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\begin{document}

\title{Unnamed Article}



\author[1]{Monika}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 08, 2024}


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\section*{Emerging technologies}

{\label{245994}}

Gra\selectlanguage{polish}ż\selectlanguage{english}yna Paliwoda-P\selectlanguage{polish}ę\selectlanguage{english}kosz\textsuperscript{1}

\textsuperscript{1}Uniwersytet Ekonomiczny w Krakowie

January 12, 2019

\par\null\par\null

\subsubsection*{Sources:}

{\label{528971}}

\url{https://en.wikipedia.org/wiki/Internet\_of\_things}

\url{https://en.wikipedia.org/wiki/3D\_printing}

\url{https://en.wikipedia.org/wiki/Self-driving\_car}

Fig.1
\url{https://en.wikipedia.org/wiki/Internet\_of\_things\#/media/File:Internet\_of\_Things.jpg}

Fig.2
\url{https://en.wikipedia.org/wiki/Internet\_of\_things\#/media/File:Internet\_of\_Things.svg}

Fig.3
\url{https://en.wikipedia.org/wiki/3D\_printing\#/media/File:MakerBot\_ThingOMatic\_Bre\_Pettis}.

jpg

Fig.4
\url{https://en.wikipedia.org/wiki/3D\_printing\#/media/File:84530877\_FillingSys\_(9415669149)}

.jpg

Fig.5
\url{https://upload.wikimedia.org/wikipedia/commons/b/ba/3D\_selfie\_in\_1\_20\_scale\_in\_my\_palm\_}

after\_one\_spray\_of\_clear\_satin\_acrylic\_varnish\_IMG\_4751\_FRD.jpg

Fig.6
\url{https://en.wikipedia.org/wiki/3D\_printing\#/media/File:HCC\_3D\_printed\_turbine\_view\_1.jpg}

Fig.7 \url{https://en.wikipedia.org/wiki/Self-driving\_car}

\par\null

Table 1 (\texttt{Wang,\ 2018})

\par\null

\section*{Internet of things}

{\label{706133}}

\subsection*{Outline}

{\label{938776}}

The Internet of things (IoT) is the network of physical devices,
vehicles, home appliances, and other items

embedded with electronics, software, sensors, actuators, and
connectivity which enables these things to

connect, collect and exchange data. IoT involves extending Internet
connectivity beyond standard devices, such as desktops, laptops,
smartphones and tablets, to any range of traditionally dumb or
non-internetenabled physical devices and everyday objects. Embedded with
technology, these devices can communicate and interact over the
Internet, and they can be remotely monitored and controlled. With the
arrival of driverless vehicles, a branch of IoT, i.e. the Internet of
Vehicle starts to gain more attention.

\par\null

\subsection*{History}

{\label{847100}}

The definition of the Internet of things has evolved due to convergence
of multiple technologies, real-time analytics, machine learning,
commodity sensors, and embedded systems. Traditional fields of embedded
systems, wireless sensor networks, control systems, automation
(including home and building automation), and others all contribute to
enabling the Internet of things.

The concept of a network of smart devices was discussed as early as
1982, with a modified Coke machine at Carnegie Mellon University
becoming the first Internet-connected appliance, able to report its
inventory and whether newly loaded drinks were cold. MarkWeiser's 1991
paper on ubiquitous computing, ``The Computer of the 21st Century'', as
well as academic venues such as UbiComp and PerCom produced the
contemporary vision of IoT. In 1994, Reza Raji described the concept in
IEEE Spectrum as ``{[}moving{]} small packets of data to a large set of
nodes, so as to integrate and automate everything from home appliances
to entire factories''. Between 1993 and 1997, several companies proposed
solutions like Microsoft's at Work or Novell's NEST. The field gained
momentum when Bill Joy envisioned Device to Device (D2D) communication
as part of his ``Six Webs'' framework, presented at the World Economic
Forum at Davos in 1999.

The term ``Internet of things'' was likely coined by Kevin Ashton of
Procter \& Gamble, later MIT's Auto-ID

Center, in 1999, though he prefers the phrase ``Internet for things''.
At that point, he viewed Radio-frequency identification (RFID) as
essential to the Internet of things, which would allow computers to
manage all~individual things.

A research article mentioning the Internet of things was submitted to
the conference for Nordic Researchers~~in Logistics, Norway, in June
2002, which was preceded by an article published in Finnish in January
2002. The implementation described there was developed by Kary Fr\selectlanguage{ngerman}¨amling
and his team at Helsinki University of Technology and more closely
matches the modern one, i.e. an information system infrastructure for
implementing smart, connected objects (Fig. 1).

Defining the Internet of things as ``simply the point in time when more
`things or objects' were connected to the Internet than people'', Cisco
Systems estimated that IoT was ``born'' between 2008 and 2009, with the
things/people ratio growing from 0.08 in 2003 to 1.84 in 2010.

\par\null

\subsection*{Applications}

{\label{981571}}

\subsubsection*{Consumer applications}

{\label{881644}}

\emph{Smart home}

IoT devices are a part of the larger concept of home automation, which
can include lighting, heating and air conditioning, media and security
systems. Long term benefits could include energy savings by
automatically ensuring lights and electronics are turned off.

A smart home or automated home could be based on a platform or hubs that
control smart devices and

appliances. For instance, using Apple's HomeKit, manufacturers can get
their home products and accessories be controlled by an application in
iOS devices such as the iPhone and the Apple Watch. This could be a
dedicated app or iOS native applications such as Siri. This can be
demonstrated in the case of Lenovo's Smart Home Essentials, which is a
line of smart home devices that are controlled through Apple's Home
app~or Siri without the need for a Wi-Fi bridge. There are also
dedicated smart home hubs that are offered as standalone platforms to
connect different smart home products and these include the Amazon Echo,
Apple's HomePod, and Samsung's SmartThings Hub.

\emph{Elder care}

One key application of smart home is to provide assistance for those
with disabilities and elderly individuals. These home systems use
assistive technology to accommodate an owner's specific disabilities.
Voice control can assist users with sight and mobility limitations while
alert systems can be connected directly to cochlear

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}
\caption{{Figure 1: Internet of Things. ~
{\label{895888}}%
}}
\end{center}
\end{figure}

implants worn by hearing impaired users. They can also be equipped with
additional safety features. These~features can include sensors that
monitor for medical emergencies such as falls or seizures. Smart home
technology applied in this way can provide users with more freedom and a
higher quality of life.

\par\null

\subsubsection*{Commercial applications}

{\label{910781}}

\emph{Medical and healthcare}

The Internet of Medical Things (also called the internet of health
things) is an application of the IoT for

medical and health related purposes, data collection and analysis for
research, and monitoring. This `Smart Healthcare', as it can also be
called, led to the creation of a digitized healthcare system, connecting
available medical resources and healthcare services.~~

IoT devices can be used to enable remote health monitoring and emergency
notification systems. These health monitoring devices can range from
blood pressure and heart rate monitors to advanced devices capable of~
monitoring specialized implants, such as pacemakers, Fitbit electronic
wristbands, or advanced hearing aids. Some hospitals have begun
implementing ``smart beds'' that can detect when they are occupied and
when a patient is attempting to get up. It can also adjust itself to
ensure appropriate pressure and support is applied to the patient
without the manual interaction of nurses. A 2015 Goldman Sachs report
indicated that healthcare IoT devices ``can save the United States more
than \$300 billion in annual healthcare expenditures by increasing
revenue and decreasing cost.'' Moreover, the use of mobile devices to
support medical follow-up led to the creation of `m-health', used ``to
analyze, capture, transmit and store health statistics from
multiple~~resources, including sensors and other biomedical acquisition
systems''.

Specialized sensors can also be equipped within living spaces to monitor
the health and general well-being of senior citizens, while also
ensuring that proper treatment is being administered and assisting
people regain lost mobility via therapy as well. These sensors create a
network of intelligent sensors that are able to collect, process,
transfer and analyse valuable information in different environments,
such as connecting inhome monitoring devices to hospital-based systems.
Other consumer devices to encourage healthy living, such as connected
scales or wearable heart monitors, are also a possibility with the IoT.
End-to-end health monitoring IoT platforms are also available for
antenatal and chronic patients, helping one manage health vitals and
recurring medication requirements.

As of 2018 IoMT was not only being applied in the clinical laboratory
industry, but also in the healthcare

and health insurance industries. IoMT in the healthcare industry is now
permitting doctors, patients and

others involved (i.e. guardians of patients, nurses, families, etc.) to
be part of a system, where patient

records are saved in a database, allowing doctors and the rest of the
medical staff to have access to the

patient's information. Moreover, IoT-based systems are patient-centered,
which involves being flexible to

the patient's medical conditions. IoMT in the insurance industry
provides access to better and new types of dynamic information. This
includes sensor-based solutions such as biosensors, wearables, connected
health devices and mobile apps to track customer behaviour. This can
lead to more accurate underwriting and new pricing models.

\emph{Transportation}

The IoT can assist in the integration of communications, control, and
information processing across various transportation systems.
Application of the IoT extends to all aspects of transportation systems
(i.e. thevehicle, the infrastructure, and the driver or user). Dynamic
interaction between these components of a transport system enables inter
and intra vehicular communication, smart traffic control, smart parking,
electronic toll collection systems, logistic and fleet management,
vehicle control, and safety and road assistance. In Logistics and Fleet
Management for example, The IoT platform can continuously monitor the
location and conditions of cargo and assets via wireless sensors and
send specific alerts when management exceptions occur (delays, damages,
thefts, etc.). If combined with Machine Learning then it also helps in
reducing traffic accidents by introducing drowsiness alerts to drivers
and providing self driven cars too.

\emph{Building and home automation}

IoT devices can be used to monitor and control the mechanical,
electrical and electronic systems used in various types of buildings
(e.g., public and private, industrial, institutions, or residential) in
home automation and building automation systems. In this context, three
main areas are being covered in literature:

The integration of the Internet with building energy management systems
in order to create energy efficient and IOT driven ``smart buildings''.

The possible means of real-time monitoring for reducing energy
consumption and monitoring occupant

behaviors.~~

The integration of smart devices in the built environment and how they
might to know who to be used in

future applications.

\subsubsection*{Industrial applications}

{\label{247655}}

\emph{Manufacturing}

The IoT can realize the seamless integration of various manufacturing
devices equipped with sensing, identification,~~processing,
communication, actuation, and networking capabilities. Based on such a
highly integrated smart cyberphysical space, it opens the door to create
whole new business and market opportunities for manufacturing. Network
control and management of manufacturing equipment, asset and situation
management, or manufacturing process control bring the IoT within the
realm of industrial applications and smart manufacturing as well. The
IoT intelligent systems enable rapid manufacturing of new products,
dynamic response~to product demands, and real-time optimization of
manufacturing production and supply chain networks, by networking
machinery, sensors and control systems together.

Digital control systems to automate process controls, operator tools and
service information systems to

optimize plant safety and security are within the purview of the IoT.
But it also extends itself to asset

management via predictive maintenance, statistical evaluation, and
measurements to maximize reliability. Smart industrial management
systems can also be integrated with the Smart Grid, thereby enabling
real-time energy optimization. Measurements, automated controls, plant
optimization, health and safety management, and other functions are
provided by a large number of networked sensors.

The term industrial Internet of things (IIoT) is often encountered in
the manufacturing industries, referring to the industrial subset of the
IoT. IIoT in manufacturing could generate so much business value that it
will eventually lead to the fourth industrial revolution, so the
so-called Industry 4.0. It is estimated that in the~future, successful
companies will be able to increase their revenue through Internet of
things by creating new business models and improve productivity, exploit
analytics for innovation, and transform workforce. The potential of
growth by implementing IIoT may generate \$12 trillion of global GDP by
2030.

\emph{Agriculture}

There are numerous IoT applications in farming such as collecting data
on temperature, rainfall, humidity, wind speed, pest infestation, and
soil content. This data can be used to automate farming techniques, take
informed decisions to improve quality and quantity, minimize risk and
waste, and reduce effort required to manage crops. For example, farmers
can now monitor soil temperature and moisture from afar, and even~~apply
IoT-acquired data to precision fertilization programs.

In August 2018, Toyota Tsusho began a partnership with Microsoft to
create fish farming tools using the

Microsoft Azure application suite for IoT technologies related to water
management. Developed in part

by researchers from Kindai University, the water pump mechanisms use
artificial intelligence to count the

number of fish on a conveyor belt, analyze the number of fish, and
deduce the effectiveness of water flow

from the data the fish provide. The specific computer programs used in
the process fall under the Azure

Machine Learning and the Azure IoT Hub platforms.

\subsubsection*{Infrastructure
applications}

{\label{166556}}

Monitoring and controlling operations of sustainable urban and rural
infrastructures like bridges, railway

tracks and on- and offshore wind-farms is a key application of the IoT.
The IoT infrastructure can be used

for monitoring any events or changes in structural conditions that can
compromise safety and increase risk. IoT can benefit the construction
industry by cost saving, time reduction, better quality workday,
paperless workflow and increase in productivity. It can help in taking
faster decisions and save money with Real-Time Data Analytics. It can
also be used for scheduling repair and maintenance activities in an
efficient manner, by coordinating tasks between different service
providers and users of these facilities. IoT devices can also be used to
control critical infrastructure like bridges to provide access to ships.
Usage of IoT devices for monitoring and operating infrastructure is
likely to improve incident management and emergency response
coordination, and quality of service, up-times and reduce costs of
operation in all infrastructure related areas. Even areas such as waste
management can benefit from automation and optimization that could be
brought in by the IoT.

\emph{Metropolitan scale deployments}

There are several planned or ongoing large-scale deployments of the IoT,
to enable better management of cities and systems. For example, Songdo,
South Korea, the first of its kind fully equipped and wired smart city,
is gradually being built, with approximately 70 percent of the business
district completed as of June 2018. Much of the city is planned to be
wired and automated, with little or no human intervention.

Another application is a currently undergoing project in Santander,
Spain. For this deployment, two approaches have been adopted. This city
of 180,000 inhabitants has already seen 18,000 downloads of its
city~smartphone app. The app is connected to 10,000 sensors that enable
services like parking search, environmental monitoring, digital city
agenda, and more. City context information is used in this deployment so
as~ to benefit merchants through a spark deals mechanism based on city
behavior that aims at maximizing the impact of each notification.

Other examples of large-scale deployments underway include the
Sino-Singapore Guangzhou Knowledge City; work on improving air and water
quality, reducing noise pollution, and increasing transportation
efficiency in San Jose, California; and smart traffic management in
western Singapore. French company, Sigfox, commenced building an
ultra-narrowband wireless data network in the San Francisco Bay Area in
2014, the first business to achieve such a deployment in the U.S. It
subsequently announced it would set up a total of 4000 base stations to
cover a total of 30 cities in the U.S. by the end of 2016, making it the
largest IoT network coverage provider in the country thus far.

Another example of a large deployment is the one completed by New York
Waterways in New York City to

connect all the city's vessels and be able to monitor them live 24/7.
The network was designed and engineered by Fluidmesh Networks, a
Chicago-based company developing wireless networks for critical
applications. The NYWW network is currently providing coverage on the
Hudson River, East River, and Upper New York Bay. With the wireless
network in place, NY Waterway is able to take control of its fleet and
passengers in a way that was not previously possible. New applications
can include security, energy and fleet management, digital signage,
public Wi-Fi, paperless ticketing and others.

\emph{Energy management}

Significant numbers of energy-consuming devices (e.g. switches, power
outlets, bulbs, televisions, etc.)

already integrate Internet connectivity, which can allow them to
communicate with utilities to balance

power generation and energy usage and optimize energy consumption as a
whole. These devices allow

for remote control by users, or central management via a cloud-based
interface, and enable functions like

scheduling (e.g., remotely powering on or off heating systems,
controlling ovens, changing lighting conditions~etc.). The smart grid is
a utility-side IoT application; systems gather and act on energy and
power-related information to improve the efficiency of the production
and distribution of electricity. Using advanced metering infrastructure
(AMI) Internet-connected devices, electric utilities not only collect
data from endusers, but also manage distribution automation devices like
transformers.

\emph{Environmental monitoring}

Environmental monitoring applications of the IoT typically use sensors
to assist in environmental protection by monitoring air or water
quality, atmospheric or soil conditions, and can even include areas like
monitoring the movements of wildlife and their habitats. Development of
resource-constrained devices connected to the Internet also means that
other applications like earthquake or tsunami early-warning systems can
also be used by emergency services to provide more effective aid. IoT
devices in this application typically span a large geographic area and
can also be mobile. It has been argued that the standardization IoT
brings to wireless sensing will revolutionize this area.

\subsection*{Trends and
characteristics}

{\label{140735}}

The IoT's major significant trend in recent years is the explosive
growth of devices connected and controlled by the Internet. The wide
range of applications for IoT technology mean that the specifics can be
very different from one device to the next but there are basic
characteristics shared by most. IoT creates opportunities for more
direct integration of the physical world into computer-based systems,
resulting in efficiency improvements, economic benefits, and reduced
human exertions.

The number of IoT devices increased 31\% year-over-year to 8.4 billion
in the year 2017 and it is estimated

that there will be 30 billion devices by 2020. The global market value
of IoT is projected to reach \$7.1

trillion by 2020 (Fig. 2).

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2/Figure2}
\caption{{Figure 2: Technology roadmap: Internet of things.
{\label{274042}}%
}}
\end{center}
\end{figure}

\par\null

\emph{Intelligence}

Ambient intelligence and autonomous control are not part of the original
concept of the Internet of things. Ambient intelligence and autonomous
control do not necessarily require Internet structures, either. However,
there is a shift in research (by companies such as Intel) to integrate
the concepts of IoT and autonomous control, with initial outcomes
towards this direction considering objects as the driving force for
autonomous IoT.

In the future, the Internet of Things may be a non-deterministic and
open network in which auto-organized or intelligent entities (web
services, SOA components) and virtual objects (avatars) will be
interoperable and able to act independently (pursuing their own
objectives or shared ones) depending on the context, circumstances or
environments. Autonomous behavior through the collection and reasoning
of context information as well as the object's ability to detect changes
in the environment (faults affecting sensors) and introduce suitable
mitigation measures constitutes a major research trend, clearly needed
to provide credibility to the IoT technology. Modern IoT products and
solutions in the marketplace use a variety of different technologies to
support such context-aware automation, but more sophisticated forms of
intelligence are requested to permit sensor units and intelligent
cyber-physical systems to be deployed in real environments.

\emph{Architecture}

Tier 1 of the IIoT architecture consists of networked things, typically
sensors and actuators, from the IIoT

equipment, which use protocols such as Modbus, Zigbee, or proprietary
protocols, to connect to an Edge

Gateway. Tier 2 includes sensor data aggregation systems called Edge
Gateways that provide functionality, such as pre-processing of the data,
securing connectivity to cloud, using systems such as WebSockets, the
event hub, and, even in some cases, edge analytics or fog computing.
Tier 3 includes the cloud application built for IIoT using the
microservices architecture, which are usually polyglot and inherently
secure in nature using HTTPS/OAuth. Tier 3 also includes storage of
sensor data using various database systems, such as time series
databases or asset stores using backend data storage systems such as
Cassandra or Postgres. In addition to the data storage, we analyze the
data using various analytics, predictive or threshold-based or
regression-based, to get more insights on the IIoT equipment.

Building on the Internet of things, the web of things is an architecture
for the application layer of the Internet of things looking at the
convergence of data from IoT devices into Web applications to create
innovative~use-cases. In order to program and control the flow of
information in the Internet of things, a predicted architectural
direction is being called BPM Everywhere which is a blending of
traditional process management with process mining and special
capabilities to automate the control of large numbers of coordinated
devices.

The Internet of things requires huge scalability in the network space to
handle the surge of devices. IETF

6LoWPAN would be used to connect devices to IP networks. With billions
of devices being added to the

Internet space, IPv6 will play a major role in handling the network
layer scalability. IETF's Constrained

Application Protocol, ZeroMQ, and MQTT would provide lightweight data
transport.

\emph{Complexity}

In semi-open or closed loops (i.e. value chains, whenever a global
finality can be settled) IoT will often be

considered and studied as a complex system due to the huge number of
different links, interactions between autonomous actors, and its
capacity to integrate new actors. At the overall stage (full open loop)
it will likely be seen as a chaotic environment (since systems always
have finality). As a practical approach, not all elements in the
Internet of things run in a global, public space. Subsystems are often
implemented to mitigate the risks of privacy, control and reliability.
For example, domestic robotics (domotics) running inside a smart home
might only share data within and be available via a local network.
Managing and controlling high dynamic ad hoc IoT things/devices network
is a tough task with the traditional networks architecture, Software
Defined Networking (SDN) provides the agile dynamic solution that can
cope with the special requirements of the diversity of innovative IoT
applications.

\emph{Size considerations}

The Internet of things would encode 50 to 100 trillion objects, and be
able to follow the movement of those objects. Human beings in surveyed
urban environments are each surrounded by 1000 to 5000 trackable
objects. In 2015 there were already 83 million smart devices in people`s
homes. This number is about to grow up to 193 million devices in 2020
and will for sure go on growing in the near future.

The figure of online capable devices grew 31\% from 2016 to 8.4 billion
in 2017.

\emph{Space considerations}

In the Internet of things, the precise geographic location of a thingand
also the precise geographic dimensions of a thingwill be critical.
Therefore, facts about a thing, such as its location in time and space,
have been less critical to track because the person processing the
information can decide whether or not that information was important to
the action being taken, and if so, add the missing information (or
decide to not take the action). (Note that some things in the Internet
of things will be sensors, and sensor location is usually important.)
The GeoWeb and Digital Earth are promising applications that become
possible when things can become organized and connected by location.
However, the challenges that remain include the constraints of variable
spatial scales, the need to handle massive amounts of data, and an
indexing for fast search and neighbor operations. In the Internet of
things, if things are able to take actions on their own initiative, this
human-centric mediation role is eliminated. Thus, the time-space context
that we as humans take for granted must be given a central role in this
information ecosystem. Just as standards play a key role in the Internet
and the Web, geospatial standards will play a key role in the Internet
of things.

\emph{A solution to ``basket of remotes''}

Many IoT devices have a potential to take a piece of this market.
Jean-Louis Gass\selectlanguage{ngerman}´ee (Apple initial alumni

team, and BeOS co-founder) has addressed this topic in an article on
Monday Note, where he predicts that the most likely problem will be what
he calls the ``basket of remotes'' problem, where we'll have hundreds of
applications to interface with hundreds of devices that don't share
protocols for speaking with one another. For improved user interaction,
some technology leaders are joining forces to create standards for
communication between devices to solve this problem. Others are turning
to the concept of predictive interaction of devices, ``where collected
data is used to predict and trigger actions on the specific devices''
while making them work together.

\subsection*{Frameworks}

{\label{751817}}

IoT frameworks might help support the interaction between ``things'' and
allow for more complex structures like distributed computing and the
development of distributed applications. Currently, some IoT frameworks
seem to focus on real-time data logging solutions, offering some basis
to work with many ``things'' and have them interact. Future developments
might lead to specific software-development environments to create the
software to work with the hardware used in the Internet of things.
Companies are developing technology platforms to provide this type of
functionality for the Internet of things. Newer platforms are being
developed, which add more intelligence.

REST is a scalable architecture that allows things to communicate over
Hypertext Transfer Protocol and is

easily adopted for IoT applications to provide communication from a
thing to a central web server.

\section*{3D printing}

{\label{189441}}

\subsection*{Outline}

{\label{698111}}

3D printing is any of various processes in which material is joined or
solidified under computer control to

create a three-dimensional object, with material being added together
(such as liquid molecules or powder grains being fused together). 3D
printing is used in both rapid prototyping and additive manufacturing.
Objects can be of almost any shape or geometry and typically are
produced using digital model data from a 3D model or another electronic
data source such as an Additive Manufacturing File (AMF) file (usually
in sequential layers). There are many different technologies, like
stereolithography (SLA) or fused deposit modeling (FDM). Thus, unlike
material removed from a stock in the conventional machining process, 3D
printing or Additive Manufacturing builds a three-dimensional object
from a computer-aided design (CAD) model or AMF file, usually by
successively adding material layer by layer.

The term ``3D printing'' originally referred to a process that deposits
a binder material onto a powder bed with inkjet printer heads layer by
layer. More recently, the term is being used in popular vernacular to
encompass a wider variety of additive manufacturing techniques. United
States and global technical standards use the official term additive
manufacturing for this broader sense (Fig. 3).

\subsection*{Terminology}

{\label{677136}}

The umbrella term additive manufacturing (AM) gained wide currency in
the 2000s, inspired by the theme

of material being added together (in any of various ways). In contrast,
the term subtractive manufacturing

appeared as a retronym for the large family of machining processes with
material removal as their common theme. The term 3D printing still
referred only to the polymer technologies in most minds, and the
term~~AM was likelier to be used in metalworking and end use part
production contexts than among polymer, inkjet, or stereolithography
enthusiasts.

By the early 2010s, the terms 3D printing and additive manufacturing
evolved senses in which they were

alternate umbrella terms for additive technologies, one being used in
popular vernacular by consumer-maker~~communities and the media, and the
other used more formally by industrial end-use part producers, machine
manufacturers, and global technical standards organizations. Until
recently, the term 3D printing has been associated with machines low-end
in price or in capability. Both terms reflect that the technologies
share the theme of material addition or joining throughout a 3D work
envelope under automated control. Peter Zelinski, the editor-in-chief of
Additive Manufacturing magazine, pointed out in 2017 that the terms are
still often synonymous in casual usage but that some manufacturing
industry experts are increasingly making a sense distinction whereby
Additive Manufacturing comprises 3D printing plus other technologies or
other aspects of a manufacturing process.

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3/Figure3}
\caption{{Figure 3: A makerBot three-dimensional printer.
{\label{658985}}%
}}
\end{center}
\end{figure}

\par\null

Other terms that have been used as synonyms or hypernyms have included
desktop manufacturing, rapid manufacturing (as the logical
production-level successor to rapid prototyping), and on-demand
manufacturing (which echoes on-demand printing in the 2D sense of
printing). That such application of the adjectives rapid and on-demand
to the noun manufacturing was novel in the 2000s reveals the prevailing
mental model of the long industrial era in which almost all production
manufacturing involved long lead times for laborious tooling
development. Today, the term subtractive has not replaced the term
machining, instead complementing it when a term that covers any removal
method is needed. Agile tooling is the use of modular means to design 10
tooling that is produced by additive manufacturing or 3D printing
methods to enable quick prototyping and responses to tooling and fixture
needs. Agile tooling uses a cost effective and high quality method to
quickly respond to customer and market needs, and it can be used in
hydro-forming, stamping, injection molding and other manufacturing
processes.

\subsection*{History}

{\label{121617}}

\emph{1981}

Early additive manufacturing equipment and materials were developed in
the 1980s. In 1981, Hideo Kodama of Nagoya Municipal Industrial Research
Institute invented two additive methods for fabricating

three-dimensional plastic models with photo-hardening thermoset polymer,
where the UV exposure area is controlled by a mask pattern or a scanning
fiber transmitter.

\emph{1984}

On 16 July 1984, Alain Le M\selectlanguage{ngerman}´ehaut´e, Olivier de Witte, and Jean Claude
Andr´e filed their patent for the

stereolithography process. The application of the French inventors was
abandoned by the French General

Electric Company (now Alcatel-Alsthom) and CILAS (The Laser Consortium).
The claimed reason was ``for

lack of business perspective''.

Three weeks later in 1984, Chuck Hull of 3D Systems Corporation filed
his own patent for a stereolithography fabrication system, in which
layers are added by curing photopolymers with ultraviolet light lasers.
Hull defined the process as a ``system for generating three-dimensional
objects by creating a cross-sectional pattern of the object to be
formed,''. Hull's contribution was the STL (Stereolithography) file
format and the digital slicing and infill strategies common to many
processes today.

\emph{1988}

The technology used by most 3D printers to dateespecially hobbyist and
consumer-oriented modelsis

fused deposition modeling, a special application of plastic extrusion,
developed in 1988 by S. Scott Crump and commercialized by his company
Stratasys, which marketed its first FDM machine in 1992.

AM processes for metal sintering or melting (such as selective laser
sintering, direct metal laser sintering,

and selective laser melting) usually went by their own individual names
in the 1980s and 1990s. At the time, all metalworking was done by
processes that we now call non-additive (casting, fabrication, stamping,
and machining); although plenty of automation was applied to those
technologies (such as by robot welding and CNC), the idea of a tool or
head moving through a 3D work envelope transforming a mass of raw
material into a desired shape with a toolpath was associated in
metalworking only with processes that removed metal (rather than adding
it), such as CNC milling, CNC EDM, and many others. But the automated
techniques that added metal, which would later be called additive
manufacturing, were beginning to challenge that assumption. By the
mid-1990s, new techniques for material deposition were developed at
Stanford and Carnegie Mellon University, including microcasting and
sprayed materials. Sacrificial and support materials had also become
more common, enabling new object geometries.

\emph{1993}

The term 3D printing originally referred to a powder bed process
employing standard and custom inkjet print heads, developed at MIT in
1993 and commercialized by Soligen Technologies, Extrude Hone
Corporation, and Z Corporation.

The year 1993 also saw the start of a company called Solidscape,
introducing a high-precision polymer jet

fabrication system with soluble support structures, (categorized as a
``dot-on-dot'' technique).

\emph{1995}

In 1995 the Fraunhofer Institute developed the selective laser melting
process.

\emph{2009}

Fused Deposition Modeling (FDM) printing process patents expired in
2009. As the various additive processes matured, it became clear that
soon metal removal would no longer be the only metalworking process done
through a tool or head moving through a 3D work envelope transforming a
mass of raw material into a desired shape layer by layer. The 2010s were
the first decade in which metal end use parts such as engine brackets
and large nuts would be grown (either before or instead of machining) in
job production rather than obligately being machined from bar stock or
plate. It is still the case that casting, fabrication, stamping, and
machining are more prevalent than additive manufacturing in
metalworking, but AM is now beginning to make significant inroads, and
with the advantages of design for additive manufacturing, it is clear to
engineers that much more is to come.

As technology matured, several authors had begun to speculate that 3D
printing could aid in sustainable

development in the developing world.

\emph{2013}

NASA employees Samantha Snabes and Matthew Fiedler create first
prototype of large-format, affordable

3D printer, Gigabot, and launch 3D printing company re:3D.

\emph{2018}

re:3D develops a system that uses plastic pellets that can be made by
grinding up waste plastic.

\subsection*{General principles}

{\label{954290}}

\emph{Modeling}

3D printable models may be created with a computer-aided design (CAD)
package, via a 3D scanner, or

by a plain digital camera and photogrammetry software. 3D printed models
created with CAD result in

reduced errors and can be corrected before printing, allowing
verification in the design of the object before it is printed. The
manual modeling process of preparing geometric data for 3D computer
graphics is similar to plastic arts such as sculpting. 3D scanning is a
process of collecting digital data on the shape and appearance of a real
object, creating a digital model based on it.

\emph{Printing}

Before printing a 3D model from an STL file, it must first be examined
for errors. Most CAD applications

(Fig. 4) produce errors in output STL files of the following types:

\begin{itemize}
\tightlist
\item
  holes,
\item
  faces normal,
\item
  self-intersections,
\item
  noise shells,
\item
  manifold errors.
\end{itemize}

A step in the STL generation known as ``repair'' fixes such problems in
the original model. Generally STLs

that have been produced from a model obtained through 3D scanning often
have more of these errors. This is due to how 3D scanning works-as it is
often by point to point acquisition, reconstruction will include errors
in most cases.

Once completed, the STL file needs to be processed by a piece of
software called a ``slicer,'' which converts the model into a series of
thin layers and produces a G-code file containing instructions tailored
to a specific type of 3D printer (FDM printers). This G-code file can
then be printed with 3D printing client software (which loads the
G-code, and uses it to instruct the 3D printer during the 3D printing
process).

Printer resolution describes layer thickness and X--Y resolution in dots
per inch (dpi) or micrometers (mm). Typical layer thickness is around
100 mm (250 DPI), although some machines can print layers as thin as 16

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure4/Figure4}
\caption{{Figure 4: CAD model used for 3D printing.
{\label{101481}}%
}}
\end{center}
\end{figure}

mm (1,600 DPI). X\{Y resolution is comparable to that of laser printers.
The particles (3D dots) are around

50 to 100 mm (510 to 250 DPI) in diameter. For that printer resolution,
specifying a mesh resolution of

0.01\{0.03 mm and a chord length ? 0.016 mm generate an optimal STL
output le for a given model input

le. Specifying higher resolution results in larger les without increase
in print quality.

Construction of a model with contemporary methods can take anywhere from
several hours to several days, depending on the method used and the size
and complexity of the model. Additive systems can typically reduce this
time to a few hours, although it varies widely depending on the type of
machine used and the size and number of models being produced
simultaneously.

Traditional techniques like injection moulding can be less expensive for
manufacturing polymer products in high quantities, but additive
manufacturing can be faster, more exible and less expensive when
producing relatively small quantities of parts. 3D printers give
designers and concept development teams the ability to produce parts and
concept models using a desktop size printer.

Seemingly paradoxic, more complex objects can be cheaper for 3D printing
production than less complex

objects.

\emph{Finishing}

Though the printer-produced resolution is sucient for many applications,
printing a slightly oversized

version of the desired object in standard resolution and then removing
material with a higher-resolution

subtractive process can achieve greater precision.

The layered structure of all Additive Manufacturing processes leads
inevitably to a strain-stepping eect on part surfaces which are curved
or tilted in respect to the building platform. The eects strongly depend
on the orientation of a part surface inside the building process.

Some printable polymers such as ABS, allow the surface nish to be
smoothed and improved using chemical vapor processes based on acetone or
similar solvents.

Some additive manufacturing techniques are capable of using multiple
materials in the course of constructing parts. These techniques are able
to print in multiple colors and color combinations simultaneously, and
would~not necessarily require painting.

Some printing techniques require internal supports to be built for
overhanging features during construction. These supports must be
mechanically removed or dissolved upon completion of the print.

All of the commercialized metal 3D printers involve cutting the metal
component o the metal substrate

after deposition. A new process for the GMAW 3D printing allows for
substrate surface modications to

remove aluminum or steel.

\subsection*{Processes and printers}

{\label{151501}}

A large number of additive processes are available. The main dierences
between processes are in the way layers are deposited to create parts
and in the materials that are used. Each method has its own advantages
and drawbacks, which is why some companies oer a choice of powder and
polymer for the material used to build the object. Others sometimes use
standard, o-the-shelf business paper as the build material to produce a
durable prototype. The main considerations in choosing a machine are
generally speed, costs of the 3D printer, of the printed prototype,
choice and cost of the materials, and color capabilities. Printers that
work directly with metals are generally expensive. However less
expensive printers can be used to make a mold, which is then used to
make metal parts.

ISO/ASTM52900-15 denes seven categories of Additive Manufacturing (AM)
processes within its meaning:

binder jetting, directed energy deposition, material extrusion, material
jetting, powder bed fusion, sheet

lamination, and vat photopolymerization.

Some methods melt or soften the material to produce the layers. In Fused
lament fabrication, also known

as Fused deposition modeling (FDM), the model or part is produced by
extruding small beads or streams of material which harden immediately to
form layers. A lament of thermoplastic, metal wire, or other material is
fed into an extrusion nozzle head (3D printer extruder), which heats the
material and turns the ow on and o. FDM is somewhat restricted in the
variation of shapes that may be fabricated. Another technique fuses
parts of the layer and then moves upward in the working area, adding
another layer of granules and repeating the process until the piece has
built up. This process uses the unfused media to support overhangs and
thin walls in the part being produced, which reduces the need for
temporary auxiliary supports for the piece.

Laser sintering techniques include selective laser sintering, with both
metals and polymers, and direct metal laser sintering. Selective laser
melting does not use sintering for the fusion of powder granules but
will completely melt the powder using a high-energy laser to create
fully dense materials in a layer-wise method that has mechanical
properties similar to those of conventional manufactured metals.
Electron beam melting is a similar type of additive manufacturing
technology for metal parts (e.g. titanium alloys). EBM manufactures
parts by melting metal powder layer by layer with an electron beam in a
high vacuum. Another method consists of an inkjet 3D printing system,
which creates the model one layer at a time by spreading a layer of
powder (plaster, or resins) and printing a binder in the cross-section
of the part using an inkjet-like process. With laminated object
manufacturing, thin layers are cut to shape and joined together.

Schematic representation of Stereolithography; a light-emitting device
a) (laser or DLP) selectively illuminate the transparent bottom c) of a
tank b) lled with a liquid photo-polymerizing resin; the solidied resin
d) is progressively dragged up by a lifting platform e)

Other methods cure liquid materials using dierent sophisticated
technologies, such as stereolithography. Photopolymerization is
primarily used in stereolithography to produce a solid part from a
liquid. Inkjet printer systems like the Objet PolyJet system spray
photopolymer materials onto a build tray in ultra-thin layers (between
16 and 30 mm) until the part is completed. Each photopolymer layer is
cured with UV light after it is jetted, producing fully cured models
that can be handled and used immediately, without post-curing.
Ultra-small features can be made with the 3D micro-fabrication technique
used in multiphoton photopolymerisation. Due to the nonlinear nature of
photo excitation, the gel is cured to a solid only in~the places where
the laser was focused while the remaining gel is then washed away.
Feature sizes of under 100 nm are easily produced, as well as complex
structures with moving and interlocked parts. Yet another approach uses
a synthetic resin that is solidied using LEDs.

In Mask-image-projection-based stereolithography, a 3D digital model is
sliced by a set of horizontal planes. Each slice is converted into a
two-dimensional mask image. The mask image is then projected onto a
photocurable liquid resin surface and light is projected onto the resin
to cure it in the shape of the layer. Continuous liquid interface
production begins with a pool of liquid photopolymer resin. Part of the
pool bottom is transparent to ultraviolet light (the
\textbackslash{}window"), which causes the resin to solidify. The object
rises slowly enough to allow resin to ow under and maintain contact with
the bottom of the object. In powder-fed directed-energy deposition, a
high-power laser is used to melt metal powder supplied to the focus of
the laser beam. The powder fed directed energy process is similar to
Selective Laser Sintering, but the metal powder is applied only where
material is being added to the part at that moment.

As of December 2017, additive manufacturing systems were on the market
that ranged from \$99 to \$500,000 in price and were employed in
industries including aerospace, architecture, automotive, defense, and
medical replacements, among many others. For example, General Electric
uses the high-end model to build parts for turbines. Many of these
systems are used for rapid prototyping, before mass production methods
are employed. Higher education has proven to be a major buyer of desktop
and professional 3D printers which industry experts generally view as a
positive indicator. Libraries around the world have also become
locations to house smaller 3D printers for educational and community
access. Several projects and companies are making eorts to develop
aordable 3D printers for home desktop use. Much of this work has been
driven by and targeted at DIY/Maker/enthusiast/early adopter
communities, with additional ties to the academic and hacker
communities.

\subsection*{Applications}

{\label{798590}}

In the current scenario, 3D printing or Additive Manufacturing has been
used in manufacturing, medical, industry and sociocultural sectors which
facilitate 3D printing or Additive Manufacturing to become successful
commercial technology (Fig.5 ). The earliest application of additive
manufacturing was on the toolroom end of the manufacturing spectrum. For
example, rapid prototyping was one of the earliest additive variants,
and its mission was to reduce the lead time and cost of developing
prototypes of new parts and devices, which was earlier only done with
subtractive toolroom methods such as CNC milling, turning, and precision
grinding. In the 2010s, additive manufacturing entered production to a
much greater extent.

Additive manufacturing of food is being developed by squeezing out food,
layer by layer, into threedimensional objects. A large variety of foods
are appropriate candidates, such as chocolate and candy, and at foods
such as crackers, pasta, and pizza.

3D printing has entered the world of clothing, with fashion designers
experimenting with 3D-printed bikinis, shoes, and dresses. In commercial
production Nike is using 3D printing to prototype and manufacture the
2012 Vapor Laser Talon football shoe for players of American football,
and New Balance is 3D manufacturing custom-t shoes for athletes. 3D
printing has come to the point where companies are printing consumer
grade eyewear with on-demand custom t and styling (although they cannot
print the lenses). On-demand customization of glasses is possible with
rapid prototyping.

Vanessa Friedman, fashion director and chief fashion critic at The New
York Times, says 3D printing will

have a signicant value for fashion companies down the road, especially
if it transforms into a print-ityourself tool for shoppers.
\textbackslash{}There's real sense that this is not going to happen
anytime soon," she says, \textbackslash{}but it will happen, and it will
create dramatic change in how we think both about intellectual property
and how things are in the supply chain." She adds:
\textbackslash{}Certainly some of the fabrications that brands can use
will be dramatically changed by technology."

In cars, trucks, and aircraft (Fig. 6), Additive Manufacturing is
beginning to transform both (1) unibody

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure5/Figure5}
\caption{{Figure 5: A 3D sele in 1:20 scale printed by Shapeways using
gypsum-based printing.
{\label{792058}}%
}}
\end{center}
\end{figure}

and fuselage design and production and (2) powertrain design and
production. For example:

\begin{itemize}
\tightlist
\item
  in early 2014, Swedish supercar manufacturer Koenigsegg announced the
  One:1, a supercar that utilizes many components that were 3D printed.
  Urbee is the name of the rst car in the world car mounted using the
  technology 3D printing (its bodywork and car windows were
  \textbackslash{}printed"),
\item
  in 2014, Local Motors debuted Strati, a functioning vehicle that was
  entirely 3D Printed using ABS plastic and carbon ber, except the
  powertrain. In May 2015 Airbus announced that its new AirbusA350 XWB
  included over 1000 components manufactured by 3D printing,~
\item
  in 2015, a Royal Air Force Euroghter Typhoon ghter jet ew with printed
  parts. The United States Air Force has begun to work with 3D printers,
  and the Israeli Air Force has also purchased a 3D printer to print
  spare parts,~
\item
  in 2017, GE Aviation revealed that it had used design for additive
  manufacturing to create a helicopter~ ~engine with 16 parts instead of
  900, with great potential impact on reducing the complexity of supply
  chains.
\end{itemize}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure6/Figure6}
\caption{{Figure 6: A Jet Engine turbine printed from the Howard Community College
Makerbot.
{\label{631782}}%
}}
\end{center}
\end{figure}

AM's impact on rearms involves two dimensions: new manufacturing methods
for established companies,~and new possibilities for the making of
do-it-yourself rearms. In 2012, the US-based group Defense Distributed
disclosed plans to design a working plastic 3D printed rearm
\textbackslash{}that could be downloaded and reproduced by anybody with
a 3D printer." After Defense Distributed released their plans, questions
were raised regarding the eects that 3D printing and widespread
consumer-level CNC machining may have on gun control eectiveness.

Surgical uses of 3D printing-centric therapies have a history beginning
in the mid-1990s with anatomical

modeling for bony reconstructive surgery planning. Patient-matched
implants were a natural extension of this work, leading to truly
personalized implants that t one unique individual. Virtual planning of
surgery and guidance using 3D printed, personalized instruments have
been applied to many areas of surgery including total joint replacement
and craniomaxillofacial reconstruction with great success. One example
of this is the bioresorbable trachial splint to treat newborns with
tracheobronchomalacia developed at the University of Michigan. The use
of additive manufacturing for serialized production of orthopedic
implants (metals) is also increasing due to the ability to eciently
create porous surface structures that facilitate osseointegration. The
hearing aid and dental industries are expected to be the biggest area of
future development using the custom 3D printing technology.

In March 2014, surgeons in Swansea used 3D printed parts to rebuild the
face of a motorcyclist who had

been seriously injured in a road accident. In May 2018, 3D printing has
been used for the kidney transplant to save a three-year-old boy. As of
2012, 3D bio-printing technology has been studied by biotechnology rms
and academia for possible use in tissue engineering applications in
which organs and body parts are built using inkjet printing techniques.
In this process, layers of living cells are deposited onto a gel medium
or sugar matrix and slowly built up to form three-dimensional structures
including vascular systems. Recently, a heart-on-chip has been created
which matches properties of cells.

In 2018, 3D printing technology was used for the rst time to create a
matrix for cell immobilization in

fermentation. Propionic acid production by Propionibacterium
acidipropionici immobilized on 3D-printed nylon beads was chosen as a
model study. It was shown that those 3D-printed beads were capable to
promote high density cell attachment and propionic acid production,
which could be adapted to other fermentation bioprocesses.

In 2005, academic journals had begun to report on the possible artistic
applications of 3D printing technology. As of 2017, domestic 3D printing
was reaching a consumer audience beyond hobbyists and enthusiasts. O the
shelf machines were increasingly capable of producing practical
household applications, for example, ornamental objects. Some practical
examples include a working clock and gears printed for home woodworking
machines among other purposes. Web sites associated with home 3D
printing tended to include backscratchers, coat hooks, door knobs, etc.

3D printing, and open source 3D printers in particular, are the latest
technology making inroads into the

classroom. Some authors have claimed that 3D printers oer an
unprecedented \textbackslash{}revolution" in STEM

education. The evidence for such claims comes from both the low cost
ability for rapid prototyping in

the classroom by students, but also the fabrication of low-cost
high-quality scientic equipment from open hardware designs forming
open-source labs. Future applications for 3D printing might include
creating open-source scientic equipment.

In the last several years 3D printing has been intensively used by in
the cultural heritage eld for preservation, restoration and
dissemination purposes. Many Europeans and North American Museums have
purchased 3D printers and actively recreate missing pieces of their
relics. The Metropolitan Museum of Art and the British Museum have
started using their 3D printers to create museum souvenirs that are
available in the museum shops. Other museums, like the National Museum
of Military History and Varna Historical Museum, have gone further and
sell through the online platform Threeding digital models of their
artifacts, created using Artec 3D scanners, in 3D printing friendly le
format, which everyone can 3D print at home.

3D printed soft actuators is a growing application of 3D printing
technology which has found its place in the 3D printing applications.
These soft actuators are being developed to deal with soft structures
and organs especially in biomedical sectors and where the interaction
between human and robot is inevitable. The majority of the existing soft
actuators are fabricated by conventional methods that require manual
fabrication of devices, post processing/assembly, and lengthy iterations
until maturity in the fabrication is achieved. To avoid the tedious and
time-consuming aspects of the current fabrication processes, researchers
are exploring an appropriate manufacturing approach for eective
fabrication of soft actuators. Thus, 3D printed soft actuators are
introduced to revolutionize the design and fabrication of soft actuators
with custom geometrical, functional, and control properties in a faster
and inexpensive approach. They also enable incorporation of all actuator
components into a single structure eliminating the need to use external
joints, adhesives, and fasteners.

\subsection*{Legal aspects}

{\label{341151}}

\subsubsection*{Intellectual property}

{\label{628166}}

3D printing has existed for decades within certain manufacturing
industries where many legal regimes,

including patents, industrial design rights, copyright, and trademark
may apply. However, there is not much jurisprudence to say how these
laws will apply if 3D printers become mainstream and individuals or
hobbyist communities begin manufacturing items for personal use, for
non-prot distribution, or for sale.

Any of the mentioned legal regimes may prohibit the distribution of the
designs used in 3D printing, or

the distribution or sale of the printed item. To be allowed to do these
things, where an active intellectual

property was involved, a person would have to contact the owner and ask
for a license, which may come with conditions and a price. However, many
patent, design and copyright laws contain a standard limitation or
exception for `private', `non-commercial' use of inventions, designs or
works of art protected under intellectual property (IP). That standard
limitation or exception may leave such private, non-commercial uses
outside the scope of IP rights.

Patents cover inventions including processes, machines, manufactures,
and compositions of matter and have a nite duration which varies between
countries, but generally 20 years from the date of application.
Therefore, if a type of wheel is patented, printing, using, or selling
such a wheel could be an infringement of the patent.~Copyright covers an
expression in a tangible, xed medium and often lasts for the life of the
author plus 70 years thereafter. If someone makes a statue, they may
have copyright on the look of that statue, so if someone sees that
statue, they cannot then distribute designs to print an identical or
similar statue.

When a feature has both artistic (copyrightable) and functional
(patentable) merits, when the question has appeared in US court, the
courts have often held the feature is not copyrightable unless it can be
separated from the functional aspects of the item. In other countries
the law and the courts may apply a dierent approach allowing, for
example, the design of a useful device to be registered (as a whole) as
an industrial design on the understanding that, in case of unauthorized
copying, only the non-functional features may be claimed under design
law whereas any technical features could only be claimed if covered by a
valid patent.

\subsubsection*{Gun legislation and
administration}

{\label{435613}}

The US Department of Homeland Security and the Joint Regional
Intelligence Center released a memo

stating that \textbackslash{}signicant advances in three-dimensional
(3D) printing capabilities, availability of free digital

3D printable les for rearms components, and diculty regulating le
sharing may present public safety risks from unqualied gun seekers who
obtain or manufacture 3D printed guns" and that \textbackslash{}proposed
legislation to ban 3D printing of weapons may deter, but cannot
completely prevent, their production. Even if the practice is prohibited
by new legislation, online distribution of these 3D printable les will
be as dicult to control as any other illegally traded music, movie or
software les."

Attempting to restrict the distribution of gun plans via the Internet
has been likened to the futility of

preventing the widespread distribution of DeCSS, which enabled DVD
ripping. After the US government

had Defense Distributed take down the plans, they were still widely
available via the Pirate Bay and other

le sharing sites. Downloads of the plans from the UK, Germany, Spain,
and Brazil were heavy. Some

US legislators have proposed regulations on 3D printers to prevent them
from being used for printing guns. 3D printing advocates have suggested
that such regulations would be futile, could cripple the 3D printing
industry, and could infringe on free speech rights, with early pioneer
of 3D printing Professor Hod Lipson suggesting that gunpowder could be
controlled instead.

Internationally, where gun controls are generally stricter than in the
United States, some commentators have said the impact may be more
strongly felt since alternative rearms are not as easily obtainable.
Ocials in the United Kingdom have noted that producing a 3D printed gun
would be illegal under their gun control laws. Europol stated that
criminals have access to other sources of weapons but noted that as
technology improves, the risks of an eect would increase.

\subsubsection*{Aerospace regulation}

{\label{917634}}

In the United States, the FAA has anticipated a desire to use additive
manufacturing techniques and has

been considering how best to regulate this process. The FAA has
jurisdiction over such fabrication because all aircraft parts must be
made under FAA production approval or under other FAA regulatory
categories. In December 2016, the FAA approved the production of a 3D
printed fuel nozzle for the GE LEAP engine. Aviation attorney Jason
Dickstein has suggested that additive manufacturing is merely a
production method, and should be regulated like any other production
method. He has suggested that the FAA's focus should be on guidance to
explain compliance, rather than on changing the existing rules, and that
existing regulations and guidance permit a company \textbackslash{}to
develop a robust quality system that adequately re ects regulatory needs
for quality assurance."

\subsection*{Health and safety}

{\label{549670}}

Research on the health and safety concerns of 3D printing is new and in
development due to the recent

proliferation of 3D printing devices. In 2017 the European Agency for
Safety and Health at Work has

published a discussion paper on the processes and materials involved in
3D printing, potential implications of this technology for occupational
safety and health and avenues for controlling potential hazards. Most
concerns involve gas and material exposures, in particular
nanomaterials, material handling, static electricity, moving parts and
pressures.

A National Institute for Occupational Safety and Health (NIOSH) study
noted particle emissions from a

fused lament peaked a few minutes after printing started and returned to
baseline levels 100 minutes after printing ended. Emissions from fused
lament printers can include a large number of ultrane particles and
volatile organic compounds (VOCs).

The toxicity from emissions varies by source material due to dierences
in size, chemical properties, and

quantity of emitted particles. Excessive exposure to VOCs can lead to
irritation of the eyes, nose, and

throat, headache, loss of coordination, and nausea and some of the
chemical emissions of fused lament

printers have also been linked to asthma. Based on animal studies,
carbon nanotubes and carbon nanobers sometimes used in fused lament
printing can cause pulmonary eects including in ammation, granulomas,
and pulmonary brosis when at the nanoparticle size.

As of March 2018, the US Government has set 3D printer emission
standards for only a limited number

of compounds. Furthermore, the few established standards address factory
conditions, not home or other nvironments in which the printers are
likely to be used.

Carbon nanoparticle emissions and processes using powder metals are
highly combustible and raise the risk of dust explosions. At least one
case of severe injury was noted from an explosion involved in metal
powders used for fused lament printing. Other general health and safety
concerns include the hot surface of UV lamps and print head blocks, high
voltage, ultraviolet radiation from UV lamps, and potential for
mechanical injury from moving parts.

The problems noted in the NIOSH report were reduced by using
manufacturer-supplied covers and full

enclosures, using proper ventilation, keeping workers away from the
printer, using respirators, turning o the printer if it jammed, and
using lower emission printers and laments. At least one case of severe

injury was noted from an explosion involved in metal powders used for
fused lament. Personal protective

equipment has been found to be the least desirable control method with a
recommendation that it only be used to add further protection in
combination with approved emissions protection.

Hazards to health and safety also exist from post-processing activities
done to nish parts after they have been printed. These post-processing
activities can include chemical baths, sanding, polishing, or vapor
exposure to rene surface nish, as well as general subtractive
manufacturing techniques such as drilling, milling, or turning to modify
the printed geometry. Any technique that removes material from the
printed part has the potential to generate particles that can be inhaled
or cause eye injury if proper personal protective equipment is not used,
such as respirators or safety glasses. Caustic baths are often used to
dissolve support material used by some 3D printers that allows them to
print more complex shapes. These baths require personal protective
equipment to prevent injury to exposed skin.

Although no occupational exposure limits specic to 3D printer emissions
exist, certain source materials used in 3D printing, such as carbon
nanober and carbon nanotubes, have established occupational exposure
limits at the nanoparticle size.

Since 3-D imaging creates items by fusing materials together, there runs
the risk of layer separation in some devices made using 3-D Imaging. For
example, in January 2013, the US medical device company, DePuy, recalled
their knee and hip replacement systems. The devices were made from
layers of metal, and shavings had come loose \{ potentially harming the
patient.

\subsection*{Impact}

{\label{412109}}

Additive manufacturing, starting with today's infancy period, requires
manufacturing rms to be exible,

ever-improving users of all available technologies to remain
competitive. Advocates of additive manufacturing also predict that this
arc of technological development will counter globalization, as end
users will do much~of their own manufacturing rather than engage in
trade to buy products from other people and corporations. The real
integration of the newer additive technologies into commercial
production, however, is more a matter of complementing traditional
subtractive methods rather than displacing them entirely.

The futurologist Jeremy Rifkin claimed that 3D printing signals the
beginning of a third industrial revolution, succeeding the production
line assembly that dominated manufacturing starting in the late 19th
century.

Since the 1950s, a number of writers and social commentators have
speculated in some depth about the social and cultural changes that
might result from the advent of commercially aordable additive
manufacturing technology. Amongst the more notable ideas to have emerged
from these inquiries has been the suggestion that, as more and more 3D
printers start to enter people's homes, the conventional relationship
between the home and the workplace might get further eroded. Likewise,
it has also been suggested that, as it becomes easier for businesses to
transmit designs for new objects around the globe, so the need for
high-speed freight services might also become less. Finally, given the
ease with which certain objects can now be replicated, it remains to be
seen whether changes will be made to current copyright legislation so as
to protect intellectual property rights with the new technology widely
available.

As 3D printers became more accessible to consumers, online social
platforms have developed to support

the community. This includes websites that allow users to access
information such as how to build a 3D

printer, as well as social forums that discuss how to improve 3D print
quality and discuss 3D printing news, as well as social media websites
that are dedicated to share 3D models. RepRap is a wiki based website
that was created to hold all information on 3d printing, and has
developed into a community that aims to bring 3D printing to everyone.
Furthermore, there are other sites such as Pinshape, Thingiverse and
MyMiniFactory, which were created initially to allow users to post 3D
les for anyone to print, allowing for decreased transaction cost of
sharing 3D les. These websites have allowed greater social interaction
between users, creating communities dedicated to 3D printing.

Some call attention to the conjunction of Commons-based peer production
with 3D printing and other lowcost manufacturing techniques. The
self-reinforced fantasy of a system of eternal growth can be overcome
with the development of economies of scope, and here, society can play
an important role contributing to the raising of the whole productive
structure to a higher plateau of more sustainable and customized
productivity. Further, it is true that many issues, problems, and
threats arise due to the democratization of the means of production, and
especially regarding the physical ones. For instance, the recyclability
of advanced nanomaterials is still questioned; weapons manufacturing
could become easier; not to mention the implications for counterfeiting
and on IP. It might be maintained that in contrast to the industrial
paradigm whose competitive dynamics were about economies of scale,
Commons-based peer production 3D printing could develop economies of
scope. While the advantages of scale rest on cheap global
transportation, the economies of scope share infrastructure costs
(intangible and tangible productive resources), taking advantage of the
capabilities of the fabrication tools. And following Neil Gershenfeld in
that \textbackslash{}some of the least developed parts of the world need
some of the most advanced technologies," Commons-based peer production
and 3D printing may oer the necessary tools for thinking globally but
acting locally in response to certain needs.

Larry Summers wrote about the \textbackslash{}devastating consequences"
of 3D printing and other technologies (robots, articial intelligence,
etc.) for those who perform routine tasks. In his view,
\textbackslash{}already there are more American men on disability
insurance than doing production work in manufacturing. And the trends
are all in the wrong direction, particularly for the less skilled, as
the capacity of capital embodying articial intelligence to replace
white-collar as well as blue-collar work will increase rapidly in the
years ahead." Summers recommends more vigorous cooperative eorts to
address the \textbackslash{}myriad devices" (e.g., tax havens, bank
secrecy, money laundering, and regulatory arbitrage) enabling the
holders of great wealth to \textbackslash{}avoid paying" income and
estate taxes, and to make it more dicult to accumulate great fortunes
without requiring \textbackslash{}great social contributions" in return,
including: more vigorous enforcement of anti-monopoly laws, reductions
in \textbackslash{}excessive" protection for intellectual property,
greater encouragement of prot-sharing schemes that may benet workers and
give them a stake in wealth accumulation, strengthening of collective
bargaining~arrangements, improvements in corporate governance,
strengthening of nancial regulation to eliminate subsidies to nancial
activity, easing of land-use restrictions that may cause the real estate
of the rich to keep rising in value, better training for young people
and retraining for displaced workers, and increased public and private
investment in infrastructure development\textbar{}e.g., in energy
production and transportation.

Michael Spence wrote that \textbackslash{}Now comes a . . . powerful,
wave of digital technology that is replacing labor

in increasingly complex tasks. This process of labor substitution and
disintermediation has been underway for some time in service
sectors\textbar{}think of ATMs, online banking, enterprise resource
planning, customer relationship management, mobile payment systems, and
much more. This revolution is spreading to the production of goods,
where robots and 3D printing are displacing labor." In his view, the
vast majority of the cost of digital technologies comes at the start, in
the design of hardware (e.g. 3D printers) and, more important, in
creating the software that enables machines to carry out various tasks.
\textbackslash{}Once this is achieved, the marginal cost of the hardware
is relatively low (and declines as scale rises), and the marginal cost
of replicating the software is essentially zero. With a huge potential
global market to amortize the upfront xed costs of design and testing,
the incentives to invest {[}in digital technologies{]} are compelling."

Spence believes that, unlike prior digital technologies, which drove rms
to deploy underutilized pools of

valuable labor around the world, the motivating force in the current
wave of digital technologies \textbackslash{}is cost

reduction via the replacement of labor." For example, as the cost of 3D
printing technology declines, it is

\textbackslash{}easy to imagine" that production may become
\textbackslash{}extremely" local and customized. Moreover, production

may occur in response to actual demand, not anticipated or forecast
demand. Spence believes that labor,

no matter how inexpensive, will become a less important asset for growth
and employment expansion, with labor-intensive, process-oriented
manufacturing becoming less eective, and that re-localization will
appear in both developed and developing countries. In his view,
production will not disappear, but it will be less labor-intensive, and
all countries will eventually need to rebuild their growth models around
digital technologies and the human capital supporting their deployment
and expansion. Spence writes that \textbackslash{}the world we are
entering is one in which the most powerful global ows will be ideas and
digital capital, not goods, services, and traditional capital. Adapting
to this will require shifts in mindsets, policies, investments
(especially in human capital), and quite possibly models of employment
and distribution."

Naomi Wu regards the usage of 3D printing in the Chinese classroom
(where rote memorization is standard) to teach design principles and
creativity as the most exciting recent development of the technology,
and more generally regards 3D printing as being the next desktop
publishing revolution.

\section*{Self-driving car}

{\label{258826}}

\subsection*{Outline}

{\label{812333}}

A self-driving car (also known as an autonomous car or a driverless car)
is a vehicle that is capable of sensing its environment and moving with
little or no human input.

Autonomous cars combine a variety of sensors to perceive their
surroundings, such as radar, computer vision, Lidar, sonar, GPS,
odometry and inertial measurement units. Advanced control systems
interpret sensory information to identify appropriate navigation paths,
as well as obstacles and relevant signage.

Potential benets include reduced costs, increased safety, increased
mobility, increased customer satisfaction and reduced crime. Safety
benets include a reduction in trac collisions, resulting injuries and
related costs, including for insurance. Automated cars are predicted to
increase trac ow; provide enhanced mobility for children, the elderly,
disabled, and the poor; relieve travelers from driving and navigation
chores; lower fuel consumption; signicantly reduce needs for parking
space; reduce crime; and facilitate business models for transportation
as a service, especially via the sharing economy.

Problems include safety, technology, liability, desire by individuals to
control their cars, legal framework

and government regulations; risk of loss of privacy and security
concerns, such as hackers or terrorism;

concern about the resulting loss of driving-related jobs in the road
transport industry; and risk of increased suburbanization as travel
becomes more convenient.

\subsection*{History}

{\label{499827}}

General Motors' Firebird II of the 1950s was described as having an
\textbackslash{}electronic brain" that allowed it to

move into a lane with a metal conductor and follow it along.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure7/Figure7}
\caption{{Figure 7: Waymo Chrysler Pacica Hybrid undergoing testing in the San
Francisco Bay Area.
{\label{684581}}%
}}
\end{center}
\end{figure}

Experiments have been conducted on automating driving since at least the
1920s; trials began in the 1950s. The rst truly automated car was
developed in 1977, by Japan's Tsukuba Mechanical Engineering Laboratory.
The vehicle tracked white street markers, which were interpreted by two
cameras on the vehicle, using an analog computer for signal processing.
The vehicle reached speeds up to 30 kilometres per hour (19 mph), with
the support of an elevated rail.

Autonomous prototype cars appeared in the 1980s, with Carnegie Mellon
University's Navlab and ALV

projects funded by DARPA starting in 1984 and Mercedes-Benz and
Bundeswehr University Munich's EUREKA Prometheus Project in 1987. By
1985, the ALV had demonstrated self-driving speeds on two-lane

roads of 31 kilometers per hour (19 mph) with obstacle avoidance added
in 1986 and o-road driving in

day and nighttime conditions by 1987. From the 1960s through the second
DARPA Grand Challenge in

2005, automated vehicle research in the U.S. was primarily funded by
DARPA, the US Army and the U.S.

Navy yielding incremental advances in speeds, driving competence in more
complex conditions, controls and sensor systems. Companies and research
organizations have developed prototypes.

The U.S. allocated \$650 million in 1991 for research on the National
Automated Highway System, which

demonstrated automated driving through a combination of automation,
embedded in the highway with

automated technology in vehicles and cooperative networking between the
vehicles and with the highway infrastructure. The program concluded with
a successful demonstration in 1997 but without clear direction or
funding to implement the system on a larger scale. Partly funded by the
National Automated Highway System and DARPA, the Carnegie Mellon
University Navlab drove 4,584 kilometers (2,848 mi) across America in
1995, 4,501 kilometers (2,797 mi) or 98\% of it autonomously. Navlab's
record achievement stood unmatched for two decades until 2015 when
Delphi improved it by piloting an Audi, augmented with Delphi
technology, over 5,472 kilometers (3,400 mi) through 15 states while
remaining in self-driving mode 99\%~of the time. In 2015, the US states
of Nevada, Florida, California, Virginia, and Michigan, together with
Washington, D.C., allowed the testing of automated cars on public roads.

In 2017, Audi stated that its latest A8 would be automated at speeds of
up to 60 kilometres per hour (37

mph) using its \textbackslash{}Audi AI." The driver would not have to do
safety checks such as frequently gripping the

steering wheel. The Audi A8 was claimed to be the rst production car to
reach level 3 automated driving,

and Audi would be the rst manufacturer to use laser scanners in addition
to cameras and ultrasonic sensors for their system.

In November 2017, Waymo announced that it had begun testing driverless
cars without a safety driver in the driver position; however, there is
still an employee in the car. In July 2018, Waymo announced that its
test~vehicles had traveled in automated mode for over 8,000,000 miles
(13,000,000 km), increasing by 1,000,000 miles (1,600,000 kilometers)
per month (Fig. 7).

\subsection*{Terminology}

{\label{686121}}

There is some inconsistency in terminology used in the self-driving car
industry. Various organizations have proposed to dene an accurate and
consistent vocabulary. Such confusion has been documented in SAE J3016
which states that \textbackslash{}Some vernacular usages associate
autonomous specically with full driving automation (level 5), while
other usages apply it to all levels of driving automation, and some
state legislation has dened it to correspond approximately to any ADS at
or above level 3 (or to any vehicle equipped with such an ADS)."

\subsubsection*{Words denition and safety
considerations}

{\label{874899}}

Modern vehicles provide partly automated features such as keeping the
car within its lane, speed controls

or emergency braking. Nonetheless, dierences remain between a fully
autonomous self-driving car on one hand and driver assistance
technologies on the other hand. According to the BBC, confusion between
those concepts leads to deaths.

Association of British Insurers considers the usage of the word
autonomous in marketing for modern cars to be dangerous, because car ads
make motorists think `autonomous' and `autopilot' means a vehicle can
drive itself, when they still rely on the driver to ensure safety.
Technology alone still is not able to drive the car.

When some car makers suggest or claim vehicles are self-driving, when
they are only partly automated,

drivers risk becoming excessively condent, leading to crashes, while
fully self-driving cars are still a long

way o in the UK.

\subsubsection*{Autonomous vs. automated}

{\label{569588}}

Autonomous means self-governing. Many historical projects related to
vehicle automation have been automated (made automatic) subject to a
heavy reliance on articial aids in their environment, such as magnetic
strips. Autonomous control implies satisfactory performance under
signicant uncertainties in the environment and the ability to compensate
for system failures without external intervention.

One approach is to implement communication networks both in the
immediate vicinity (for collision avoidance) and farther away (for
congestion management). Such outside in uences in the decision process
reduce an individual vehicle's autonomy, while still not requiring human
intervention.

Wood et al. (2012) wrote, \textbackslash{}This Article generally uses
the term `autonomous,' instead of the term `automated.` " The term
\textbackslash{}autonomous" was chosen \textbackslash{}because it is the
term that is currently in more widespread use (and thus is more familiar
to the general public). However, the latter term is arguably more
accurate. `Automated' connotes control or operation by a machine, while
`autonomous' connotes acting alone or independently. Most of the vehicle
concepts (that we are currently aware of) have a person in the driver's
seat, utilize a communication connection to the Cloud or other vehicles,
and do not independently select~either destinations or routes for
reaching them. Thus, the term `automated' would more accurately describe
these vehicle concepts." As of 2017, most commercial projects focused on
automated vehicles that did not communicate with other vehicles or with
an enveloping management regime.

Put in the words of one Nissan engineer, \textbackslash{}A truly
autonomous car would be one where you request it to

take you to work and it decides to go to the beach instead."

EuroNCAP denes autonomous in \textbackslash{}Autonomous Emergency
Braking" as: \textbackslash{}the system acts independently

of the driver to avoid or mitigate the accident." which implies the
autonomous system is not the driver.

\subsubsection*{Autonomous versus
cooperative}

{\label{680563}}

To make a car travel without any driver embedded within the vehicle some
system makers used a remote

driver.

But according to SAE J3016, some driving automation systems may indeed
be autonomous if they perform all of their functions independently and
self-suciently, but if they depend on communication and/or cooperation
with outside entities, they should be considered cooperative rather than
autonomous.

\subsubsection*{Self-driving car}

{\label{619923}}

Techemergence says.

``Self-driving'' is a rather vague term with a vague meaning

-?Techemergence

PC mag denition is: A computer-controlled car that drives itself. Also
called an \textbackslash{}autonomous vehicle"

and \textbackslash{}driverless car," self-driving cars date back to the
1939 New York World's Fair when General Motors

predicted the development of self-driving, radio-controlled electric
cars.

UCSUSA denition is: Self-driving vehicles are cars or trucks in which
human drivers are never required

to take control to safely operate the vehicle. Also known as autonomous
or \textbackslash{}driverless" cars, they combine sensors and software
to control, navigate, and drive the vehicle. Currently, there are no
legally operating, fully-autonomous vehicles in the United States.

NHTSA denition is: These self-driving vehicles ultimately will integrate
onto U.S. roadways by progressing

through six levels of driver assistance technology advancements in the
coming years. This includes everything from no automation (where a fully
engaged driver is required at all times), to full autonomy (where an
automated vehicle operates independently, without a human driver).

\subsection*{Classication}

{\label{488524}}

A classication system based on six dierent levels (ranging from fully
manual to fully automated systems)

was published in 2014 by SAE International, an automotive
standardization body, as J3016, Taxonomy and Denitions for Terms Related
to On-Road Motor Vehicle Automated Driving Systems. This classication

system is based on the amount of driver intervention and attentiveness
required, rather than the vehicle

capabilities, although these are very loosely related. In the United
States in 2013, the National Highway

Trac Safety Administration (NHTSA) released a formal classication
system, but abandoned this system

in favor of the SAE standard in 2016. Also in 2016, SAE updated its
classication, called J3016 201609.

\subsubsection*{Levels of driving
automation}

{\label{372245}}

In SAE's automation level denitions, \textbackslash{}driving mode" means
\textbackslash{}a type of driving scenario with characteristic

dynamic driving task requirements (e.g., expressway merging, high speed
cruising, low speed trac jam,

closed-campus operations, etc.)"

Level 0: Automated system issues warnings and may momentarily intervene
but has no sustained vehicle

control.

Level 1 (\textbackslash{}hands on"): The driver and the automated system
share control of the vehicle. Examples are

Adaptive Cruise Control (ACC), where the driver controls steering and
the automated system controls

speed; and Parking Assistance, where steering is automated while speed
is under manual control. The driver must be ready to retake full control
at any time. Lane Keeping Assistance (LKA) Type II is a further

example of level 1 self-driving.

Level 2 (\textbackslash{}hands o"): The automated system takes full
control of the vehicle (accelerating, braking, and

steering). The driver must monitor the driving and be prepared to
intervene immediately at any time if the automated system fails to
respond properly. The shorthand \textbackslash{}hands o" is not meant to
be taken literally. In fact, contact between hand and wheel is often
mandatory during SAE 2 driving, to conrm that the driver is ready to
intervene.

Level 3 (\textbackslash{}eyes o"): The driver can safely turn their
attention away from the driving tasks, e.g. the driver

can text or watch a movie. The vehicle will handle situations that call
for an immediate response, like

emergency braking. The driver must still be prepared to intervene within
some limited time, specied by the manufacturer, when called upon by the
vehicle to do so. As an example, the 2018 Audi A8 Luxury Sedan was the
rst commercial car to claim to be capable of level 3 self-driving. This
particular car has a so-called Trac Jam Pilot. When activated by the
human driver, the car takes full control of all aspects of driving in
slow-moving trac at up to 60 kilometers per hour (37 mph). The function
works only on highways with a physical barrier separating one stream of
trac from oncoming trac.

Level 4 (\textbackslash{}mind o"): As level 3, but no driver attention
is ever required for safety, i.e. the driver may safely

go to sleep or leave the driver's seat. Self-driving is supported only
in limited spatial areas (geofenced) or

under special circumstances, like trac jams. Outside of these areas or
circumstances, the vehicle must be able to safely abort the trip, i.e.
park the car, if the driver does not retake control.

Level 5 (\textbackslash{}steering wheel optional"): No human
intervention is required at all. An example would be a robotic taxi.

In the formal SAE denition below, note in particular what happens in the
shift from SAE 2 to SAE 3: the

human driver no longer has to monitor the environment. This is the nal
aspect of the \textbackslash{}dynamic driving

task" that is now passed over from the human to the automated system. At
SAE 3, the human driver still

has the responsibility to intervene when asked to do so by the automated
system. At SAE 4 the human

driver is relieved of that responsibility and at SAE 5 the automated
system will never need to ask for an

intervention.

\subsection*{Legal denition}

{\label{731890}}

In the district of Columbia (DC) code, \textbackslash{}Autonomous
vehicle" means a vehicle capable of navigating

District roadways and interpreting trac-control devices without a driver
actively operating any of the

vehicle's control systems. The term \textbackslash{}autonomous vehicle"
excludes a motor vehicle enabled with active

safety systems or driver- assistance systems, including systems to
provide electronic blind-spot assistance, crash avoidance, emergency
braking, parking assistance, adaptive cruise control, lane-keep
assistance, lanedeparture warning, or trac-jam and queuing assistance,
unless the system alone or in combination with other systems enables the
vehicle on which the technology is installed to drive without active
control or monitoring by a human operator.

In the same district code, it is considered that:

\begin{quote}
An autonomous vehicle may operate on a public roadway; provided, that
the vehicle:
\end{quote}

\begin{enumerate}
\tightlist
\item
  Has a manual override feature that allows a driver to assume control
  of the autonomous vehicle at any time;~
\item
  ~Has a driver seated in the control seat of the vehicle while in
  operation who is prepared to take control of the autonomous vehicle at
  any moment; and
\item
  Is capable of operating in compliance with the District's applicable
  trac laws and motor vehicle laws and trac control devices.
\end{enumerate}

\subsection*{Technical challenges}

{\label{404791}}

The challenge for driverless car designers is to produce control systems
capable of analyzing sensory data in order to provide accurate detection
of other vehicles and the road ahead. Modern self-driving cars generally
use Bayesian simultaneous localization and mapping (SLAM) algorithms,
which fuse data from multiple sensors and an o-line map into current
location estimates and map updates. Waymo has developed a variant of
SLAM with detection and tracking of other moving objects (DATMO), which
also handles obstacles such as cars and pedestrians. Simpler systems may
use roadside real-time locating system (RTLS) technologies to aid
localization. Typical sensors include Lidar, stereo vision, GPS and IMU.
Udacity is developing an open-source software stack. Control systems on
automated cars may use Sensor Fusion, which is an approach that
integrates information from a variety of sensors on the car to produce a
more consistent, accurate, and useful view of the environment.

Driverless vehicles require some form of machine vision for the purpose
of visual object recognition. Automated cars are being developed with
deep neural networks, a type of deep learning architecture with many
computational stages, or levels, in which neurons are simulated from the
environment that activate the network. The neural network depends on an
extensive amount of data extracted from real-life driving scenarios,
enabling the neural network to \textbackslash{}learn" how to execute the
best course of action.

In May 2018, researchers from MIT announced that they had built an
automated car that can navigate

unmapped roads. Researchers at their Computer Science and Articial
Intelligence Laboratory (CSAIL)

have developed a new system, called MapLite, which allows self-driving
cars to drive on roads that they

have never been on before, without using 3D maps. The system combines
the GPS position of the vehicle, a ``sparse topological map'' such as
OpenStreetMap, (i.e. having 2D features of the roads only), and a series
of sensors that observe the road conditions.

\subsection*{Human factor challenges}

{\label{165851}}

Alongside the many technical challenges that autonomous cars face, there
exist many human and social

factors that may impede upon the wider uptake of the technology. As
things become more automated, the human users need to have trust in the
automation, which can be a challenge in itself.

\subsection*{Testing}

{\label{372480}}

Testing vehicles with varying degrees of automation can be done
physically, in closed environments, on public roads (where permitted,
typically with a license or permit or adhering to a specic set of
operating principles) or virtually, i.e. in computer simulations. When
driven on public roads, automated vehicles require a person to monitor
their proper operation and \textbackslash{}take over" when needed. Apple
is currently testing self-driven cars, and has increased the number of
test vehicles from 3 to 27 in January 2018, and to 45 in March 2018.

One way to assess the progress of automated vehicles is to compute the
average distance driven between

\textbackslash{}disengagements``, when the automated system is turned o,
typically by a human driver. In 2017, Waymo reported 63 disengagements
over 352,545 miles (567,366 km) of testing, or 5,596 miles (9,006 km) on
average, the highest among companies reporting such gures. Waymo also
traveled more distance in total than any other. Their 2017 rate of 0.18
disengagements per 1,000 miles (1,600 km) was an improvement from 0.2
disengagements per 1,000 miles (1,600 km) in 2016 and 0.8 in 2015. In
March, 2017, Uber reported an average of 0.67 miles (1.08 km) per
disengagement. In the nal three months of 2017, Cruise Automation (now
owned by GM) averaged 5,224 miles (8,407 km) per disruption over 62,689
miles (100,888 km). In July~2018, the rst electric driverless racing car
\textbackslash{}Robocar'' completed 1.8 kilometers track, using its
navigation

system and articial intelligence (Table 1).

\par\null\selectlanguage{english}
\begin{table}[h!]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCC}
Maker & Distance between disengagements & Distance \\
BMW & 638 miles (1027\selectlanguage{ngerman} km) & 638 miles (1027\selectlanguage{ngerman} km) \\
Bosch & 0.68 miles (1.09\selectlanguage{ngerman} km) & 983 miles (1582\selectlanguage{ngerman} km) \\
Delphi Automotive Systems & 14.9 miles (24.0\selectlanguage{ngerman} km) & 2658 miles (4278\selectlanguage{ngerman} km) \\
Ford & 196.6 miles (316.4\selectlanguage{ngerman} km) & 590 miles (950\selectlanguage{ngerman} km) \\
General Motors & 54.7 miles (88.0\selectlanguage{ngerman} km) & 8156 miles (13126\selectlanguage{ngerman} km) \\
Mercedes Benz & 2 miles (3.2\selectlanguage{ngerman} km) & 673 miles (1083\selectlanguage{ngerman} km) \\
Nissan & 263.3 miles (423.7\selectlanguage{ngerman} km) & 6056 miles (9746\selectlanguage{ngerman} km) \\
Tesla & 2.9 miles (4.7\selectlanguage{ngerman} km) & 550 miles (890\selectlanguage{ngerman} km) \\
Waymo & 5127.9 miles (8252.6\selectlanguage{ngerman} km) & 635868 miles (1023330\selectlanguage{ngerman} km) \\
\end{tabulary}
\caption{{Table 1: Self-driving car testing results.
{\label{747005}}%
}}
\end{table}\subsection*{Fields of application}

{\label{344349}}

\subsubsection*{Automated trucks}

{\label{251138}}

Several companies are said to be testing automated technology in semi
trucks. Otto, a self-driving trucking company that was acquired by Uber
in August 2016, demonstrated their trucks on the highway before being
acquired. In May 2017, San Francisco-based startup Embark announced a
partnership with truck manufacturer Peterbilt to test and deploy
automated technology in Peterbilt's vehicles. Waymo has also said to be
testing automated technology in trucks, however no timeline has been
given for the project.

In March 2018, Starsky Robotics, the San Francisco-based automated truck
company, completed a 7-mile

(11 km) fully driverless trip in Florida without a single human in the
truck. Starsky Robotics became the

rst player in the self-driving truck game to drive in fully automated
mode on a public road without a person in the cab. In Europe, the truck
Platooning is considered with the Safe Road Trains for the Environment
approach. Vehicular automation also covers other kinds of vehicles such
as Buses, Trains, Trucks. Lockheed Martin with funding from the U.S.
Army developed an automated truck convoying system that uses a lead
truck operated by a human driver with a number of trucks following
autonomously. Developed as part of the Army's Autonomous Mobility
Applique System (AMAS), the system consists of an automated driving
package that has been installed on more than nine types of vehicles and
has completed more than 55,000 hours of driving at speeds up to 64
kilometres per hour (40 mph) as of 2014. As of 2017 the Army was
planning to eld 100-200 trucks as part of a rapid-elding program.

\subsubsection*{Transport systems}

{\label{927282}}

In Europe, cities in Belgium, France, Italy and the UK are planning to
operate transport systems for

automated cars, and Germany, the Netherlands, and Spain have allowed
public testing in trac. In 2015,

the UK launched public trials of the LUTZ Pathnder automated pod in
Milton Keynes. Beginning in

summer 2015, the French government allowed PSA Peugeot-Citroen to make
trials in real conditions in the Paris area. The experiments were
planned to be extended to other cities such as Bordeaux and Strasbourg
by 2016. The alliance between French companies THALES and Valeo
(provider of the rst self-parking car system that equips Audi and
Mercedes premi) is testing its own system. New Zealand is planning to
use automated vehicles for public transport in Tauranga and
Christchurch.

In China, Baidu and King Long produce automated minibus, a vehicle with
14 seats, but without driving

seat. With 100 vehicles produced, 2018 will be the rst year with
commercial automated service in China.

Those minibuses should be at level 4, that is driverless in closed
roads.~~

\subsection*{Potential advantages}

{\label{862648}}

\subsubsection*{Safety}

{\label{600493}}

Driving safety experts predict that once driverless technology has been
fully developed, trac collisions (and resulting deaths and injuries and
costs), caused by human error, such as delayed reaction time,
tailgating, rubbernecking, and other forms of distracted or aggressive
driving should be substantially reduced. Consulting rm McKinsey \&
Company estimated that widespread use of autonomous vehicles could
\textbackslash{}eliminate 90\% of all auto accidents in the United
States, prevent up to US\$190 billion in damages and health-costs
annually and save thousands of lives."

According to motorist website \textbackslash{}TheDrive.com" operated by
Time magazine, none of the driving safety

experts they were able to contact were able to rank driving under an
autopilot system at the time (2017)

as having achieved a greater level of safety than traditional fully
hands-on driving, so the degree to which

these benets asserted by proponents will manifest in practice cannot be
assessed. Confounding factors that could reduce the net eect on safety
may include unexpected interactions between humans and partly or fully
automated vehicles, or between dierent types of vehicle system;
complications at the boundaries of functionality at each automation
level (such as handover when the vehicle reaches the limit of its
capacity); the eect of the bugs and aws that inevitably occur in complex
interdependent software systems; sensor or data shortcomings; and
successful compromise by malicious interveners.

\subsubsection*{Welfare}

{\label{128238}}

Automated cars could reduce labor costs; relieve travelers from driving
and navigation chores, thereby replacing behind-the-wheel commuting
hours with more time for leisure or work; and also would lift
constraints on occupant ability to drive, distracted and texting while
driving, intoxicated, prone to seizures, or otherwise impaired. For the
young, the elderly, people with disabilities, and low-income citizens,
automated cars could provide enhanced mobility. The removal of the
steering wheel\textbar{}along with the remaining driver interface and
the requirement for any occupant to assume a forward-facing
position\textbar{}would give the interior of the cabin greater ergonomic
exibility. Large vehicles, such as motorhomes, would attain appreciably
enhanced ease of use.

\subsubsection*{Traffic}

{\label{534617}}

Additional advantages could include higher speed limits; smoother rides;
and increased roadway capacity; and minimized trac congestion, due to
decreased need for safety gaps and higher speeds. Currently, maximum
controlled-access highway throughput or capacity according to the U.S.
Highway Capacity Manual is about 2,200 passenger vehicles per hour per
lane, with about 5\% of the available road space is taken up by cars.
One study estimated that automated cars could increase capacity by 273\%
(\textasciitilde{}8,200 cars per hour per lane). The study also
estimated that with 100\% connected vehicles using vehicle-to-vehicle
communication, capacity could reach 12,000 passenger vehicles per hour
(up 445\% from 2,200 pc/h per lane) traveling safely at 120 km/h (75
mph) with a following gap of about 6 m (20 ft) of each other. Currently,
at highway speeds drivers keep between 40 to 50 m (130 to 160 ft) away
from the car in front. These increases in highway capacity could have a
signicant impact in trac congestion, particularly in urban areas, and
even eectively end highway congestion in some places. The ability for
authorities to manage trac ow would increase, given the extra data and
driving behavior predictability combined with less need for trac police
and even road signage.

\subsubsection*{Lower costs}

{\label{726433}}

Safer driving is expected to reduce the costs of vehicle insurance.
Reduced trac congestion and the

improvements in trac ow due to widespread use of automated cars will
also translate into better fuel

eciency. Additionally, self-driving cars will be able to accelerate and
brake more eciently, meaning higher~fuel economy from reducing wasted
energy typically associated with inecient changes to speed (energy
typically lost due to friction, in the form of heat and sound).

\subsubsection*{Parking space}

{\label{447408}}

Manually driven vehicles are reported to be used only 4-5\% time, and
being parked and unused for the

remaining 95-96\% of the time. Autonomous vehicles could, on the other
hand, be used continuously after

it has reached its destination. This could dramatically reduce the need
for parking space. For example,

in Los Angeles, 14\% of the land is used for parking alone, equivalent
to some 17,020,594 square meters.

This combined with the potential reduced need for road space due to
improved trac ow, could free up

tremendous amounts of land in urban areas, which could then be used for
parks, recreational areas, buildings, among other uses; making cities
more livable.

\subsubsection*{Related effects}

{\label{290515}}

By reducing the (labor and other) cost of mobility as a service,
automated cars could reduce the number of cars that are individually
owned, replaced by taxi/pooling and other car sharing services. This
would also dramatically reduce the size of the automotive production
industry, with corresponding environmental and economic eects. Assuming
the increased eciency is not fully oset by increases in demand, more
ecient trac ow could free roadway space for other uses such as better
support for pedestrians and cyclists.

The vehicles' increased awareness could aid the police by reporting on
illegal passenger behavior, while

possibly enabling other crimes, such as deliberately crashing into
another vehicle or a pedestrian. However, this may also lead to much
expanded mass surveillance if there is wide access granted to third
parties to the large data sets generated. The future of passenger rail
transport in the era of automated cars is not clear.

\subsubsection*{Potential limits or
obstacles}

{\label{735278}}

The sort of hoped-for potential benets from increased vehicle automation
described may be limited by

foreseeable challenges, such as disputes over liability (will each
company operating a vehicle accept that

they are its \textbackslash{}driver" and thus responsible for what their
car does, or will some try to project this liability

onto others who are not in control?), the time needed to turn over the
existing stock of vehicles from nonautomated to automated, and thus a
long period of humans and autonomous vehicles sharing the roads,
resistance by individuals to having to forfeit control of their cars,
concerns about the safety of driverless in practice, and the
implementation of a legal framework and consistent global government
regulations for selfdriving cars. Other obstacles could include
de-skilling and lower levels of driver experience for dealing with
potentially dangerous situations and anomalies, ethical problems where
an automated vehicle's software is forced during an unavoidable crash to
choose between multiple harmful courses of action ('the trolley
problem'), concerns about making large numbers of people currently
employed as drivers unemployed (at the same time as many other alternate
blue collar occupations may be undermined by automation), the potential
for more intrusive mass surveillance of location, association and travel
as a result of police and intelligence agency access to large data sets
generated by sensors and pattern-recognition AI (making anonymous travel
dicult), and possibly insucient understanding of verbal sounds, gestures
and non-verbal cues by police, other drivers or pedestrians.

Possible technological obstacles for automated cars are:

\begin{itemize}
\tightlist
\item
  articial Intelligence is still not able to function properly in
  chaotic inner-city environments,~
\item
  a car's computer could potentially be compromised, as could a
  communication system between cars,~
\item
  susceptibility of the car's sensing and navigation systems to dierent
  types of weather (such as snow) or deliberate interference, including
  jamming and spoong,
\item
  avoidance of large animals requires recognition and tracking, and
  Volvo found that software suited to caribou, deer, and elk was
  ineective with kangaroos,
\item
  autonomous cars may require very high-quality specialized maps to
  operate properly. Where these maps may be out of date, they would need
  to be able to fall back to reasonable behaviors,
\item
  competition for the radio spectrum desired for the car's
  communication,~
\item
  eld programmability for the systems will require careful evaluation of
  product development and the component supply chain,
\item
  current road infrastructure may need changes for automated cars to
  function optimally,
\item
  discrepancy between people's beliefs of the necessary government
  intervention may cause a delay in accepting automated cars on the
  road. Whether the public desires no change in existing laws, federal
  regulation, or another solution; the framework of regulation will
  likely result in dierences of opinion,
\item
  employment - Companies working on the technology have an increasing
  recruitment problem in that the available talent pool has not grown
  with demand. As such, education and training by third party
  organizations such as providers of online courses and self-taught
  community-driven projects such as DIY Robocars and Formula Pi have
  quickly grown in popularity, while university level extra-curricular
  programmers such as Formula Student Driverless have bolstered graduate
  experience. Industry is steadily increasing freely available
  information sources, such as code, datasets and glossaries to iden the
  recruitment pool.
\end{itemize}

\subsection*{Potential disadvantages}

{\label{338698}}

A direct impact of widespread adoption of automated vehicles is the loss
of driving-related jobs in the road transport industry. There could be
resistance from professional drivers and unions who are threatened by
job losses. In addition, there could be job losses in public transit
services and crash repair shops. The automobile insurance industry might
suer as the technology makes certain aspects of these occupations
obsolete. A frequently cited paper by Michael Osborne and Carl Benedikt
Frey found that automated cars would make many jobs redundant.

Privacy could be an issue when having the vehicle's location and
position integrated into an interface in

which other people have access to. In addition, there is the risk of
automotive hacking through the sharing of information through V2V
(Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols.
There is also the risk of terrorist attacks. Self-driving cars could
potentially be loaded with explosives and used as bombs.

The lack of stressful driving, more productive time during the trip, and
the potential savings in travel time

and cost could become an incentive to live far away from cities, where
land is cheaper, and work in the

city's core, thus increasing travel distances and inducing more urban
sprawl, more fuel consumption and an increase in the carbon footprint of
urban travel. There is also the risk that trac congestion might
increase, rather than decrease. Appropriate public policies and
regulations, such as zoning, pricing, and urban design are required to
avoid the negative impacts of increased suburbanization and longer
distance travel.

Some believe that once automation in vehicles reaches higher levels and
becomes reliable, drivers will pay less attention to the road. Research
shows that drivers in automated cars react later when they have to
intervene in a critical situation, compared to if they were driving
manually. Depending on the capabilities of automated vehicles and the
frequency with which human intervention is needed, this may counteract
any increase in safety, as compared to all-human driving, that may be
delivered by other factors.

Ethical and moral reasoning come into consideration when programming the
software that decides what

action the car takes in an unavoidable crash; whether the automated car
will crash into a bus, potentially

killing people inside; or swerve elsewhere, potentially killing its own
passengers or nearby pedestrians. A

question that programmers of AI systems nd dicult to answer (as do
ordinary people, and ethicists) is

\textbackslash{}what decision should the car make that causes the
`smallest' damage to people's lives?"

The ethics of automated vehicles are still being articulated, and may
lead to controversy. They may also

require closer consideration of the variability, context-dependency,
complexity and non-deterministic nature of human ethics. Dierent human
drivers make various ethical decisions when driving, such as avoiding
harm to themselves, or putting themselves at risk to protect others.
These decisions range from rare extremes~such as self-sacrice or
criminal negligence, to routine decisions good enough to keep the trac
owing but bad enough to cause accidents, road rage and stress.

Human thought and reaction time may sometimes be too slow to detect the
risk of an upcoming fatal crash, think through the ethical implications
of the available options, or take an action to implement an ethical
choice. Whether a particular automated vehicle's capacity to correctly
detect an upcoming risk, analyze the options or choose a `good' option
from among bad choices would be as good or better than a particular
human's may be dicult to predict or assess. This diculty may be in part
because the level of automated vehicle system understanding of the
ethical issues at play in a given road scenario, sensed for an instant
from out of a continuous stream of synthetic physical predictions of the
near future, and dependent on layers of pattern recognition and
situational intelligence, may be opaque to human inspection because of
its origins in probabilistic machine learning rather than a simple,
plain English `human values' logic of parsable rules. The depth of
understanding, predictive power and ethical sophistication needed will
be hard to implement, and even harder to test or assess.

The scale of this challenge may have other eects. There may be few
entities able to marshal the resources and AI capacity necessary to meet
it, as well as the capital necessary to take an automated vehicle system
to market and sustain it operationally for the life of a vehicle, and
the legal and `government aairs' capacity to deal with the potential for
liability for a signicant proportion of trac accidents. This may have
the eect of narrowing the number of dierent system operators, and
eroding the presently quite diverse global vehicle market down to a
small number of system suppliers.~~

\subsection*{References}

{\label{627332}}

Brian Wang. Uber' self-driving system was still 400 times worse
{[}than{]} Waymo in 2018 on key distance

intervention metric\emph{. NextBigFuture.com,} 2018.

\par\null

\subsection*{~}\label{section}

\selectlanguage{english}
\FloatBarrier
\end{document}

