[Federal Register Volume 87, Number 179 (Friday, September 16, 2022)]
[Notices]
[Pages 57019-57022]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-20188]


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

DEPARTMENT OF TRANSPORTATION

[Docket No. DOT-OST-2022-0096]


Enhancing the Safety of Vulnerable Road Users at Intersections; 
Request for Information

AGENCY: Department of Transportation (DOT).

ACTION: Notice; request for information (RFI).

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

SUMMARY: Improving the safety of pedestrians, bicyclists, and other 
vulnerable road users (VRUs) is of critical importance to achieving the 
objectives of DOT's National Roadway Safety Strategy (NRSS), and DOT's 
vision of zero fatalities and serious injuries across our 
transportation system. According to data from the National Highway 
Traffic Safety Administration (NHTSA), in 2020 there were 10,626 
traffic fatalities in the United States at roadway intersections, 
including 1,674 pedestrian and 355 bicyclist fatalities. These 
fatalities at intersections represent 27% of the total of 38,824 road 
traffic deaths recorded in 2020. Separately, considerable development 
efforts have been made into automation technologies over the past two 
decades, including in the areas of vehicle automation, machine vision, 
perception and sensing, vehicle-to-everything (V2X) communications, 
sensor fusion, image and data analysis, artificial intelligence (AI), 
path planning, and real-time decision-making. DOT is interested in 
receiving comments on the possibility of adapting existing and emerging 
automation technologies to accelerate the development of real-time 
roadway intersection safety and warning systems for both drivers and 
VRUs in a cost-effective manner that will facilitate deployment at 
scale.

DATES: Written submissions must be received within 30 days of the 
publication of this RFI. DOT will consider comments received after this 
time period to the extent practicable.

ADDRESSES: Please submit any written comments to Docket Number DOT-OST-
2022-0096 electronically through the Federal eRulemaking Portal at 
https://www.regulations.gov. Go to https://www.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 further information contact 
[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: DOT is committed to the vision of zero 
fatalities and serious injuries on our Nation's roadways, and improving 
the safety of vulnerable road users (VRUs) at intersections is an 
important component of that vision. According to data from NHTSA, in 
2020 there were 10,626 traffic fatalities in the United States at 
intersections, including 1,674 pedestrians and 355 bicyclists. These 
fatalities at intersections represent 27% of the total of 38,824 road 
traffic deaths recorded in 2020. Ensuring VRU safety is an urgent issue 
as it is essential to allowing pedestrians, bicyclists, wheelchair 
users, and others the safe use of roadways in urban and rural 
environments in the United States. Reducing crashes at roadway 
intersections is an important component of making our streets safer for 
all users.
    Considerable development efforts have occurred in automation and 
vehicle automation technologies over the past two decades, including in 
the areas of machine vision, perception and sensing, vehicle-to-
everything (V2X) communications, sensor fusion, image and data 
analysis, artificial intelligence (AI), path planning, and real-time 
decision-making on board vehicles. For the purposes of this RFI, these 
automation technologies are considered to include but are not limited 
to advanced driver assistance systems (ADAS), automated driving systems 
(ADS) and associated vehicle connectivity technologies, as well as 
other automation technologies that can enhance the safety of VRUs at 
roadway intersections. DOT is interested in receiving comments on the 
feasibility of adapting these automation technologies to the 
application of warning systems that provide real-time safety and 
warning alerts for both VRUs and drivers at intersections in a cost-
effective manner that will facilitate the deployment of these systems 
at scale. Such safety systems could warn of and mitigate the effects of 
an impending crash at an intersection for VRUs and vehicles alike.

A Conceptual VRU and Vehicle Warning System

    The on-vehicle automation technologies currently being developed 
for fully automated vehicle operation--including machine vision, 
perception, sensor fusion, real-time decision-

[[Page 57020]]

making, artificial intelligence and V2X--could be used today to enhance 
safety for all road users. Consider the deployment of these 
technologies as infrastructure assets at each roadway intersection, 
pedestrian crossing, and railroad crossing, in order to alert 
approaching vehicles of the approach or incursion of pedestrians, 
bicyclists and other VRUs, and vice versa. A conceptual VRU and vehicle 
warning system will likely be made up of fixed infrastructure assets 
that use robust sensing and computational technologies to perform 
optimally across a range of environmental and operational conditions, 
including non-line-of-sight (NLOS) conditions. The conceptual 
intersection safety system that is described in this RFI should not be 
considered as prescriptive, but merely one potential configuration 
amongst many possible designs.
    At busy roadway intersections across any particular time period 
there will be a large number of vehicle and VRU movements, including 
vehicles turning, pedestrians crossing the roadway, bicyclists crossing 
the roadway, etc. For the majority of these movements, including those 
that involve close interaction between drivers and VRUs, the vehicle-
VRU interaction will proceed without incident. A small fraction of 
those interactions might involve near-misses where a vehicle comes 
close to colliding with another vehicle or a VRU at a roadway 
intersection. A much smaller fraction of those interactions results in 
a collision between vehicles and VRUs, resulting in injury or in a 
smaller fraction yet, an entirely avoidable pedestrian or VRU fatality. 
It is the intent of this RFI to investigate the possibility of 
developing new technologies, or new technology and/or system 
combinations, to prevent vehicle-VRU crashes while facilitating normal 
traffic flows and VRU movements.
    For the purposes of this RFI, VRUs are defined as pedestrians, 
bicyclists, and micro-mobility device users, including users of 
scooters, e-skateboards, wheelchairs, etc. Vehicles are defined as any 
roadway vehicles including passenger cars, trucks, vans, public transit 
buses, and commercial vehicles. Equipping each roadway intersection 
location today with the requisite machine vision hardware, 
computational capability, networking, communications, and safety 
alerting and warning technology would likely cost hundreds of thousands 
of dollars per roadway intersection. While this concept of repurposing 
mobile (vehicle) automation technologies in the fixed domain is not 
new, it has not been commercialized or implemented at scale due to the 
high system costs involved and the complexities of developing a 
standardized and proven safety solution. There is an imperative to 
reduce the cost of providing advanced safety systems that can ensure 
the safety of all road users at roadway intersections, pedestrian 
crossings, trail-roadway crossings, and railroad crossings. A cost 
reduction of 10-100x for such a system--down to under $10,000 for the 
hardware and software ``stack'' per intersection--would significantly 
accelerate the implementation and deployment of these potentially life-
saving road safety technologies. As an example of the potential of cost 
reduction in an adjacent domain, LiDAR units for automated vehicles 
(AVs) have seen a 100x reduction in cost while progressing from large 
roof-mounted electro-mechanical systems to smaller solid-state devices.
    An effective roadway intersection safety system (designated here as 
a ``conceptual VRU and vehicle warning system'') will likely require 
machine vision, perception or sensing (LiDAR, radar, cameras, acoustics 
etc. mounted on stationary structures), sensor fusion, computation, 
communications, and warning systems to be developed, tested and 
validated, and integrated along with software for vision, sensing, and 
decision-making (to include AI). The intention of this RFI is to 
ascertain the state of the art of relevant automation technologies, and 
the potential for re-purposing existing and emerging technologies for 
this stationary intersection safety application. The reduction in the 
cost of these life-saving systems by a factor of 10-100x through the 
targeted application of automation technologies would allow for the 
development of a new, standardized VRU warning system that could 
significantly benefit system end-users, including State, local, Tribal 
and territorial DOTs and jurisdictions.

Additional Considerations for a VRU and Vehicle Warning System

    The development of an automated VRU and vehicle warning system 
should incorporate the use of existing standards and protocols to the 
greatest extent possible. System-to-vehicle and vehicle-to-system 
communications and networking (V2X), using standard and emerging 
protocols, will likely be required (note that ``system'' here can 
include fixed infrastructure elements or communication with portable 
devices). For instance, smart mobile phone notifications for either 
VRUs or approaching vehicles using near-field communications (such as 
Bluetooth) might be a useful additional warning technology, beyond 
other alerting systems, but the use of smart electronic devices by VRUs 
should not be a requirement for the efficacy of an intersection safety 
system. Virtual machine vision systems incorporating ``crowd-sourced'' 
vehicle-based real-time imaging and information sharing (moving and 
parked vehicles) could also be of use. Ensuring night-time, low light, 
and reduced visibility (e.g., fog, rain, snow) operation will be 
critical for such an intersection safety system. It is anticipated that 
developers of VRU and vehicle warning systems will benefit from the 
collection of large amounts of data and imagery from the operation of a 
real-world roadway intersection to develop vision systems and train 
machine learning (ML) algorithms. This data could be developed and 
shared to accelerate the parallel development of effective solutions.
    Important considerations for any intersection safety technology 
include its efficacy of operation while not degrading existing levels 
of safety or traffic operation; its ability to be implemented and 
deployed at scale; the system cost; consistent and reliable system 
operation and performance; operation under all weather, lighting and 
environmental conditions; reliability and maintenance requirements; 
personnel and training requirements; ease of deployment; ease of 
calibration and customization at a specific intersection location; its 
potential for rapid commercialization and deployment within 3-5 years; 
upgradeability and modularity, and interoperability and data transfer 
capability with existing signal operating systems and traffic 
management systems, while avoiding technological lock-in.
    It is not anticipated that a single technical solution or system 
will be suitable for implementation at all roadway intersections, but 
it is anticipated that a single solution can be developed that will 
suit a large proportion of the most crash-prone intersections. These 
technologies may also serve to enhance the use of Data-Driven Safety 
Analysis (DDSA) techniques that can inform State, local, Tribal, and 
territorial DOTs in their decision making, and allow them to target the 
implementation of infrastructure investments that improve safety and 
equity. Once deployed in multiple locations, real-time data sharing 
between adjacent or neighboring intersection safety systems could 
further improve the safety of local road networks.

[[Page 57021]]

General Considerations for the Development of a VRU and Vehicle Warning 
System

    First, the addition of a VRU and vehicle warning system should not 
degrade the baseline performance of any existing intersection. It is 
acknowledged that a hardware and software-based intersection safety 
system may have significant additional `soft' costs beyond the cost of 
construction (or bill of materials for its constituent components)--
permitting, installation, testing, calibration, operation (although 
operation should be fully automated), training, maintenance, 
integration with other existing systems, R&D costs, etc. A VRU and 
vehicle warning system should ideally leverage existing components, 
systems and technologies to the greatest extent possible (including 
open, interoperable communications to maximize the accessibility and 
safety benefits), should meet all applicable Federal and State 
standards, should be suitable both for new installations and retrofits, 
and its software should use transparent non-opaque algorithms. Any 
system installation, use, operation, and maintenance should be 
expeditious and minimally disruptive to the road users. It is 
anticipated that determining the performance of any intersection safety 
system will require extensive testing in both benign and extreme 
environments, including for electromagnetic compatibility, and will 
probably require extensive data collection for overall system 
development, testing, validation and calibration.

System Components and Hardware and Software Technologies--A Conceptual 
Design

    A conceptual design for a VRU and vehicle intersection safety 
system would likely require the following elements and would probably 
need to account for the associated features or considerations (these 
potential design elements should not be considered to be prescriptive, 
but merely representative of the current state of the art):
     Sensing and perception. A perception system will likely 
require machine vision that includes cameras, LiDAR and radar that 
provide a full field of view under all lighting and weather conditions, 
and to provide redundancy. The resolution, bandwidth, latency, power 
consumption, and cost considerations of the vision and perception 
system will be important.
     Sensor fusion, image and data analysis. This will likely 
require high computational throughput (of the order of gigapixels per 
second), and should utilize industry-standard computational and 
networking bus architectures. The real-time image and data analysis 
should sense the movement of individual VRUs and vehicles, and be 
capable of inferring intent. Privacy protections should be maintained, 
and precise timing (derived from global navigation satellite systems 
[GNSS] or secondary or back-up sources that can be space- or land-
based) should be used. It is likely that the sensor fusion, image, and 
data analysis will require significant levels of AI (and ML) capability 
and be capable of high gigabit per second data throughputs.
     Path planning and prediction. The discrete paths of motion 
of all vehicles and VRUs in or near the intersection (perhaps as many 
as twenty or more items of interest) will likely need to be tracked and 
predicted simultaneously in order to determine potential or impending 
vehicle-VRU conflicts. This computation, logic and decision-making will 
likely need to be performed by a high bandwidth, low latency, high 
speed microprocessor-based system located at the intersection (perhaps 
in a roadside unit, or RSU). The real-time decision-making process will 
need to result in an ``alert or no alert/warning or no warning'' output 
that minimizes false positives and false negatives while ultimately 
providing safe and actionable warnings to the VRUs and/or approaching 
vehicles.
     Data handling and storage. Large quantities of data 
(potentially terabytes of data per day per intersection) may be 
required to be stored and archived, with attention paid to 
anonymization, privacy, and cybersecurity threats. This will likely 
include local storage as well as cloud- or edge-based archiving.
     Communications and networking. A roadside unit or other 
form of infrastructure (i.e., Access Point, small-cell set-up, or edge-
computer) will likely be required to house the computational hardware 
as well as providing full connectivity--perhaps to include 5G 
connectivity, V2X, Wi-Fi or other near-field communications, and GPS or 
its equivalent (for precision timing). The roadside infrastructure will 
likely provide secure interconnection to the intersection traffic 
signals (via a signal cabinet) and to a central traffic management 
system for that jurisdiction (potentially through a wireless or fiber 
optic link).
     Warning system. A VRU and vehicle warning system will 
likely require audible alarms, visual alerts, and other more advanced 
real-time alerts, such as haptic or projected images, for example. It 
will require real-time interconnection with the intersection's traffic 
signals, perhaps to adjust signal timing in real-time. The alerting 
system will need to be capable of alerting VRUs who are visually or 
hearing impaired, and offer ADA-compliant operation.
     Other intersection safety system considerations. A fully 
automated system is desired that does not degrade the underlying 
existing safety of an intersection, is upgradeable by virtue of a 
modular hardware and software design, uses open architectures to the 
fullest extent possible, including potentially open-source software, 
utilizes industry-accepted software development practices and is 
intrinsically cybersecure and maintains data privacy protections.
    This RFI is intended to inform DOT on the status of automation 
technologies and other complementary technologies that can be used to 
improve or enhance the safety of pedestrians, bicyclists, and other 
VRUs at or near roadway intersections. DOT seeks information on the 
state of the art, and emerging trends in, perception, machine vision, 
sensor fusion, real-time image and data analysis, path planning, 
decision-making, connectivity, and warning systems that could be 
implemented in real-time at intersections to improve pedestrian and 
other VRU safety.

Specific Questions

    Responses to this RFI are intended to inform DOT on the status of 
technologies that can be used to improve or enhance the safety of 
pedestrians, bicyclists, and other VRUs at or near roadway 
intersections, including the status of the current technical 
development or deployment of those technologies.
    DOT is providing the following questions to prompt feedback and 
comments. DOT encourages public comment on any or all of these 
questions, and also 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 operators, 
transportation-focused groups, organizations and associations, and the 
public.
    The questions to which DOT is interested in receiving responses 
are:

(A) General Technical Considerations

    1. What is the overall feasibility of developing an effective 
intersection safety system for vulnerable road users (VRUs) based on 
existing and emerging mobile (vehicle) automation technologies 
(including other

[[Page 57022]]

complementary technologies) as described in this RFI?
    2. What perception, machine vision, and sensor fusion technologies 
(and other sensing modalities or combinations) are best suited to an 
effective intersection safety and VRU and vehicle warning system?
    3. What real-time image and data analysis techniques are best 
suited to provide the required machine vision and perception for an 
effective intersection safety system?
    4. What techniques are most effective in providing real-time 
vehicle and VRU path planning and prediction capabilities at fixed 
roadway intersections?
    5. What new and emerging technologies can enhance machine-based 
decision making at intersections--including determining potential 
vehicle-VRU conflicts, incidents, dilemma zones, and encroachment in 
real-time?
    6. What is the potential role of AI and/or ML in perception, image 
analysis, data analysis and decision-making at intersections, both in 
real-time and asynchronously? What is the potential for real-time 
learning and group learning across a number of similarly-equipped 
intersections?
    7. How could such a system work effectively with all types of VRUs 
(pedestrians, bicyclists, wheel-chair users, users of electric 
scooters, etc.) and all types of vehicles (cars, trucks, vans, transit 
buses, commercial vehicles, etc.)?

(B) System Installation and Deployment

    1. How can the required installation, setup and calibration 
requirements for a perception and decision-making based intersection 
safety system be minimized?
    2. What pedestrian and VRU alerting and warning methodologies and 
systems would be most useful, including for example, visual (or 
projected), audible, haptic, connected, other?
    3. What vehicle driver alerting and warning systems would be most 
useful, to alert drivers in real-time of impending conflicts at 
intersections?
    4. What potential modes of connectivity, such as V2X (V2N, V2P, 
V2V, V2I . . . ), cellular or Wi-Fi, for connecting vehicles, 
infrastructure, signals, and VRUs, would be most useful and effective 
to assure the greatest degree of accessibility for all intersection 
users?
    5. What industry standards, best practices, processes, protocols, 
and interoperability requirements and capabilities are needed or best 
suited for the development of an effective intersection safety system?
    6. How can interfaces with traffic signal controllers and traffic 
management systems be best implemented? What data storage and curation 
of the system performance history (on-board, at the edge or in the 
cloud) are required?
    7. How can issues related to reduced visibility (e.g., night-time, 
low light, bad weather) be addressed and mitigated during both the 
development and deployment of an effective intersection safety system?
    8. Are there any existing research and development efforts, 
deployments, or pilot demonstrations underway that aim to provide some 
or all of the capabilities described in this RFI?

(C) Human Factors and Performance Measurement

    1. What human behavioral considerations are most important in the 
implementation of an intersection safety system to ensure maximum VRU 
and driver compliance with the warnings and alerts provided?
    2. What are the most relevant human factors, cognition and human-
machine interface (HMI) considerations for both VRUs and drivers to 
ensure the maximum efficacy of an intersection safety system?
    3. What metrics, key performance indicators, and measures of 
success are important for determining the performance and efficacy of 
an intersection safety system?
    4. How would testing and validation of an intersection safety 
system best be accomplished before full system deployment at active 
intersections?
    5. How can a testing and validation plan be devised that would 
balance testing and development safety with the ultimate real-world 
performance of an intersection safety system?
    6. What performance data would be required to validate the testing 
and efficacy of an intersection safety system, and how could that 
performance data be generated?
    7. What measurement and statistical approaches are applicable to 
real-time decision-making at intersections? How can decision or warning 
errors be minimized (e.g., through reducing false positives and/or 
false negatives)?

(D) Development Costs and Time to Deployment

    1. What is the potential schedule and cost to develop an effective 
intersection safety system? What are the potential future hardware and 
software ``stack'' costs for a system that can be deployed at the scale 
of (for example) 100,000 commercial installations after 3-5 years of 
development?
    2. What equity considerations factor into the potential testing, 
implementation, and deployment of an effective intersection safety 
system?
    3. What team composition of development, commercialization and 
deployment partners would be required to achieve the successful 
commercialization and deployment of such a system?
    4. For what proportion of intersections (signalized and/or 
unsignalized) would such a system be well-suited? What characteristics 
or measures are important in determining whether a specific 
intersection is well-suited for the implementation of an effective 
intersection safety system? How could such a system be further 
developed or adapted for use in rural areas?
    5. What are the installation, calibration, training, maintenance, 
and operating considerations for deployment of such a system across its 
full life-cycle by a range of potential end-users, including State, 
local, Tribal and territorial DOTs, cities and towns?

(E) Please Comment on Any Other Issues Relevant to the Development, 
Commercialization, and Deployment of an Effective Intersection Safety 
System

Confidential Business Information

    Do not submit information whose disclosure 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 September 13, 2022.
Robert C. Hampshire,
Deputy Assistant Secretary for Research and Technology.
[FR Doc. 2022-20188 Filed 9-15-22; 8:45 am]
BILLING CODE 4910-9X-P