,
Autonomous mobility systems and vehicles on roads and
rails, in the air, and on water (AI-intensive computation hardware and
its design are beyond the scope of this RFI),
Optimization of road traffic management systems and
signalized intersections in cities and towns across timescales from
seconds or minutes to hours, including such elements as variable speed
limit control, queue detection and prediction, and wrong-way driving
detection,
Optimization of equitable curb management in urban areas,
Transportation systems management and operations (TSMO)
optimization and control,
Use of AI to assess traveler behavior and preferences
across modes,
Real-time monitoring of transit rail systems for
maintenance assessment and state of good repair,
Real-time monitoring of transit facilities for incident
risk analysis,
Air traffic control optimization for large-scale aviation
operations facilitated by AI,
Development and operation of secure complementary
position, navigation, and timing (PNT) systems using AI-based
recognition and utilization of signals of opportunity,
AI assessment and assurance tools, methods and frameworks,
benchmarks, testing environments, validation and verification, and the
creation of datasets for AI and AI-enabled systems across all modes of
transportation,
Automating and digitizing physical infrastructure asset
management through AI to optimize planning, design, operations,
construction, and maintenance, and end of life,
Optimizing planning, design, build and permitting for
infrastructure construction and repair, and reducing construction costs
by incorporating best practices developed through generative AI,
including natural language processing (NLP) and large language model
(LLM)-based processing of existing knowledge and databases,
Sensor output processing, sensor fusion, data analysis,
and ML for analysis and control of large-scale transportation networks
and systems, including remote sensing,
Real-time control and optimization of traffic networks and
signalization from the local scale to a full city or region,
Optimization of multimodal freight and logistics networks
and supply chains nationally, including commercial vehicle, marine,
rail and aviation freight and logistics systems,
Safe operation of uncrewed air systems (UAS) in emerging
aviation applications,
Developing shared mobility-on-demand (MOD) services, from
AI-based dynamic route scheduling and fleet optimization for city or
region-wide passenger demand using traveler decision support tools,
Offline analysis of traffic data, transportation safety
data, and emissions inventories,
[[Page 36851]]
Enhancing mapping and spatial AI for real-time automation
and navigation across all modes, as well as for infrastructure design,
maintenance, and repair,
AI-based robotic repair and repurposing of pipeline
infrastructure, and
AI-enhanced robotic mapping of sub-surface infrastructure
and utilities for safe, efficient, and cost-effective ``dig once''
construction.
Specific Questions
This RFI seeks information that will assist ARPA-I and the U.S.
Department of Transportation in carrying out responsibilities under
section 8 (c)(iii) of E.O. 14110, as noted above.
DOT is providing the following specific questions to prompt
feedback and comments. DOT encourages public comment on any of these
questions and seeks any other information commenters believe is
relevant.
DOT is requesting information from all interested entities and
stakeholders, including innovators and technology developers,
researchers and universities, transportation system and infrastructure
owners and operators, transportation-focused groups, organizations and
associations, and the public. Where appropriate, responses should
include discussion of real-world applications and actual examples of AI
technologies, tools, and methods currently being used or contemplated
for future use in the transportation and mobility domain.
DOT is interested in receiving succinct and relevant responses to
some or all of the following questions, keeping in mind the current
efforts and potential use cases as described above:
Question 1: Current AI Applications in Transportation
What are the relevant current or near-term applications of AI in
transportation? If applicable, describe the mode(s) of transportation
that these applications cover, referencing DOT's stated priorities
(including safety, climate and sustainability, equity, economic
strength and global competitiveness, and transformation) that these
applications support.
Question 2: Opportunities of AI in Transportation
What are the future potential opportunities in transportation that
AI can facilitate? Describe the mode(s) of transportation that these
opportunities cover, referencing DOT's stated priorities (including
safety, climate and sustainability, equity, economic strength and
global competitiveness, and transformation) as appropriate.
Question 3: Challenges of AI in Transportation
What are the current or future challenges of AI in transportation,
including risks presented by the use of AI in transportation and
potential barriers to its responsible adoption? Describe the mode(s) of
transportation that these challenges cover, referencing DOT's stated
priorities (including safety, climate and sustainability, equity,
economic strength and global competitiveness, and transformation) as
appropriate.
Question 4: Autonomous Mobility Ecosystems
What are the opportunities, challenges, and risks of AI related to
autonomous mobility ecosystems, including software-defined AI
enhancements? Describe how AI can responsibly facilitate autonomous
mobility, including specifically safety considerations.
Question 5: Other Considerations in the Development of AI for
Transportation
Comment on any other considerations relevant to the development,
challenges, and opportunities of AI in transportation that have not
been included in the questions above. These considerations may include
ones such as potential priorities in transportation-specific future AI
R&D funding, access to transportation datasets, the development of AI
testbeds, physical and digital infrastructure needs and requirements,
and workforce training and education.
Confidential Business Information
Do not submit information disclosure of which is restricted by
statute, such as trade secrets and commercial or financial information
(hereinafter referred to as Confidential Business Information ``CBI'')
to Regulations.gov. Comments submitted through Regulations.gov cannot
be claimed as CBI. Comments received through the website will waive any
CBI claims for the information submitted.
Issued in Washington, DC, on April 26, 2024.
Robert C. Hampshire,
Principal Deputy Assistant Secretary for Research and Technology and
Chief Science Officer.
[FR Doc. 2024-09645 Filed 5-2-24; 8:45 am]
BILLING CODE 4910-9X-P
