Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Review
The impact universe—a framework for prioritizing
the public interest in the Internet of Things
Francine Berman,
1,
*Emilia Cabrera,
2
Ali Jebari,
2
and Wassim Marrakchi
2
1
Department of Information and Computer Sciences, University of Massachusetts at Amherst, Amherst, MA, USA
2
Radcliffe Institute for Advanced Study Undergraduate Research Partner, Harvard University, Cambridge, MA, USA
*Correspondence: fberman@umass.edu
https://doi.org/10.1016/j.patter.2021.100398
SUMMARY
The connected technologies of the Internet of Things (IoT) power the world we live in. IoT systems and de-
vices are critical infrastructure—they provide a platform for social interaction, fuel the marketplace, enable
the government, and control the home. Their increasing ubiquity and decision-making capabilities have pro-
found implications for society. When humans are empowered by technology and technology learns from
experience, a new kind of social contract is needed, one that specifies the roles and rules of engagement
for a cyber-social world. In this paper, we describe the ‘‘impact universe,’’ a framework for assessing the
impacts and outcomes of potential IoT social controls. Policymakers can use this framework to guide tech-
nological innovation so that the design, use, and oversight of IoT products and services advance the public
interest. As an example, we develop an impact universe framework that describes the social, economic, and
environmental impacts of self-driving cars.
INTRODUCTION: MANAGING CONNECTED
TECHNOLOGIES TO PROMOTE THE PUBLIC INTEREST
It is hard to fathom just how fundamental technology has
become in the world we live in. Internet-connected systems
and devices are critical infrastructure—they run power, water,
and communication systems, they drive cars, planes, and trains,
and they have changed how we do business. They provide a
platform for social interaction—targeting and modulating a
mindboggling set of options. They power the marketplace,
manage the organization, enable the government, and control
the home.
The increasing ubiquity, decision-making capabilities, and far-
reaching impacts of connected technologies, also called the
Internet of Things (IoT), have profound implications for individ-
uals and society. When humans are empowered by technology
and technology learns from experience, a new kind of social
contract is needed, one that specifies the roles and rules of
engagement for a cyber-social world. Creating this social con-
tract requires promoting the public interest in a rapidly changing
THE BIGGER PICTURE Digital technologies are fundamental to the world we live in. Internet-connected sys-
tems and devices are a critical infrastructure: they run power and water systems, they drive cars, planes, and
trains, and they have changed how we do business. They provide a platform for social interaction—targeting
and modulating a mindboggling set of options.
The increasing ubiquity, decision-making capabilities, and far-reaching impacts of connected technologies
have profound implications for individuals and society. They mandate new social controls—policy, regula-
tion, law, standards, recommended practice—that promote the public interest in a rapidly changing environ-
ment. Developing effective social controls requires a holistic appraisal of their potential impact on society
and the environment.
The ‘‘impact universe’’ is a framework that exposes a broad set of impacts—both quantifiable and qualita-
tive—to assess the benefits and risks of connected systems and devices. Developing an impact universe
framework requires a stakeholder to identify benefits and risks in aggregate; it encourages them to focus
beyond the single metric valuation that often characterizes the development of social controls for connected
systems. It provides a tool for stakeholders to more effectively guide technological innovation, so that the
design, development, use, and standardization of connected products and services advances the public in-
terest and promotes social responsibility in a tech-powered world.
ll
OPEN ACCESS
Patterns 3, January 14, 2022 ª2021 The Author(s). 1
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
environment driven by technology innovation and the business
opportunities it provides. It requires social controls—policy,
regulation, law, standards, recommended practice—that pro-
mote human benefits and mitigate the risks and dangers of a
tech-powered world.
The goals of this paper are 2-fold: (1) to describe a frame-
work—the ‘‘impact universe’’ —for thinking holistically about
the potential impacts of social controls for systems and the de-
vices in the IoT, and (2) to demonstrate by example—through
development of an impact universe framework for self-driving
cars—how complex it is to balance distinct public-focused goals
and strategies to promote beneficial outcomes. In general,
creating and employing an impact universe framework proceeds
in three phases: first, a stakeholder (a policymaker for our pur-
poses) identifies relevant public interest goals and key strategies
that promote them. Second, she identifies synergies and incom-
patibilities between the chosen strategies and goals and revises
them as necessary. Third, she develops targeted social controls
that promote the synergies of the chosen strategies and goals
and minimize their incompatibilities. This must be done with
respect to the context in which the social controls will be de-
ployed, and targeted to time frames specific to this context.
This is not straightforward. Different policymakers working on
the same issues often have competing goals and strategies and
varying abilities to control/guide public outcomes. Moreover, the
complexity of developing socially responsible policy is exacer-
bated by the complexity of architecting (or re-architecting)
technology to support public versus private interests. This is
especially true for the heterogeneous, decentralized IoT that un-
derlies much of the technological world we live in.
The IoT is a deeply integrated ecosystem of devices and sys-
tems that commonly exchange information, make decisions, and
manage and monitor in the background. Everyday devices and
systems—baby monitors, phones, home appliances, cars—
increasingly connect to the Internet and collect information,
modulate choices, and autonomously take on decision-making
responsibilities.
These technologies have tremendous benefits and sometimes
dangerous risks. Smart medical devices can efficiently regulate
insulin or monitor heartbeats, alerting the individual and medical
professionals when, or before, there is a problem. But without
adequate cybersecurity protections, smart medical devices
can be easily hacked, with potentially catastrophic or fatal re-
sults. This presents a challenge for manufacturers: How much
safety and at what price? Beefing up cybersecurity increases
the time and effort spent on product design, development, and
testing, potentially making products more expensive and/or
increasing time-to-market. Fixing vulnerabilities after product
release may also be problematic, as technical architectures
can be difficult and expensive to reverse-engineer. Facebook’s
iconic motto—‘‘move fast and break things’’ —increases eco-
nomic competitiveness, but it is an irresponsible approach for
IoT products and services used as critical infrastructure, or
when ‘‘breaking things’’ has potentially catastrophic results.
The lifeblood of the IoT is data—data that are often collected
and retained for competitive advantage by private entities who
fully control all aspects of its access, stewardship, preservation,
and use. Lack of transparency and access to these data can
work against the public interest: accident statistics from tests
with self-driving cars are needed to gauge their level of safety,
yet many states do not require private companies to report this
information. Consumer data used for pricing or assessment
may exacerbate social inequities, yet their algorithms, data,
and training sets are often unavailable for public scrutiny. Effec-
tive IoT policy must focus not just on products and services, but
on complex issues regarding the stewardship, preservation, ac-
cess, and use of the valuable data that drives them.
For technology to advance society, the design and develop-
ment trajectory of the IoT must be managed. Creating social con-
trols that ensure that IoT products incorporate adequate safety
and security, and other public protections requires leadership
from the public sector, whose mission it is to promote and pro-
tect the public interest. Moreover, it is easier for companies to
incorporate public protections when everyone has to do it.
With leadership and social controls from the public sector, the
current culture of tech opportunism can begin to move toward
a culture of tech in the public interest.
Creating social controls is complex, to say the least. Effective
standards, policy, and regulations must be crafted that promote
individual, community, and environmental protections during
design, development, deployment, use, interaction, and
disposal of IoT devices and systems. New laws, policy, and reg-
ulations must be created to deal with decision-making technolo-
gies, and to assign liability when autonomous systems fail. Many
potential impacts of IoT devices and systems are unclear and
need to be formally studied. Good social controls must be spe-
cific and enforceable, promote well-defined public objectives,
and coordinate multiple strategies to achieve desired outcomes.
Creating them is not an exact science.
The emerging field of Public Interest Technology (PIT) can
help. PIT focuses on the development and use of technology
in a socially responsible manner, with the goal of promoting
the common good. PIT strategies include public-focused prod-
uct design, standards that promote safety, security, and other
public interests, and public interest-focused policy and regula-
tion. These strategies are important because, without them,
profit, market leadership, and other private sector goals may
prevail, often leaving consumers and citizens at increased
risk. PIT strategies provide a way to rein in tech and reduce
catastrophic outcomes. They help regulators, policy makers,
and technologists think holistically about the social impacts of
tech products and services, using multi-disciplinary perspec-
tives from computer and information science, law, policy,
ethics, social science, science and technology studies, and
domain disciplines. These perspectives can be synergized
into a holistic framework—the impact universe—that provides
a PIT-focused way of looking at the broad and important social
effects of the IoT.
We define an impact universe framework for an IoT device or
system (‘‘thing’’) T as a set of goals for T, the strategies that pro-
mote these goals, and the interdependencies between all of
them. The impact universe exposes T’s potential benefits and
risks in the larger ecosystem, where human behavior, the char-
acteristics of the natural world, and existing social controls and
cyberinfrastructure all influence T’s realization. Innovation is
not stand-alone; the success and usefulness to the public of
IoT device or system T is dependent on its ability to navigate
this larger ecosystem.
ll
OPEN ACCESS
2Patterns 3, January 14, 2022
Review
Creating an impact universe framework is contextual—it is
dependent on which device or system, which goals, and which
strategies are identified to achieve a stakeholder’s desired out-
comes. It is dependent on whether the target environment is a
local, state, or federal jurisdiction. It may also vary based on
the population for whom the controls are intended. The impact
universe framework is also holistic—the framework helps
expose trade-offs within the larger cyber-social context in which
goals and strategies must be achieved.
An impact universe framework is an abstract construct and, as
with many things, the devil is in the details. To illustrate, we
describe in this paper an impact universe for the development
of social controls for connected autonomous vehicles (CAVs)—
self-driving cars. Our purpose is to demonstrate by example
how complex it is to identify effective goals and strategies that
promote technology in the public interest. Self-driving cars are
an ideal focus for this case study—there is already considerable
experience with their benefits and challenges, and they have
captured the imagination of both industry and the public. Their
development trajectory is complex, and the stakes in creating
appropriate policy, law, and incentives to guide their development
are high. They are expected to become ubiquitous over the next
few decades, and they are coming to a highway near you.
But self-driving cars are just one example of how the creation
and deployment of the IoT will change the world we live in far
more than we expect. The impact universe framework—exami-
nation of the synergies and incompatibilities of potential goals
and strategies in context—can be used to frame conversations
about the development of social controls for any IoT device or
system—a connected baby monitor, an implantable insulin
pump, a smart refrigerator, a surveillance system, and the like.
We briefly describe using the impact universe framework
approach for IoT devices and systems other than CAVs in
‘‘Beyond CAVs: creating an impact universe for other IoT devices
and systems’’ and discuss the challenges of subjectivity and
potential next steps in ‘‘The impact universe framework—
continuing the conversation.’’
In the following sections, we focus on CAVs. We describe how
policymakers, auto manufacturers, businesses, and the public
will influence the trajectory of CAV design, development, and
use. The impact universe framework helps focus close attention
on the larger social, economic, and environmental context in
which this trajectory will evolve. It is both challenging to steer
this trajectory toward the public interest and also critical,
because ultimately how IoT systems, such as CAVs, are
managed matters if we want to ensure that humans thrive, the
planet is protected, and society is advanced in an IoT-pow-
ered world.
An impact universe framework for CAVs
The development of automobiles has always focused on multiple
overarching goals—safety, environmental sustainability, eco-
nomic growth, etc. Consider the two overarching goals of safety
and environmental sustainability. Both are currently the focus of
a broad spectrum of transportation-related laws, regulations,
policy, and standards, many developed by stakeholders who
are public policymakers.
Reducing natural resource depletion is one specific goal that
promotes environmental sustainability. As CAVs become more
prevalent, manufacturers predict that next-generation CAVs
will be lighter, more energy efficient, use algorithms to drive
safely, and will not need as much protective safety equipment
as current vehicles. The strategy of building lighter-weight
CAVs with less safety equipment and more sustainable materials
will help achieve the goal of reducing natural resource depletion.
However, the strategy of building lighter CAVs that travel in
close, automated eco-platoons at high speeds will be a mixed
bag for the overarching goal of safety. While the predictability
of CAV platoons will likely reduce the overall number of accidents
(one specific safety goal), the accidents that do occur at higher
speeds and in lighter cars may be more dangerous to the
CAV’s occupants, working against the goal of decreasing the
percentage of severe accidents (another specific safety goal).
Understanding how the strategy to build lighter-weight, envi-
ronmentally friendly CAVs may result in both positive and nega-
tive safety outcomes raises specific policy issues: How much
and what kind of safety equipment should be required? Should
there be minimum weight requirements for cars? How fast and
close should CAVs travel? Should investments in research be
increased to explore safer and more sustainable materials for
next-generation CAVs? By taking into account the universe of
potential impacts, policymakers can gain important information
to assist them in creating effective social controls that achieve
their desired outcomes.
For our case study, consider a stakeholder policymaker
whose overarching goals are environmental sustainability, public
protections, and economic growth. Her specific goals in these
areas and the potential strategies that promote them are gener-
alized for illustration, and to demonstrate how complex and
contextual developing effective social controls can be. It will
be important for our policymakers to partner with technical and
industry experts whose deep knowledge is needed to create
realistic and effective strategies and specific social controls.
This multi-sector, multi-disciplinary approach, a PIT approach,
is critical to ensure that social goals are promoted, human ben-
efits are maximized, and the risks of ever more capable and
prevalent technologies are mitigated.
Self-driving cars in the cultural imagination
In 1966, one of the Oscar-nominated short-animated films was
‘‘What on Earth! The Automobile Inherits the Planet’’ from the Na-
tional Film Board of Canada. As per the Film Board: ‘‘The
animated short proposes what many earthlings have long
feared—that the automobile has inherited the planet. When life
on Earth is portrayed as one long, unending conga-line of cars,
a crew of extra-terrestrial visitors understandably assume they
are the dominant race. While humans, on the other hand, are
merely parasites.’’
1
The film, a bit over 9 minutes long, shows Earth’s inhabitants—
cars—traveling together, dictating the form of the built environ-
ment, ‘‘learning,’’ ‘‘playing,’’ and ‘‘reproducing,’’ all with hilarious
and often prescient results. It is not until the last minute of the film
that humans are introduced as parasites of the vehicle ‘‘earth-
lings,’’ and ones that do not matter very much.
The film is over 50 years old, but not so far from the future en-
visioned for CAVs 30–40 years from now (except for the whole
‘‘humans as parasites’’ part .). As vehicles become more and
more independent and autonomous, the notion that cars will
ll
OPEN ACCESS
Patterns 3, January 14, 2022 3
Review
be driving around on their own, platooning in groups, dictating
the development of roads and cities, and changing the transpor-
tation economy provides another perspective on how we will
experience the social and environmental impacts of future trans-
portation.
In this film and a host of other creative projects, society has
imagined a world in which technology dominates. This world is
increasingly possible, and the stakes continue to rise with tech-
nological innovation, more sophisticated artificial intelligence,
and few public protections, all of which encourage tech oppor-
tunism, rather than tech in the public interest. The challenge of
prioritizing tech in the public interest falls to policymakers, who
must help re-orient tech to advance society, and the public,
who must both advocate for change and deal with its conse-
quences. Yet the road ahead must be traveled, and social con-
trols that promote tech in the public interest will serve as our
best way forward.
But today, in the early 2020s, true self-driving cars have yet to
be built. Like many technologies in the IoT, the self-driving car in-
dustry has a long way to go before it achieves the full autonomy
expected by 2050–2060. As with human-driven cars, the future
of CAVs will be driven by both technological innovation and so-
cial controls; and, at this juncture, the public sector and the auto-
motive industry have a tremendous opportunity to evolve them in
a way that creates far-reaching and beneficial public impacts by
the 2050s, when they are expected to be ubiquitous. Before we
describe an impact universe framework that can guide future
CAV development, we describe the state of the industry and cur-
rent technological challenges in creating self-driving cars.
State of the automotive industry: True self-driving cars
do not yet exist
The first thing to understand about CAVs is that fully operational
self-driving cars do not yet exist. In 2016, the US National High-
way Transportation Safety Administration adopted a classifica-
tion system developed by the Society of Automotive Engineers
(SAE) that characterizes various levels of vehicle autonomy
based on the amount of human intervention and attentiveness
needed.
2
According to the SAE classification, the most autono-
mous cars you can buy in 2020 are level 3 cars. Level 0 cars have
no self-driving capabilities. A Model T was a level 0 car, but so
are some used pre-adaptive cruise control cars today. Level 5
cars can perform all driving tasks in all conditions—they are
true self-driving cars and are still an aspiration. At level 5, the
car is the driver, and no human driver is required. You currently
cannot buy new cars at either level 0 or level 5.
You still need a driver in level 3 cars and drivers must be ready
to take control at any time, but the car can take on many of the
responsibilities of monitoring the environment and operation.
Many automakers, including Audi, Nissan, Volvo, GM, and Tesla,
are beginning to build more and more capable level 3 cars for the
consumer market. On a recent road trip with a friend who drives a
high-end Tesla, both she and her car shared driving responsibil-
ities. It was an interesting partnership. My friend knew she could
rely on the car to navigate itself in highway conditions, but that
she needed to take over when driving on poorly marked, narrow
residential streets. This knowledge was critical to her under-
standing of what her car could and could not do, and to driving
it safely and effectively.
A level 4 car can essentially drive itself under certain condi-
tions. The catchphrase is ‘‘certain conditions’’ —self-driving
models cannot yet handle all the situations the car will encounter
and so have not yet reached 100% autonomy (the so far mythical
level 5). The transportation industry talks about the scope and
limits of the car as its ODD—operational design domain. ODD
describes the ‘‘operating conditions under which a given driving
automation system or feature thereof is specifically designed to
function.’’
3
This means that unusual weather conditions, obsta-
cles that the algorithms do not recognize, or unfamiliar road
conditions may all be outside of a level 4 car’s ODD and require
human intervention. Moreover, some errors may be a conse-
quence of the gap between the assumed limits of an ODD and
the environment the ODD actually describes.
Today, level 4 cars are being developed largely for commercial
use by both tech and commercial car companies. We expect to
see them on the road and available for sale to consumers some-
time in the next decade. By 2060, it will be hard to buy a car that
is not self-driving.
4
We are not there yet though. In 2017, Forbes
predicted that there would be 10 million ‘‘self-driving’’ cars in
2020.
5
That is less than 1% of the 1.4 billion cars on the road
in 2020.
6
A lot of innovation, planning, and development has to
happen before all transportation becomes smart transportation.
And that is not just a challenge but a great opportunity to steer
things in the right direction.
How self-driving cars work
The development of self-driving cars has broad social, environ-
mental, and economic repercussions. To understand these re-
percussions, it is useful to first explore how CAVs work.
Self-driving cars are known in the industry as Connected
Autonomous Vehicles and each of these terms has a specific
meaning. Connected, because data are collected wirelessly
from sources external to the car (satellites for weather prediction,
information from other cars, signals from highway sensors) as
well as sources internal to the car. Data are also sent to external
sources (information for other cars, data for the company, data
for crowd-sourced functions). Autonomous, because sensors
in and out of the car provide information to computers that
plan and provide instructions to actuators that operate the car.
CAVs are essentially ‘‘regular’’ (human-driven) cars enhanced
with cameras and sensors that ‘‘see’’ the environment, com-
puters that plan operation, and actuators that do the driving.
They drive essentially the same way human drivers do—through
a sense/plan/act approach—but CAVs do it with sensors and
computers rather than human brains. And, like human drivers,
they have to perform all these operations in a split second. My
friend’s Tesla has a variety of screens showing data on the pre-
vailing environment and potential obstacles—almost like a
driving video game where the reward is safe and efficient arrival
at your destination.
The complexity of the programs used by CAVs to accomplish
these self-driving tasks is extraordinary. The car must continu-
ously form a 3D representation of its environment: what is the
speed limit, what do road signs say, where is construction? Is
the person in the road a pedestrian or police rerouting traffic?
What is the weather like? What are cyclists and other cars doing?
To get this information, the CAV uses a variety of sensing sys-
tems. These include GPS (global positioning system) to
ll
OPEN ACCESS
4Patterns 3, January 14, 2022
Review
determine location information and LIDAR (light detection and
ranging) to ‘‘see’’ things at short distances (up to 450 ft) and
assist with emergency braking, pedestrian detection, and colli-
sion avoidance. Cameras provide additional details that help
with lane departure warnings and traffic sign recognition. Ultra-
sound (sound wave mapping) helps with adaptive cruise control
and obstacle detection. Inertial movement detectors keep track
of how many times the wheels rotate for relative distance
calculations. Radar (radio wave mapping) helps with blind spot
detection, rear collision warning, cross-traffic alerts and other
functions.
7
Why so many sensing/seeing systems? Because each of them
has different benefits, ranges, liabilities, and errors. We use
different sensors and perspectives to help the car ‘‘see’’ the
same landscape robustly and to minimize the weaknesses and
errors of any one system. Because the sensors provide multiple
perspectives and often information backup for each other, the
CAV’s computers can more robustly assess the ever-changing
environment in which the car is operating.
As the CAV’s computers receive information from sensors and
elsewhere, algorithms continuously model the surrounding envi-
ronment to determine how the car should proceed. The CAV’s al-
gorithms must detect objects, recognize what those objects are,
place them in the environment, and predict how they will move.
8
They do that through a complex and coordinated set of machine
learning algorithms. Regression algorithms develop an image-
based model for prediction and feature selection. Pattern recog-
nition algorithms filter images in preparation for classification.
Clustering algorithms discover structures from data points for
hard-to-identify images. Decision matrix algorithms systemati-
cally analyze and rate the image algorithms, based on confi-
dence of correctness and other values.
9
The outputs of the
data-driven analysis and ranking of these algorithms are instruc-
tions that allow the car to drive on its own—braking, acceler-
ating, changing lanes, and doing everything a human would do.
For level 3 and 4 ‘‘drivers,’’ the car they are dealing with is al-
ways changing. Data from previous drives of theirs and others is
used to help algorithms learn and improve performance, i.e., the
car is regularly getting ‘‘smarter.’’ My Tesla-owning friend re-
ceives software upgrades roughly every 3 weeks that improve
her car’s ability to drive autonomously. Recent software updates
included enabling the DashCam to automatically save video
clips when a safety event is detected, addition of Disney+ to
the car’s streaming options, and language support for car pas-
sengers who speak Romanian.
10
Tesla drivers are essentially
driving a different car with every update and must learn and
adapt to new capabilities.
Even now, when they are far from perfect (almost), self-driving
car cars are breathtakingly complex and surprisingly safe. (There
have been roughly a half dozen fatal accidents involving autono-
mous vehicles, although determining cause is challenging.
Determining safety statistics that compare fatalities in autono-
mous vehicles for a set number of vehicle miles traveled to fatal-
ities in non-autonomous vehicles for the same number of vehicle
miles traveled is still premature because not all of the relevant
data are available.)
A decade ago, the typical CAV program was around 100
million lines of code. In contrast, the program that flies the Boe-
ing 787 Dreamliner was around 6.5 million lines of code.
11
One
reason for this is that the ambient conditions for planes are easier
to model—although planes need to take into consideration a
broad scope of weather conditions and some other planes,
CAV’s need to take into consideration many more interactions
with objects that behave unpredictably. Over the last decade,
CAV programs have become even more complex. It is no sur-
prise that CAV companies are considering the development of
Jetsons-like VTOLs (vertical take-off and landing vehicles) that
fly, in addition to terrestrial cars that drive themselves.
12
(In the
1960s, ‘‘The Jetsons’’ TV show featured a family of the future
who used flying cars—a common mode of transportation.)
CASE STUDY OF A CAV IMPACT UNIVERSE
FRAMEWORK THAT PROMOTES ENVIRONMENTAL
SUSTAINABILITY, PUBLIC PROTECTIONS, AND
ECONOMIC GROWTH
To illustrate how nuanced the impact universe framework can
be, we now develop an impact universe framework for CAVs.
Although today true self-driving cars do not yet exist, consider
self-driving cars in 2050–2060, when they will be prevalent.
Our cars will be lighter (hundreds of pounds), go father (hundreds
of miles per charge), and travel in groups or ‘‘platoons.’’
13
They
may look more like the pod-shaped cars in the Jetsons cartoon
show than they will resemble the cars of today. We will see
changes in the way CAVs are built, the way they are used, and
the transportation economy. CAVs will influence where we live
and how we think about our commute to work. They will provide
new opportunities for long-distance travel, mobility, land-use,
and create new and different jobs. And they will impact the
development of infrastructure, use of resources in the natural
environment, and lifestyle choices.
Social controls deployed now will greatly influence the way
current and future CAVs are designed, developed, utilized,
commercialized, and managed. In the following sections, we
describe specific goals and strategies for creating an impact uni-
verse framework for CAVs with the overarching goals of environ-
mental sustainability, public safety, and economic growth.
Goals for promoting environmental sustainability
We often think about the environmental impact of automobiles in
terms of emissions and air pollution, but the manufacturing,
operation, and disposal of cars during their automotive ‘‘life cy-
cle’’ have even broader affects. Natural and other resources
are used during the manufacture (‘‘birth’’) of a vehicle as well
as to power it (through fuel or electricity) during its operation
(‘‘life’’). After a CAV stops operating (‘‘death’’), recycling and re-
purposing, rather than disposal, of CAV components can reduce
e-waste.
Complicating this is the expected use model for self-driving
cars (e.g., owner-deployed or used as a commercial service),
which greatly impacts wear and tear and the calculation of the
lifespan of electric components used in the CAV. This means
that, without additional information about the social and eco-
nomic environment in which CAVs operate, it is hard to accu-
rately predict how long they will last and whether components
can be recycled. Design and maintenance must also be consid-
ered—can CAV components be upgraded in software, or must
ll
OPEN ACCESS
Patterns 3, January 14, 2022 5
Review
they be swapped out for new hardware? All of these influence
calculations of the sustainability of CAVs.
In addition to impacting the natural environment, CAVs will
also impact the design of the built environment—roads, support
facilities, land-use, parking, etc. —accommodating new modes
of operation and passengers’ expanded lifestyle choices.
Because the entire life cycle of the car should be taken into ac-
count, various specific goals can be used to promote environ-
mental sustainability. These include:
dReduce emissions from present levels during the
manufacturing and operation of CAVs (E1)
dPromote sustainable materials usage for manufacturing
and operation of CAVs (E2)
dMinimize additional planetary e-waste through the recy-
cling and repurposing of CAV parts and systems (E3)
dBuild transportation infrastructure that promotes environ-
mental sustainability (E4)
We describe strategies for goals E1–E4 below.
Strategies to reduce emissions (E1)
Emissions depend on both vehicle design and vehicle use. In the
transportation industry, the relationship between car use and its
level of emissions is often captured by the ASIF formula.
14
The
formula models emission level as a product of activity level
(use), modal share (fraction of travel conducted in the usage
mode), and carbon intensity of fuels used in that mode.
15
Emissions (ASIF) = activity level (A) 3modal share (S) 3energy
intensity (I) 3fuel carbon content (F)
As the formula shows, there are multiple ways to reduce
emissions. Many strategies fall into the general categories of
(1) building cars that are more energy efficient (i.e., build cars
with electric batteries versus fuel-injected engines), relevant to
lowering I and F in the ASIF formula, and (2) driving cars in a
more energy-efficient manner, also known as eco-driving strate-
gies, relevant to lowering the A and S parts of the ASIF formula.
With respect to building more energy-efficient cars, there has
been a growing shift toward plug-in electric vehicles (PEVs),
15
with recent announcements of large investments by Ford and
notice that all GM vehicles will be all-electric by 2035. By 2040,
it is expected that it will no longer be possible to buy a non-elec-
tric new car.
16
Federal emission standards
17
guide the design of
today’s vehicles but have also been a cause for controversy.
Many auto manufacturers want these standards loosened, while
environmentalists want them strengthened.
18
Adding to the mix
is the existence of different standards in different jurisdictions;
for example, California law is tougher on emissions than federal
law. Public officials looking to lower emissions more aggres-
sively may want to encourage specific jurisdictional controls
that incentivize exceeding or strengthening current standards.
With respect to driving cars more sustainably, eco-driving can
help lower emissions during vehicle operation. Eco-driving tech-
niques include smooth acceleration and deceleration, mainte-
nance of a steady speed when possible (think cruise control),
minimizing idling, anticipation of traffic flow, etc. Basically,
eco-driving is what we think of as ‘‘good driving habits,’’ and
they can be algorithmically programmed in autonomous vehi-
cles. Current level 3 and 4 cars incorporate eco-driving tech-
niques, and 2050 promises close platoons of coordinated
CAVs, all of which incorporate eco-driving strategies.
Studies show that eco-driving can reduce emissions by up to
9%.
19
Efforts to promote human eco-driving focus primarily on
training and practice (see the report by Shaheen et al.
20
for an
analysis of public education programs), rather than regulation.
(There is some regulation to limit poor driving strategies such
as excessive idling.
21
) This will be different for CAVs, where algo-
rithms will do the driving and more research and development
are needed to define and incorporate autonomous eco-driving
techniques. A policymaker’s strategy to reduce emissions
through eco-driving might be to invest in further research
exploring improved CAV eco-driving approaches. There is
precedent for this, both for eco-driving and other transporta-
tion-related areas, as demonstrated by the important state
investments in research made by the National Cooperative High-
way Research Program.
22
Based on this discussion, strategies to reduce emissions may
include:
dStrengthen federal emission standards
dCreate incentives for auto manufacturers to cut emissions
in CAVs
dInvest in research to improve eco-driving
Strategies for promoting sustainable materials usage
for CAV manufacturing and operation (E2)
The desire for more energy-efficient vehicles with reduced emis-
sions has led to the increasing prevalence of PEVs. It is currently
predicted that, by 2025, 70% of PEVs will have lithium-ion batte-
ries.
23
Lithium-ion batteries are rechargeable and attractive for
electric-powered vehicles because they have a high energy den-
sity (can store a great deal of energy per volume) and little energy
leakage.
Lithium-ion batteries include other materials as well: Tesla’s
lithium-ion battery is also composed of nickel, cobalt, and
aluminum. The Nissan Leaf’s lithium-ion battery is also
comprised of magnesium.
23
But many of the materials in modern
PEV batteries—lithium carbonate, graphite, and cobalt—are
expensive, considered to be in limited supply, or may not keep
up with demand.
To promote sustainability, both researchers and manufac-
turers must explore new materials and battery designs that can
be used with next-generation CAVs. What will future CAV batte-
ries look like? Each potential combination of materials must be
viewed from multiple perspectives: chemical and materials prop-
erties, safety and performance, cost and availability, etc. Each
battery design will dictate the type and amount of natural mate-
rials used and potentially depleted. It will also have implications
for infrastructure in the built environment as recharging stations
and other services will need to be planned and sited with power
generation in mind. Successful new battery designs will be the
result of both research and market forces. Investment in
research will be important to manage the supply of lithium car-
bonate and other materials currently in use.
Other CAV materials will need to be monitored and managed
as well. In vehicles with autonomous capabilities, computers
and LIDAR commonly require rare-earth materials in small
ll
OPEN ACCESS
6Patterns 3, January 14, 2022
Review
amounts.
19
Although not all 17 rare-earth elements are actually
rare (some are plentiful in the Earth’s crust and on the ocean
floor),
24
they are often diffuse or environmentally damaging to
extract and refine.
25
They are none-the-less critical at this point
in time. According to experts, "rare-earth metals, when looked at
anatomically, seem to be inseparable from each other, in that
they are all almost exactly the same in terms of their chemical
properties. However, in terms of their electronic properties
[and] their magnetic properties, each one is really exquisitely
unique, and [can] occupy a tiny niche in our technology, where
virtually nothing else can.’’
26
The availability of rare-earth elements is complicated by geo-
political issues.
27
China has systematically captured the rare-
earth market and used it as a strategic advantage. Current US
efforts to partner with other countries as well as rebuild the US
domestic supply chain have important business, environmental,
and political consequences. All must be factored into plans for
design, production, and prevalence of CAVs and other IoT
products.
We do not quite have a model with the specificity of the ASIF
formula to estimate materials depletion, and this will also depend
on the prevalence of CAV uptake. However, policymakers can
work to mitigate the depletion of materials used in the manufac-
ture of CAV components and processes just as we do now with
non-autonomous vehicles. They can do this by exploring and
incentivizing the use of sustainable, energy-efficient materials,
and developing economic and social controls that promote the
reduction of the number of vehicles on the road through incen-
tives for public transportation or other alternatives. Strategies
to promote sustainable materials usage may include:
dInvest in electric battery research that focuses on sustain-
able materials
dIncentivize the use of sustainable electronic components
for CAVs
Strategies for minimizing e-waste (E3)
Dealing with the ‘‘death’’ and ‘‘afterlife’’ of a CAV’s many elec-
tronic components will be a critical strategy for promoting envi-
ronmental sustainability. The world currently produces more
than 50 tons of e-waste per year,
28
only 20% of which was docu-
mented and recycled in 2018.
29
Landfill e-waste may contami-
nate soil and groundwater and/or expose workers to hazardous
and carcinogenic substances. Moreover, data on e-waste is not
universally collected, making it hard to estimate the size of the
problem. Increasing prevalence of CAVs will exacerbate these
current challenges.
CAV recycling and repurposing can minimize e-waste and
extend the economic value of automotive components. We
currently recycle conventional vehicle components, such as
aluminum, copper, scrap metal, and tires. In total, around 80%
(by weight) of a conventional vehicle is recycled and the rest is
often sent to the shredder.
30
A primary focus for recycling and repurposing will be PEVs.
PEVs are currently assumed to last at least as long as their ve-
hicles, and battery repurposing can extend the life of still-useful
batteries. When a battery is removed from a PEV, it may still
retain 75%–80% of its original capacity.
31
This still-functional
battery can then provide an alternative to traditional lead batte-
ries for purposes such as automotive starting and ignition, tele-
communications backup power, grid connected energy stor-
age, etc.
32
Moreover, dissembling and recycling battery
components and materials creates fewer emissions than pro-
ducing new batteries from natural sources.
31
As CAVs become
more prevalent, the recycling industry for rare or expensive ma-
terials (like the ones used in lithium-ion batteries) is also ex-
pected to grow.
Recycling and repurposing other components that give CAVs
their autonomy—sensors, processing computers, and other
non-battery components—may, however, be a mixed bag. It is
not clear whether the life of various components will be useful
past the life of the car. Even if there is useful life in them, ad-
vances in hardware technology may make it inefficient to re-
use them. The development of strategies to reduce the negative
environmental impacts of CAV electronics may require further
study and must take both the potential for further functionality
and the problems with disposal into consideration.
Note that the lifespans of CAV components and CAVs them-
selves are dependent on how CAVs are used. Conventional
owner-operated vehicles are often assumed to have a 15-year
lifespan, during which they are parked 95% of the time. Recently,
Ford’s AV operations chief conjectured that future CAVs that are
used more extensively for ride-hailing services may last around 4
years.
33
CAVs as a service will have different component life-
times than CAVs dedicated to individual use. The interplay of
CAV use, ownership, economics, and other aspects of the social
environment in which CAVs will be deployed is critical to accu-
rately calculate their environmental, business, workforce, infra-
structure, and production impacts.
While policymakers may need to wait for more experience and
information to specifically guide the disposal and afterlife of CAV
components, they can continue to promote today’s guidance on
repurposing and recycling.
34,35
Moreover, they can begin to
collect the data necessary to help calibrate environmental e-
waste from CAV autonomous components. Gathering useful
data and incentivizing recycling and repurposing of CAV elec-
tronic components are two strategies that can help promote a
policymaker’s objectives.
dIncentivize the recycling and repurposing of CAV electronic
components
dCollect information on the levels and amount of CAV
e-waste
Strategies for promoting a sustainable built
environment (E4)
CAVs will also impact land-use and the built environment—urban
and suburban density, roads, parking and way stops, facilities
for maintenance, repair, disposal, etc.
Consider parking. A typical vehicle may be parked for 95% of
its lifetime and a recent study estimates urban land-use to
include between 10 and 54 parking spaces per acre in 5 major
cities.
36
But self-driving cars will not really need to park nearby
when we arrive at our destination. They can go pick up other rid-
ers or shelter far away, autonomously coming to pick us up when
needed. This means that we can allocate real estate to other
things: more residential or office units, bike paths, parks, and
pedestrian walkways, etc.
ll
OPEN ACCESS
Patterns 3, January 14, 2022 7
Review
CAVs may promote dispersed land-use around metropolitan
areas and will change how we think about commuting. When
you do not have to drive, you can work in your car, visit with
friends, read, sleep, or take meetings. An hour commute to a
much farther workplace can add 2 hours of work time. A long
CAV trip on highways that accommodate higher speeds and pla-
tooning may replace some trips that we presently take by train or
air. With prevalent CAVs responsible for the driving, and outfitted
to accommodate other activities, the way we use and think about
vehicle transportation will change.
This will lead to significant changes in the built infrastruc-
ture—roads, transportation facilities and services, land plan-
ning for evolving changes in population density, etc. It will
also impact the economics of the transportation ecosystem—
who owns and who uses, who works and at what job, what
kind of companies are needed to support the automotive
ecosystem, who their customers are, etc. Decisions about the
built environment are deeply connected to business models,
sharing, workforce, planning, and other areas. (See ‘‘Promoting
economic growth goals,’’ for more details about this.) Our pol-
icymaker is aware of the many ways that things will change, but
experience and planning expertise will be needed in advance of
policy and infrastructure development. Her strategy may be to
create a request for study and convene an expert group to get
ahead of potential changes, charging this group to recommend
ways to structure infrastructure and develop a built environ-
ment that promotes strong communities and environmental
sustainability:
dCreate a request for study to recommend planning guide-
lines for the built environment
Beyond CAVs, sustainability is a key impact for almost all IoT
devices and systems. The CAV-focused discussion here will be
similar to a life-cycle discussion for smart appliances, Fitbits, or
iPhones. What electronics should be included and how do we
deal with them at the end of the life of the product or component?
How will the product impact the natural and built environments?
The impact universe framework for these products may expose
different priorities and relationships but will likely be equally
nuanced.
Promoting public protections—safety, security, and
privacy goals
In 1965, Ralph Nader published the book ‘‘Unsafe at Any
Speed,’’
37
exposing the defects and dangers of the Chevrolet
Corvair. The book, and the recognition that vehicle accidents
were the leading cause of death for Americans under 44, spurred
legislation on vehicle safety and established the National High-
way Traffic Safety Administration.
38
Since then, vehicle safety,
security, and more recently privacy have been critical priorities
for the public and the transportation industry. The development
of CAVs—more complex, more autonomous, and more likely to
blur social and technical boundaries—exacerbates all chal-
lenges with public protections.
For policymakers, embedding public protections in the design,
use, and, oversight of CAVs will be critical to promote the public
interest. Common goals to promote public protections for auton-
omous transportation include:
dPromote CAV safety through design and construction
(passive safety) and driving and operation (active
safety) (P1)
dPromote CAV cybersecurity through designs that meet and
contribute to best practice standards and approaches (P2)
dProvide consumers the right to know what personal data
are being collected and how the data are used in CAVs.
Create opt-in options for collection and use of consumer
personal information not needed for CAV operation, main-
tenance, or safety (P3)
Strategies for promoting safety (P1)
It is hard to imagine anyone in the transportation ecosystem for
whom human safety is not a primary objective. The commitment
to safety is echoed in every stakeholder group, from vehicle
manufacturers to auxiliary industries, such as insurance pro-
viders, to the United States Department of Transportation, who
named safety as their highest priority.
39
Two common ways of promoting safety are passive safety and
active safety. Passive safety focuses on making the ‘‘hardware’’
of the car—materials, design, components, body—better able to
avoid, withstand, and minimize accidents. Active safety focuses
on promoting safety through the driving and operation of the car.
Passive safety approaches—standards, design, research into
materials, aerodynamics, energy efficiency, and other contribu-
tors—will evolve with increased prevalence of CAVs. As
described earlier, future CAVs are expected to be much lighter
and ‘‘right-sized’’ in comparison with today’s cars, and new de-
signs may achieve this goal by jettisoning safety equipment and
varying the materials chosen for construction (potentially replac-
ing heavier materials, such as steel, with aluminum, carbon fiber,
or other lightweight synthetics). As these designs evolve, policy-
makers and the automotive industry will need to carefully
consider the impact of the new designs on safety.
But CAVs will also bring new venues for passive safety. In the
future, CAVs will operate on streets and highways by joining and
leaving platoons of vehicles.
40
They will draw information from
V2V (vehicle-to-vehicle) sources and sources in the road infra-
structure and the built environment (V2I [vehicle-to-infrastruc-
ture]). They will need to process almost instantaneous alerts of
V2V and V2I systems to improve their approach to braking,
turning, lane changing, and more. Potential safety risks for vehi-
cles traveling in a platoon will need to be factored into future CAV
standards and regulations. Effective strategies may focus on the
creation of studies and advisory groups to gather data, study,
and envisage needed standards, regulation, and protections
for V2V and V2I environments.
Active safety focuses on driving and operation. For CAVs, pro-
moting active safety shifts the focus from the human driver to the
autonomous driving system: making a CAV actively safe essen-
tially amounts to making the algorithms and sensors accurate,
responsive, fault tolerant, and robust.
Active safety in CAVs is a game changer. With over 90% of car
crashes due to human error,
41
it is widely believed that removing
humans from the driving equation could dramatically improve
the safety of road transportation. Data show that accidents in hu-
man-driven cars are often a result of distraction, misjudgment, or
impairment. One way to beef up safety for human drivers has
been to restrict scenarios in which drivers are more likely to
ll
OPEN ACCESS
8Patterns 3, January 14, 2022
Review
make these kinds of mistakes, for example, by prohibiting drink-
ing and driving, texting and driving, etc. However, it is rarely the
case that a human driver will misclassify a cyclist as a trash can.
CAV systems are highly susceptible to these kinds of errors.
Autonomous systems eliminate the distraction and impair-
ment mistakes that plague humans, but they can introduce
new errors not common to humans. Potential sources of system
errors include existence uncertainty, state uncertainty, and class
uncertainty. Existence uncertainty describes the uncertainty
around whether the system ‘‘sees’’ an object—a stop sign,
road marking, etc. State uncertainty refers to uncertainty in a
measured physical variable or characteristic (e.g., size, position,
speed), and can reduce data integrity. Class uncertainty de-
scribes confusion about the correct identification of an ob-
ject—cyclist or trash can? —and can be caused by limitations
or problems with CAV identification algorithms. Not surprisingly,
humans trounce autonomous systems when it comes to class
certainty.
42
To improve the safety of CAVs, data must be gathered on the
types and impacts of algorithmic and system errors that will be
critical to improving their safety. Policymakers can promote
active safety by requiring independent collection and analysis
of errors that lead to CAV accidents, much like the Federal Avia-
tion Administration collects data in assessing airplane crashes.
43
Note that there are always trade-offs between safety and other
goals, e.g., economics and environmental sustainability. Policy-
makers must determine the right balance for these competing
goals to promote the public interest in the transportation
ecosystem. Moreover, safety trade-offs will be different in a
world where there is a substantial number of both CAVs and hu-
man-driven vehicles—the midway point between today’s envi-
ronment (mostly human driven), and the environment we will
see as we approach 2050 (mostly autonomous). As we transition
to a virtually all-autonomous environment, safety trade-offs will
need to be continually re-assessed.
Note that sometimes these trade-offs will be counterintuitive.
A study by the Rand Corporation contrasted safety, measured
by the number of fatalities, with prevalence and maturity of
CAV systems. The study’s results indicate that early release
and prevalence of less mature (and more accident-prone)
CAVs may actually save more lives.
41
One possible explanation
is that replacing unpredictable human drivers with less mature
CAV systems creates more predictability in the overall system
and safer roadways.
In addition to strategies currently being utilized to promote
automotive safety, CAV-specific strategies to promote safety
may include:
dCreate a request for study to recommend best practice and
standards for V2V and V2I engagement
dRequire and coordinate data collection on accidents for
autonomous vehicles driving on public roads
Strategies for promoting cybersecurity (P2)
As a ‘‘computer that drives,’’
44
the security of the CAV can be a
direct contributor to its potential for safety. Security is a system’s
protection from theft, damage, and disruption or misdirection of
the services provided by its hardware, software, or electronic
data. As CAVs evolve, their sensors, hardware, software, and
data provide new attack surfaces and new ways to threaten
the safety of passengers and others. Since 2016, the number
of annual cybersecurity incidents involving CAVs has increased
by 605%.
45
Over the last two decades, cybersecurity, and the
risks of cyber-physical systems, have been an increasing
focus of the US National Academies Computer Science and
Telecommunications Board, the National Institute of Standards
and Technology, the Department of Transportation, and other
groups.
The high-profile remote hacking of a Jeep Cherokee
46
(Box 1)
raised public awareness that security of the software and hard-
ware of CAVs can be a serious risk to safety. Cars and systems
that can be hacked are cars that are unsafe.
Many CAV security vulnerabilities are similar to those found
in other computer systems, and good practice for CAV cyber-
security will mirror good practice for other connected digital
products. Common types of attack include unauthorized soft-
ware updates, password and key attacks, network protocol
attacks, denial of service attacks, and more.
47
These attacks
could come from short-range or local means, through
networks produced by V2V and V2I connectivity, and even
physical access.
47
Additional vulnerabilities may come from
nefarious activity, inability to deal with network outages, inter-
ception, or hijacking of information, failures and malfunctions
of digital devices, systems, or power, loss, or leakage of infor-
mation, as well as traditional physical threats to conventional
automotive components.
48
In CAVs, any system may have se-
curity vulnerabilities, from the connected GPS, Bluetooth,
WIFI, and media systems, to the physical LIDAR, cameras,
or other sensors.
Good security practices include the ability to patch/upgrade
CAV software, use of standard protocols and implementations,
ensuring that the CAV can function offline, use of encryption
and authentication for CAV data, segmentation—the ability to
isolate systems to reduce vulnerabilities, and layering—moni-
toring data traffic and isolating the effects of malware.
For example, security segmentation involves protecting CAVs
by creating closed circuits whenever possible, so that the cor-
ruption of one system will not lead to the corruption of the entire
vehicle. Security breaches are most dangerous when remote ac-
cess to a less important system can serve as a gateway to a
more important one, as in the Jeep remote hijacking scenario.
In the same way that connectivity can increase the hacking sur-
face, segmentation and independence of components can
reduce the hacking surface, allowing the lack of connection be-
tween different components to function as a built-in barrier. This
can help prevent accessing driving functions through infotain-
ment systems, a common source of vulnerabilities.
49
Good security is a win for everyone. It is important to recognize
that responsibility for this will largely be in the hands of the auto
manufacturers, rather than the regulators. To create a culture of
security best practices for CAVs, the transportation industry
formed an Auto-ISAC (Information Sharing and Analysis Center)
in 2015, following the model of cybersecurity information sharing
in aviation and other industries.
50
The goal of the group is to
‘‘share and analyze intelligence about emerging cybersecurity
risks to the vehicle, and to collectively enhance vehicle cyberse-
curity capabilities and the commercial vehicle sector.’’ Just as in
aviation, industry proactivity is critical to promote the safety and
ll
OPEN ACCESS
Patterns 3, January 14, 2022 9
Review
security of vehicles at the design and implementation stage,
when strategies can be most effective.
A policymaker may choose to promote her security objectives
as we do now—by monitoring security problems and expecting
or requiring that security systems for CAVs meet best industry
practice. In addition to supporting current cybersecurity efforts,
she may promote more long-range strategies, for example,
increasing researcher access to security breach data (see the
strategies in the last section) and increasing funding for CAV cy-
bersecurity research. A strategy in this vein would be:
dInvest in cybersecurity research that improves security
practice and standards for CAVs
Strategies for promoting personal privacy (P3)
Although, from an operational perspective, CAVs are computers
that drive, from a privacy perspective, CAVs are essentially
smartphones with wheels.
51
McKinsey estimates that modern
cars collect as much as 25 gigabytes of data per hour from sen-
sors, including data on web browsing, video and music stream-
ing, biometrics, driving behavior, smartphone usage, etc.
51,52
Currently there is little restriction on what can be done with these
data, and whom it can be shared with or sold to.
Occupants of a car typically know neither the extent of the
data collected nor what is done with it. Moreover, sensors and
cameras also collect data about individuals outside the car—by-
standers—to make planning and operational decisions, and pri-
vacy protections must cover them as well. Complicating things is
the fact that personal data (location and travel history, images of
someone walking across the street, etc.) may be used for CAV
operation as well as commercial opportunities, so prohibiting
the collection of some personal data can negatively impact per-
formance and safety.
Like other connected digital products and services, CAVs will
require proactive policy and regulatory restrictions and oversight
to protect personal privacy. Some CAV privacy restrictions will
resemble privacy restrictions for other connected digital con-
sumer products. Some may need to be targeted to the CAV envi-
ronment, for example. prohibition of ‘‘take it or leave it’’ terms
that endanger CAV occupants who have not opted-in to personal
data collection. All of this both mandates and complicates the
deployment of effective CAV privacy protections.
Vehicle privacy has been discussed for over a decade—much
in advance of today’s autonomy innovations—with the Vehicle
Information Infrastructure Report,
53
convened by the Depart-
ment of Transportation, providing an early look. Today, various
reports and gatherings by the Federal Trade Commission
54
and industry-wide consortia, such as the Auto-ISAC,
50
focus
on privacy and security issues for CAVs.
55
Following FTC guide-
lines and the Consumer Privacy Principles of the Alliance of
Automobile Manufacturers,
56
design principles and practices
that promote personal privacy in CAVs may include:
dRequirements for transparency about the collection, use,
and sharing of personal information. Provision of access
to data subjects for reasonable review and correction
dData minimization, de-identification, and retention policies
that focus on the use of personal data only for business
purposes and only as long as is needed
dChoice and control of data subjects about the collection,
use, access, and sharing of personal information beyond
operational purposes
dIncorporation of Notice and Consent policies for use of
CAV personal data
dSecuring of personal data and promotion of data privacy
when possible, including encryption, anonymization, dif-
ferential privacy, etc.
dDevelopment of monitoring and accountability mecha-
nisms to ensure compliance with privacy policies and the
commitment of industry to comply
dDesign requirements to build strong privacy (and security)
protections into vehicle designs at the outset, rather than
an afterthought
All of these provide specificity to the general promotion of
transparency and consumer control for non-operational use of
personal CAV data. In developing specific social controls, a pol-
icymaker may need to extend existing privacy protections to
CAV environments or specify targets and practices similar to
those above in new policy or legislation. Her proactivity will be
important, as greater privacy protections are unlikely to come
from industry. A strategy that serves as placeholder for the
above recommendations and those similar to them is
dRequire transparency and consumer control of personal
data for non-operational use
Some of the most effective strategies for promoting privacy,
safety, and security are to make these protections part of the
hardware and software architecture design of the device or sys-
tem itself, and to limit unsafe use through standards, policy,
regulation, etc. This is true for virtually all IoT products and
Box 1. Hacking a Jeep Cherokee
One of the most high-profile examples of a security breach of a semi-autonomous vehicle is a 2015 ‘‘white hat’’ remote hacking
demonstration of a Jeep Cherokee. To demonstrate security flaws, hackers took over the vehicle while a journalist was driving
down an interstate at 70 mph.
By remotely accessing the vehicle’s onboard entertainment and navigation system, the hackers gained control over every vehicle
feature from windshield wipers to the accelerator to demonstrate vulnerabilities in the car’s computer system. They used the ve-
hicle’s cellular network, connected to the entertainment system, to hack the vehicle from a remote location. This resulted in the first
massive security-related vehicle recall, with 1.4 million vehicles being recalled.
This was a demonstration, but the risk of security breaches is likely to increase as vehicles evolve toward full autonomy. Many of
the features of CAVs, such as platooning and sharing of training data, will require constant connectivity and will also provide new
points of entry for attackers.
ll
OPEN ACCESS
10 Patterns 3, January 14, 2022
Review
services, but the balance of priorities one might consider for a
Fitbit, where a worst-case outcome is much less likely to be
catastrophic, versus for a car or connected pacemaker, whose
worst-case outcomes may be fatal, must reflect that. The level
of risk of various prioritizations of goals and strategies can be
exposed by their impacts and inter-relationships in the impact
universes for each of these devices.
Promoting economic growth goals
Today, most American families own a car, with 91.3% of house-
holds in 2020 reporting having access to at least one vehicle.
57
But use of car services such as Lyft and Uber are on the rise.
What happens when transportation moves from a durable
good to a service? Economists expect that CAVs will broadly
transform the transportation industry including its players, its
workforce, and its impact on other sectors.
Although early indicators show that the changes are likely to
be dramatic—the current ride-hailing service industry, which
did not exist a decade ago, was recently valued at 61.3 billion
USD.
58
It is also expected that changes will evolve over the
next few decades. Getting ahead of the game will help a pub-
lic-focused policymaker steer the transportation economy in
the right direction. Her policy goals may be to
dGather information and accurately model CAV impacts on
the economy (Econ 1)
dUse policy to incentivize and promote economic growth
and stability (Econ 2)
These are accomplished now by gathering data and
convening expert groups to research, model, report, and predict
the economic impacts of technologies and industries, and this
information is used to inform economic policy. This approach
will be important to assess the economic implications of CAVs
as well.
To understand the economic implications of the CAV industry,
a good place to start is three areas in which economists expect
dramatic changes in the automotive ecosystem—market leader-
ship, vehicle-related services, and the transportation workforce.
To demonstrate the wide-ranging economics of CAV develop-
ment, we briefly discuss these areas below.
Leadership in the CAV marketplace
Today, the automotive industry is one of the largest in the global
economy, with automobiles and auto parts making up 20% of all
US retail sales,
59
and worldwide sales amounting to over 1.5 tril-
lion USD.
60
Most of these sales benefit around a dozen powerful
conglomerates. Moreover, automotive manufacturers can con-
trol almost the entirety of the process—design, manufacturing,
sales, and service.
With the emergence of CAVs, new players are entering the
transportation industry: tech companies. Tech companies are
creating cars from the inside out, focusing on the complex soft-
ware systems that will allow cars to drive themselves. Whether
automobile-focused companies (building cars with smart sys-
tems) or tech companies (building smart systems surrounded
by driving hardware) will prevail impacts the character of the
market, who benefits, and when.
61
The stakes are high. In the automotive industry, research and
development into CAV technology has already reached over 100
billion USD.
62
But the spending may not come from the com-
panies you think—Google’s Waymo or GM’s Cruise, for
example. In 2019, Volkswagen spent the most of any company
on CAV technology, followed by Samsung, Ford, Toyota,
BMW, and Audi. Spending the least on CAVs that year were Ap-
ple, GM, and Uber.
62
Recently, Tesla poured $1.5 billion in just
one year into the research and development of its most
advanced cars. It may also be the most synergistic tech/automo-
tive company of the bunch, focusing on both the hardware (car)
and the software (smart system).
63
A shift in dominance from traditional automobile manufac-
turers to tech companies would have a tremendous impact on
the economy. If traditional manufacturers become secondary
or partners to tech companies, there will be repercussions
throughout the supply chain and economic environment. It
may also be true that CAVs, thought of as software products,
may be subject to different kinds of policies and legislation, as
is beginning to happen with smartphones and medical device
apps. Tech companies also bring different corporate and profes-
sional cultures and business models (e.g., the commoditization
of collected data) into the auto industry. The economics of trans-
portation will shift, not just with new technology, but with new
players and approaches.
Ride-hailing and vehicle-related services
One of the most dramatic shifts in the transportation economy in
recent years has been the rise of ride-hailing services, and com-
panies such as Lyft and Uber. CAVs may have an equally dra-
matic effect on who provides these services. In contrast to today,
CAV ride-hailing vehicles will likely be owned by companies.
Costs for these companies will include not just the cost of vehi-
cles but other costs of vehicle ownership—car cleaning, mainte-
nance, and insurance, as well as customer-focused coordination
and services. The cost model of ride-hailing as a service may
increasingly resemble that of today’s rental car companies.
The commercialization and privatization of ride-hailing will also
impact the need for, and prevalence of, public transportation. If
CAVs as a service move into the private sector, as air transpor-
tation has today, the need for public vehicle transportation may
diminish. We currently do not know how the balance of public
transportation, private ownership of vehicles, and commercial
ride-hailing options will shift. Modeling these shifts as they occur,
as well as their economic repercussions, will be critical to inform
economic policy and incentives with respect to CAVs.
The data collected by CAVs will also bring new economic op-
portunities. As with other connected systems, some collected
CAV data can be sold and used for purposes other than driving.
One can imagine fleets of CAVs collecting all kinds of data—im-
ages, audio, video, environmental information, etc. —for a wide
variety of applications, just as Google Earth data are used for
more than maps.
Imagine new services that might use CAV data: car companies
collecting weather information for their internal autonomous
systems could sell it to weather services that could share near-
perfect and real-time weather information across any CAV-
populated location. In the absence of restriction, CAV passenger
data could be used for marketing, enabling the purchase of stra-
tegically placed ads, sponsored destinations, etc. In particular,
the availability of dynamic surveillance data from CAVs may
allow CAVs to double as surveillance systems, capturing
ll
OPEN ACCESS
Patterns 3, January 14, 2022 11
Review
everyday movement and patterns that were previously private.
Doing so sets up incompatibilities between strategies that pro-
mote individual privacy and strategies that promote economic
growth, requiring prioritization and risk mitigation from policy-
makers developing CAV social controls promoting the public in-
terest.
Traditional transportation-related industries will also be
impacted by increasing vehicle autonomy. As CAVs usher in a
shift from private ownership of vehicles to ride services, insur-
ance for individuals may look more like today’s renter’s insur-
ance than owner’s insurance, with primary costs moving to the
ride service industry. The models used by insurance companies
to determine premiums will change as well. Insurance com-
panies currently collect premiums based on how ‘‘costly’’ they
believe a driver to be, i.e., how much they would need to pay if
there were some kind of accident. CAV technology promises
more safety and, with it, the potential for less expensive pre-
miums under the current model. KPMG predicts that autono-
mous systems will reduce accident frequency by 90% by
2050.
64
On the other hand, the accidents that do happen could
involve more costly repairs. For example, sensors, already
becoming common for rear end detection, are much more costly
to replace than a simple bumper. The upshot is that while these
accidents may be less frequent, they may be much more
expensive.
All of this means that the economic impact of CAVs on insur-
ance will be subject to competing influences: fewer accidents,
more expensive repairs, possibly greater medical costs because
accidents may be more severe (from lighter cars and higher
speeds). Combining these influences with plausible model in-
stantiations indicates that overall losses from accidents could
fall by 63%—22 billion USD—by 2050.
64
Similar modeling by De-
loitte predicts that auto insurance premiums will decrease by
almost 30% of current levels.
65
Workforce evolution
Not surprisingly, CAVs will greatly impact the transportation
workforce. In addition to those employed in the manufacture,
maintenance, repair, sales, and other aspects of the automotive
industry, many Americans today are employed as drivers. Truck
drivers, delivery services, chauffeurs, bus drivers, taxi drivers,
and others may be replaced in the near-to-longer term with
CAVs and last-mile robotic delivery options—an estimated 5
million jobs in the current economy would be lost if CAVs were
prevalent today. But it will take a decade or more for level 5
CAVs to be prevalent and they will bring new jobs as well as elim-
inate current jobs. Both trends will impact the economy, as will
the local and federal political environments in which workforce
planning and related decisions are made.
Consider trucking. Currently, the trucking industry, which
moves 71% of all freight in America, is valued at $700 billion
USD.
66
Truck drivers spend days on long-haul drives across
the country, with the average professional long-haul trucker log-
ging more than 100,000 miles per year, or 274 miles per day.
66
The majority of these miles are accumulated on long, straight
stretches of highway. These portions of roads and driving are
currently the easiest and safest for CAVs. They have consistent
speeds, fewer distractions, such as pedestrians and stops,
and little complicated maneuvering. Moreover, such roads are
ideal for platooning.
The delivery industry is beginning to see more and more semi-
autonomous trucks navigating the highways. At the end of 2019,
California’s Department of Motor Vehicles announced that they
would permit testing and deployment of driverless trucks for
commercial use.
67
In 2021, after testing operations with ‘‘super-
vised autonomy’’ (autonomous runs with a driver on board who
can take over) in several states, companies, such as TuSimple,
are beginning to deploy autonomous trucks without drivers on
board.
68
Over the next decade, semi-autonomous and autono-
mous trucks will become more and more prevalent.
Passenger trips will also shift CAV-related jobs with some new
CAV-related jobs reflecting traditional jobs in current rental com-
panies: maintenance, repairs, regular cleanup, etc. Ride-hailing
services of the future may provide concierge services that cater
to passengers who want food, rest, work options, or entertain-
ment on their trips.
As software design, computation, and robotics become a
more dominant part of CAV manufacturing, automotive com-
panies may also shift their workforces to include a larger
proportion of data and computer scientists, roboticists, engi-
neers, materials scientists, and others, with a corresponding
impact on budgeting, salaries, and needed skills. CAV me-
chanics may focus on the software and robotics aspects of ailing
CAVs at least as much as the hardware, expanding needed skills.
It is only 30–40 years away, but it is a brave new world.
Market leadership, services, and workforce are only three
areas of economic impact for CAVs. There are many more that
will have economic repercussions—changes to land-use and
the built environment, economic impacts of new commuting
and travel patterns, use of CAVs for work and entertainment,
etc. Public-focused policymakers and their academic col-
leagues will need to employ expanded and accurate models
and data to inform economic policy for a future in which CAVs
are prevalent. An excellent example of how complex this
modeling can be and how many stakeholders must be involved
is found in a report by Shaheen et al.
69
Strategies that help lay
the groundwork for accurate economic modeling and to promote
economic growth include:
dGather and make accessible CAV-related economic data
that can be used for economic modeling by academics
and the public sector
dConvene expert groups to research, model, predict, and
report economic impacts of CAVs
dEncourage the development of new services that uti-
lize CAVs
SYNERGIES AND INCOMPATIBILITIES IN THE CAV
IMPACT UNIVERSE FRAMEWORK
‘‘Case study of a CAV impact universe framework that promotes
environmental sustainability, public protections, and economic
growth’’ describes a diverse set of goals and strategies that
a policymaker seeking to create CAV social controls in the public
interest might consider. Structuring them as an impact universe
provides a tool for understanding their trade-offs by putting
diverse goals and strategies into a single framework and weigh-
ing them in context, instead of considering one goal at a time.
ll
OPEN ACCESS
12 Patterns 3, January 14, 2022
Review
This helps the policy maker avoid heading down a path based on
a single goal only to find out that her strategies undermine
another goal in unanticipated ways.
The goals and strategies described in ‘‘Case study of a CAV
impact universe framework that promotes environmental sus-
tainability, public protections, and economic growth’’ are exem-
plars that would underlie more specific and targeted social con-
trols. But even at this level of generality, it is clear that some goals
and strategies are synergistic, and some are incompatible. The
goals and strategies from ‘‘Case study of a CAV impact universe
framework that promotes environmental sustainability, public
protections, and economic growth’’ are listed in Table 1.
Given a set of goals and strategies, a policymaker can deter-
mine which are synergistic and which potentially work against
each other. Some synergies in Table 1 include:
dInvesting in eco-driving research (strategy for E1) will pro-
mote public safety (goal P1). This strategy will also promote
environmental sustainability (goal E3) byreducing emissions
(strategy for E3) as well as reducing e-waste (strategy for E3)
in that eco-driving promotes a longer life for CAVs
dCollecting data on CAV accidents (strategy for P1) will
inform better cybersecurity (goal P2), as well as increase
the accuracy of economic models (strategy for Econ 1)
dMinimizing e-waste (strategy for E3) will create new CAV
services (strategy for Econ 2) and promote more sustain-
able materials usage (strategy for E2)
When goals and strategies work against each other, it will be
important for policymakers to tailor or revise their strategies
and the operational social controls that affect them to minimize
incompatibilities. For example, in Table 1.
dPromoting personal privacy (goal P3) may translate into
more complex software architectures, more testing, and
additional mechanisms to support user control and trans-
parency. This may increase costs and time-to-market for
manufacturers, slowing the potential for economic growth
(goal Econ 2)
dIncreased privacy controls (strategy for P3) and strength-
ened security standards (goal P2) may also limit new
avenues for CAV services, such as dynamic surveillance
(strategy for Econ 2), impacting the growth potential of
the CAV economy
Note that all strategies will need to be prioritized, and different
stakeholders working on the same policy interventions may pri-
oritize them differently. The process of making sausage out of
these perspectives, priorities, and dependencies is hard to sys-
temize, which makes the development of an impact universe
necessarily non-deterministic. But visualizing critical information
that describes synergistic and incompatible dependencies and
then trying to minimize the ‘‘friction’’ in the system can help.
One way to visualize the synergies and incompatibilities of a
set of goals and strategies is to represent them as interacting
gears: each gear is a goal or strategy, and movement of one
gear (indicated by an arrow) may impact the movement of other
gears. When various goals and strategies are synergistic, they
drive one another (i.e., interacting gears can move forward
simultaneously); when they are incompatible, their movement
is mutually limiting (i.e., forward motion of one gear deters for-
ward motion of another). In Figure 1, synergistic movements
for some goals and strategies in Table 1 are visualized by two
sets of gears, and incompatible movement is visualized by the
gears in Figure 2.
Table 1. Goals and strategies in the CAV impact universe
Overarching
goals Goals Strategies
Environmental
sustainability
reduce
emissions (E1)
strengthen federal
emission standards
create incentives
to cut emissions
invest in eco-
driving research
promote
sustainable
materials
usage (E2)
invest in electric
battery research
incentive use of
sustainable
components in CAVs
minimize
e-waste (E3)
collect information
on levels and
amount of e-waste
incentivize recycling and
repurposing of CAV
electronic components
promote a
sustainable
built
environment (E4)
create a request for
study to recommend
planning guidelines for
the built environment
Public
protections
promote CAV
safety (P1)
create a request for
study to recommend best
practice and standards for
V2V and V2I engagement
require and coordinate
data collection on accidents
for autonomous vehicles
driving on public roads
promote CAV
cybersecurity (P2)
invest in cybersecurity
research that improves
security practice and
standards for CAVs
promote personal
privacy in CAVs (P3)
require transparency and
consumer control for non-
operational CAV data
Economic
growth
gather and closely
model CAV impacts
on the economy
(Econ 1)
gather and make
accessible CAV-related
economic data that can
be used for economic
modeling by academics
and the public sector
convene expert groups
to research, model, predict,
and report economic
impacts of CAVs
use policy to
promote economic
growth and
stability (Econ 2)
encourage the development
of new services that
involve CAVs
ll
OPEN ACCESS
Patterns 3, January 14, 2022 13
Review
As with other parts of the digital world, assessing the informa-
tion presented by the impact universe framework must be done
by humans. Their choices about which goals are important,
which strategies should prevail, and how to target specific social
controls that affect them will, and should, subjectively reflect
their priorities and the context in which their approaches must
be effectively deployed.
BEYOND CAVs: CREATING AN IMPACT UNIVERSE FOR
OTHER IoT DEVICES AND SYSTEMS
The purpose of an impact universe framework is to provide an in-
tegrated, contextual, multi-disciplinary, and holistic view of the
interdependencies of the goals and strategies that underlie po-
Figure 1. Compatible CAV goals and
strategies
Optimization of the central goal or strategy can
promote other goals and strategies in each set
of gears.
tential social controls for connected tech-
nologies. After examining an impact uni-
verse framework, specific social controls
can better leverage the context and time
frames in which they will operate, and de-
ployed to promote the stakeholder’s
desired outcomes.
The impact universe framework for
CAVs demonstrates how complex and
nuanced the impacts of an IoT device or system may be and
how complex it is to understand them holistically. However,
CAVs are only one example. Impact universe frameworks can
be developed for any IoT ‘‘thing’’ and can also be developed
for any type of stakeholder (not just policymakers) and any set
of stakeholder goals (not just those described here). Although
the impact universe framework for a smart toaster, a Fitbit, or a
smart grid will look different than the impact universe we have
described for a CAV, the same approach will provide critical in-
sights needed to better evaluate the benefits, risks, and implica-
tions of social controls for the target IoT device.
Clustering and scoping may help. There are classes of IoT
things for whom security vulnerabilities can lead to catastrophic
results. Hacking a car or a pacemaker in the worst-case can kill a
user. The impact universe for these devices must expose the re-
lationships between security and all other interests and rank pri-
orities in a way that promotes risk mitigation above all else. Other
IoT devices could be considered together to promote specific
interests. Take user privacy. Smart doorbells, smart home assis-
tants, CAVs, etc. all collect personal data. Opt-in standards, ac-
cess policies that restrict sharing without user consent, and
product and system design alternatives that do not collect per-
sonal information could all be explored to promote user privacy.
Creating an impact universe for classes of products or services
with respect to a specific public interest profile can help create
needed social controls in the IoT.
Complicating it all is the need to create these controls while
technologies are dynamically evolving. As we saw for CAVs,
we often do not immediately know the implications of new tech-
nologies. If we are lucky, we may have experience with some
strategies that promote a stakeholder’s goals. For example,
the ASIF model and experience with current emission standards
provide a solid footing from which to develop strategies and tar-
geted social controls that promote environmental sustainability
goals for CAVs.
On the other hand, many of the specifics needed to develop
effective social controls may be unavailable, premature, or
hard to quantify when decisions must be made. For example,
many of the specifics needed for good economic growth models
for CAVs may not be known. This is true for many IoT products,
not just CAVs: the prevalence, uptake, and specifics of use and
ownership for a wide variety of products will be important in
developing accurate assessments of their lifetime, their potential
Figure 2. Incompatible CAV goals and strategies—optimization of
promotion of personal privacy limits potential economic growth and
some new businesses
ll
OPEN ACCESS
14 Patterns 3, January 14, 2022
Review
for spawning additional goods and services, their potential
contribution to planetary e-waste, and their economic trajectory.
The impact universe framework demonstrates that both
known and unknown, easily quantifiable, and hard-to-specify
impacts matter, and that all are needed to assess the benefits
and risks of devices and systems in the IoT. Developing an
impact universe framework provides a useful approach for as-
sessing these benefits and risks because it requires a stake-
holder to identify them in aggregate. It encourages them to focus
beyond the quantifiable information before them, and beyond
the single metric valuation that often characterizes the develop-
ment of social controls for the IoT. In this, the impact universe
framework provides a tool for stakeholders to more effectively
guide technological innovation, so that the design, development,
use, and standardization of IoT products and services advances
the public interest, and ultimately a better IoT.
THE IMPACT UNIVERSE FRAMEWORK—CONTINUING
THE CONVERSATION
The challenging and sometimes frustrating aspect to developing
the impact universe framework as a structured approach is that
all terms are subjective and open to interpretation: What is the
public interest? What kinds of impacts? Which priorities mat-
ter most?
All of these questions must be addressed in context, and all
depend on the priorities and perspectives of the stakeholders.
What is considered in the public interest varies per group, per
government, per worldview. The importance of individual privacy
is different in the EU, US, and China. Safety is defined differently
by companies and consumers. Impact is hard to define, and hard
to calibrate in terms of the effect an action will have on an individ-
ual’s life.
In developing the impact universe as a way to expose the re-
lationships between potential actions in a real-world environ-
ment, we lack deterministic methodology. Our challenges to
navigate the ‘‘messiness’’ of the real world of decision making
and not oversimplify the nuanced relationships between various
impacts are shared with other approaches—value sensitive
design, systems engineering, and various models from opera-
tions research. In that sense, this paper contributes to the con-
versation about the need for context, the need to prioritize with
partial information, and the development of tools that can help
stakeholders manage potential tangible and intangible out-
comes in the IoT.
Going forward, it would be useful to add new case studies of
impact universe frameworks for additional IoT things (or classes
of things) to explore the benefits and limitations of this approach.
It would also be useful to explore how the impact universe frame-
work might be combined with other methodologies and tools for
analysis.
In seeking to define tools for policymakers, we also need to
advance the goals of the policies we are creating. What is in
the public interest for the IoT? Are there fundamental digital
rights that need to be articulated for citizens and that can form
the basis of the social controls we develop? Considerable
work has been done in the EU to define the digital rights of its cit-
izens, with multiple digital rights initiatives leading to the General
Data Protection Regulation. A core set or ‘‘bill’’ of digital rights
does not currently exist within the US. It is only when we define
which digital rights are fundamental, that we can then begin to
articulate the responsibilities of the public and private sectors
to promote those rights.
We are just at the beginning of this discussion, particularly in
the US. Continuing the conversation is critical if we want to
derive the benefits and leverage the opportunities of the IoT,
minimize its risks, and empower a public who can thrive in our
cyber-social world.
ACKNOWLEDGMENTS
All participants were funded by the Radcliffe Institute for Advanced Study at
Harvard University as part of Berman’s appointment as the 2019–2020 Kather-
ine Bessell Hampson Fellow. We are grateful to Radcliffe for the opportunity to
undertake this work. This paper benefitted greatly from discussions and feed-
back from many colleagues and friends, as well as the reviewers of this piece.
We are grateful to the Radcliffe Fellows class of 2019–2020, and in particular to
Liz Chiarello, Anne Higonnet, and Alexandra Lahav for their feedback and in-
sights. The project was conceptualized in part based on conversations with
Josh Greenberg and Danny Goroff and improved by suggestions from Amy
Brand and Xiao-Li Meng. Many thanks to Alyssa Goodman for illuminating
self-driving car ‘‘field studies’’.
AUTHOR CONTRIBUTIONS
F.B. wrote, conceptualized, and directed the research for this paper. E.C., A.J.,
and W.M. were undergraduate Radcliffe Research Partners during 2019–2020,
engaged in discussions on the topic, and developed briefings that contributed
to the paper.
DECLARATION OF INTERESTS
Francine Berman is a member of the advisory board for Patterns.
REFERENCES
1. A 1960’s Cartoon Hila riously Mocks America’s Car Obsession. Bloomberg
CityLab. [Online] 2016. https://www.citylab.com/transportation/2016/07/
a-1960s-cartoon-hilariously-mocks-americas-car-obsession/491558/.
2. Automated Vehicles for Safety. U.S. Department of Transportation. [On-
line] https://www.nhtsa.gov/technology-innovation/automated-vehicles-
safety#topic-road-self-driving.
3. Eliot, L. (2019). Amalmagating of Operational Design Domans (ODDs) for
AI Self-Driving Cars (Aitrends). https://www.aitrends.com/selfdriving
cars/amalgamating-of-operational-design-domains-odds-for-ai-self-
driving-cars/.
4. Litma n, T. (2021). Autonomous Vehicle Implementation Predictions, Victo-
ria Transport Policy Institute Report. https://www.vtpi.org/avip.pdf.
5. Galland, D. (2017). 10 Million Self-Driving Cars will Hit the Road by 2020—
Here’s How to Profit (Forbes). https://www.forbes.com/sites/oliviergarret/
2017/03/03/10-million-self-driving-cars-will-hit-the-road-by-2020-heres-
how-to-profit/#28dd309f7e50.
6. Chesterton, A. (2018). How Many Cars are there in the World? (Carsguide).
https://www.carsguide.com.au/car-advice/how-many-cars-are-there-in-
the-world-70629.
7. Self-Driving Car Technology: How do Self-Driving Cars Work?. Landmark
Dividend; 2021. [Online] https://www.landmarkdividend.com/self-driving-
car/.
8. Ravind ra, S. (2017). The Machine Learning Algorithms Used in Self-Driving
Cars (KDnuggets). https://www.kdnuggets.com/2017/06/machine-
learning-algorithms-used-self-driving-cars.html.
9. Gupta, A. (2018). Machine Learning Algorithms in Autonomous Driving
(IIoT World). https://iiot-world.com/machine-learning/machine-learning-
algorithms-in-autonomous-driving/.
ll
OPEN ACCESS
Patterns 3, January 14, 2022 15
Review
10. Lambert, F. (2021). Tesla Releases Big New Software Update with Dis-
ney+, Car Wash Mode, Hotspot, and More (Elektrek). https://electrek.
co/2021/07/29/tesla-releases-big-software-update-disney-car-wash-
mode-hotspot/.
11. Hall, K. (2009). Modern Luxury Vehicles Claimed to Feature more Software
than a Fighter Jet (MotorAuthority). https://www.motorauthority.com/
news/1026505_modern-luxury-vehicles-claimed-to-feature-more-
software-than-a-fighter-jet.
12. Brown, D. (2019). Possibility or Pipe Dream: How Close are we to Seeing
Flying Cars? (USA Today). https://www.usatoday.com/story/tech/2019/
11/04/flying-cars-uber-boeing-and-others-say-theyre-almost-ready/
4069983002/.
13. Anderson , J., Kalra, N., Stanley, K., Sorensen, P., Samaris, C., and Oluwa-
tola, T. (2016). Autonomous Vehicle Technology: A Guide for Policy-
makers, Rand Corporation Report. https://www.rand.org/content/dam/
rand/pubs/research_reports/RR400/RR443-2/RAND_RR443-2.pdf.
14. Schipper, L. (2002). Sustainable urban transport in the 21st century: a n ew
agenda. Transp. Res. Rec. 1792, 12–19. https://doi.org/10.3141/1792-02.
15. Gardner, G. (2016). Why Most self-Driving Cars will Be electric (USA
Today). https://www.usatoday.com/story/money/cars/2016/09/19/why-
most-self-driving-cars-electric/90614734/.
16. Will Electric Vehices Really Create a Cleaner Planet? Thomson Reuters.
[Online] https://www.thomsonreuters.com/en/reports/electric-vehicles.
html.
17. Light Duty Vehicle Emissions. United States Environmental Protection
Agency.[Online] https://www.epa.gov/greenvehicles/light-duty-vehicle-
emissions#standards.
18. O’Kane, S. (2019). Automakers Still Want to Lower Emissions Standards in
the US (The Verge). https://www.theverge.com/2019/6/7/18656986/
automakers-lower-emissions-standards-us-environment-
pollution-trump.
19. Gawron, J., Keoleian, G., De Kleine, R., Wallington, T., and Kim, H.C.
(2018). Life cycle assessment of connected and automated vehicles:
sensing and computing subsystem and vehicle level effects. Environ.
Sci. Technol. 52, 3249–3256. https://doi.org/10.1021/acs.est.7b04576.
20. Shaheen, S., Martin, E., and Finson, R. (2012). Ecodriving and Carbon
Footprinting: Understanding How Public Education Can Reduce Green-
house Gas Emissions and Fuel Use (Mineta Transportation Institute).
https://transweb.sjsu.edu/sites/default/files/2808-ecodriving-
greenhouse-gas-emissions-fuel-use-public-education.pdf.
21. Hutchings, R., and Tyrrell, K. (2018). Putting the Brakes on Idling Vehicles,
National Conference of State Legislatures. https://www.ncsl.org/
research/environment-and-natural-resources/putting-the-brakes-on-
idling-vehicles.aspx.
22. National Cooperative Highway Research Program. [Online] http://www.
trb.org/NCHRP/NCHRP.aspx.
23. Desjardins, J. (2016). Here Are the Raw Materials We Need to Fuel the
Electric Car Boom (Business Insider). https://www.businessinsider.com/
materials-needed-to-fuel-electric-car-boom-2016-10.
24. Dengler, R. (2018). Global Trove of Rare Earth Metals Found in Japan’s
Deep-Sea Mud (Science News). https://www.science.org/content/
article/global-trove-rare-earth-metals-found-japans-deep-sea-mud.
25. Rare-Earth Element. Wikipedia. [Online] https://en.wikipedia.org/wiki/
Rare-earth_element.
26. Sella, A. (2016). Insight: Rare-Earth Metals (TRT World). https://www.
youtube.com/watch?v=UvQMiqqzcZE.
27. Subin. (2021). The New U.S. Plan to Rival China and End Cornering of Mar-
ket in Rare Earth Metals (CNBC). https://www.cnbc.com/2021/04/17/the-
new-us-plan-to-rival-chinas-dominance-in-rare-earth-metals.html.
28. Time to Seize Opportunity, Tackle Challenge of E-Waste. UN Environ-
mental Program. [Online] 2019. https://www.unenvironment.org/news-
and-stories/press-release/un-report-time-seize-opportunity-tackle-
challenge-e-waste.
29. Global E-Waste—Statistics and Facts. Statista. 2021. [Online] https://
www.statista.com/topics/3409/electronic-waste-worldwide/.
30. LeBlanc, R. Car Recycling Facts and Figures. The Balance Small Busi-
ness. 2019. [Online] https://www.thebalancesmb.com/how-are-cars-
recycled-2877944.
31. Hall, D., and Lutsey, N. (2018). Effects of Battery Manufacturing on Electric
Vehicle Life-Cycle Greenhouse Gas Emissions (International Council on
Clean Transportation). https://theicct.org/publications/EV-battery-
manufacturing-emissions.
32. Neubauer, J., Smith, K., Wood, E., and Pesaran, A. (2015). Identifying and
Overcoming Critical Barriers to Widespread Second Use of PEV Batteries
(National Renewable Energy Laboratory Report). https://doi.org/10.2172/
1171780.https://www.nrel.gov/docs/fy15osti/63332.pdf.
33. Thurbon, R. (2019). Ford Executive Says self-Driving Cars will only have a
Four-Year Lifespan (TechSpot). https://www.techspot.com/news/81606-
ford-executive-self-driving-cars-only-have-four.html.
34. Auto Recycling Facts and Figures. The Balance Small Business. 2019.
[Online] https://www.thebalancesmb.com/auto-recycling-facts-and-
figures-2877933/.
35. End-of Life Vehicle Directives. ScienceDirect. [Online] https://www.
sciencedirect.com/topics/engineering/end-of-life-vehicle-directives.
36. Parking Has Eaten American Cities. Bloomberg CityLab. [Online] 2018.
https://www.bloomberg.com/news/articles/2018-07-24/the-overparked-
states-of-america.
37. Nader, R. (1965). Unsafe at any Speed (Grossman).
38. Branch, A.. National traffic and motor vehicle safety act. Britannica. 2015.
[Online]https://www.britannica.com/topic/National-Highway-Traffic-Safety-
Administration
39. Safety and Health. U.S. Department of Transportation. [Online] https://
www.transportation.gov/policy/transportation-policy/safety.
40. Jia, D., Lu, K., and Wang, J. (2016). A survey on platoon-based vehicular
cyber-physical systems. IEEE Commun. Surv. Tutorials 18, 263–284.
https://doi.org/10.1109/COMST.2015.2410831.
41. Bauman, M. (2017). Why Waiting for Perfect Autonomous Vehicles May
Cost Lives (The RAND Blog). https://www.rand.org/blog/articles/2017/
11/why-waiting-for-perfect-autonomous-vehicles-may-cost-lives.html.
42. Dietma yer, K., Mauer, M., Gerdes, C., Lenz, B., and Winner, H. (2016). Pre-
dicting of machine perception for automated driving. Autonomous Driving:
Technical, Legal and Social Aspects (Springer). https://doi.org/10.1007/
978-3-662-48847-8.
43. Accident & Incident Data. Federal Aviation Administration. [Online] 2019.
https://www.faa.gov/data_research/accident_incident/.
44. Schneier, B. (2018). Click Here to Kill Everybody: Security and Survival in a
Hyper-Connected World (W.W. Norton & Company).
45. Automotive Cybersecurity Incidents Doubled in 2019, up 605% since
2016. Help Net Security. [Online] January 6, 2020. https://www.
helpnetsecurity.com/2020/01/06/automotive-cybersecurity-incidents/.
46. Greenb erg, A. (2015). Hackers Remotely Kill a Jeep on the Highway—With
me in it (Wired). https://www.wired.com/2015/07/hackers-remotely-kill-
jeep-highway/.
47. Sheehan, B., Murphy, F., Mullins, M., and Ryan, C. (2019). Connected and
autonomous vehicles: a cyber-risk classification framework. Transp. Res.
A Policy Pract. 124, 523–536. https://doi.org/10.1016/j.tra.2018.06.033.
48. Cyber Security and Resilience of Smart Cars. European Union Agency for
Cybersecurity. [Online] January 13, 2017. https://www.enisa.europa.eu/
publications/cyber-security-and-resilience-of-smart-cars.
49. Motor Vehicles Increasingly Vulnerable to Remote Exploits. FBI Internet
Crime Complaint Center. [Online] March 17, 2016. https://www.ic3.gov/
Media/Y2016/PSA160317.
50. Auto-ISAC. Automotive Information Sharing and Analysis Center. [Online]
https://automotiveisac.com.
ll
OPEN ACCESS
16 Patterns 3, January 14, 2022
Review
51. Anderson, R. (2017). When Safety and Security Become One (Light
Blue Touchpaper). https://www.lightbluetouchpaper.org/2017/06/01/
when-safety-and-security-become-one/.
52. Richter, F. (2017). Big Data on Wheels (Statista Infographics). https://
www.statista.com/chart/8018/connected-car-data-generation/.
53. Jacobson, L. (2007). Vehicle infrastructure integration for privacy policies
framework. The Institutional Issues Subcommittee of the National VII Coa-
lition (United States: National Surface Transportation Infrastructure
Financing Commission).
54. Data protection report. Norton Rose Fulbright blog network. [Online] 201 7.
https://www.dataprotectionreport.com/2017/07/the-privacy-
implications-of-autonomous-vehicles/.
55. Privacy & Data Security Update (2016). Federal Trade Commission. [On-
line] https://www.ftc.gov/reports/privacy-data-security-update-2016.
56. Consumer Privacy Protection Principles. AutoAlliance [Online] 2018.
https://autoalliance.org/wp-content/uploads/2017/01/
Consumer_Privacy_Principlesfor_VehicleTechnologies_Services-03-21-
19.pdf.
57. Car Ownership Statistics. ValuePenguin [Online] 2021. https://www.
valuepenguin.com/auto-insurance/car-ownership-statistics.
58. Curley, R. (2019). Global Ride Sharing Industry Valued at More than $61
Billion (Business Traveller).
59. Amadeo, K. (2019). The Economic Impact of the Automotive Industry (The
Balance). https://www.thebalance.com/economic-impact-of-automotive-
industry-4771831.
60. Dingle r, R. (2019). Self-Driving Car Fleets: Transportation as a Service (The
Medium). https://medium.com/adventures-in-consumer-technology/the-
new-business-model-of-self-driving-car-fleets-a14d94d61148.
61. Lipson, H., and Kurman, M. (2016). Driverless. Chapter 3 (MIT Press).
62. Geske, D.A. (2019). Look at the Investment in Self-Driving Cars (Interna-
tional Business Times). https://www.ibtimes.com/look-investment-self-
driving-cars-who-has-spent-most-2848289.
63. Trefis Team (2020). How Does Tesla Spend its Money? (Forbes). https://
www.forbes.com/sites/greatspeculations/2020/01/03/how-does-tesla-
spend-its-money/?sh=49da041625da.
64. The Chaotic Middle. KPMG [Online] 2017. https://institutes.kpmg.us/
content/dam/institutes/en/manufacturing/pdfs/2017/chaotic-middle-
autonomous-vehicle-paper.pdf.
65. Matley , J., Gandhi, M., Carrier, M., Tomopoulos, P., Peterson, S. Future of
automotive insurance in the new mobility ecosystem. Deloite. 2016. [On-
line] https://www2.deloitte.com/us/en/pages/consulting/articles/
automotive-insurance-future-mobility-ecosystem.html.
66. John, S. 11 Incredible Facts about the $700 Billion US Trucking Industry.
Markets Insider. https://markets.businessinsider.com/news/stocks/
trucking-industry-facts-us-truckers-2019-5-1028248577 2019.
67. Hawkins, A. (2019). Light Duty Autonomous Vehicles Get the Green Light
in California (The Verge). https://www.theverge.com/2019/12/18/
21028288/self-driving-cars-light-duty-trucks-california-dmv.
68. Acke rman, E. (2021). This Year Autonomous Trucks Will Take to the Roads
with No One on board (IEEE Spectrum). https://spectrum.ieee.org/this-
year-autonomous-trucks-will-take-to-the-road-with-no-one-on-board.
69. Sha heen, S., Cohen, A., Broader, J., Davis, R., Brown, L., Neelakantan, R.,
and Gopalakrishna, E. (2020). Mobility on Demand Planning and Imple-
mentation: Current Practices, Innovations, and Emerging Mobility Futures
(National Transportation Library). https://rosap.ntl.bts.gov/view/
dot/50553.
ll
OPEN ACCESS
Patterns 3, January 14, 2022 17
Review