Internet of things
Outline
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-internet-enabled 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.
History
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. Mark
Weiser’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 Främling 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.
Internet of Things.
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.
Applications Consumer applications 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. 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 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. Commercial applications
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 in-home
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. 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. the vehicle, 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. 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. Industrial applications
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. 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.
Infrastructure applications 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. 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. 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 end-users, but also manage distribution automation devices
like transformers. 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. Trends and
characteristics 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.
Technology roadmap: Internet of things.
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. 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. 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. 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. 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. Space considerations
In the Internet of things, the precise geographic location of a
thing—and also the precise geographic dimensions of a thing—will 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. A solution to
“basket of remotes” Many IoT devices have a potential to take a piece
of this market. Jean-Louis Gassée (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. Frameworks 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.
3D printing Outline 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.
A MakerBot three-dimensional printer.
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. Terminology 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. 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 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. History
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. 1984 On 16 July 1984, Alain Le Méhauté, Olivier de
Witte, and Jean Claude André 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. 1988 The technology used by
most 3D printers to date—especially hobbyist and consumer-oriented
models—is 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. 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). 1995 In 1995 the Fraunhofer Institute developed the
selective laser melting process. 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. 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. 2018 re:3D develops a
system that uses plastic pellets that can be made by grinding up waste
plastic. General principles 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.
CAD model used for 3D printing.
Printing Before printing a 3D model from an STL file, it must first be
examined for errors. Most CAD applications produce errors in output STL
files of the following types: holes, faces normal, self-intersections,
noise shells, manifold errors. 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 (µm). Typical layer
thickness is around 100 µm (250 DPI), although some machines can print
layers as thin as 16 µm (1,600 DPI). X–Y resolution is comparable to
that of laser printers. The particles (3D dots) are around 50 to 100 µm
(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 file for a given model input file. Specifying
higher resolution results in larger files 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 flexible
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. Finishing Though the
printer-produced resolution is sufficient 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 effect on part surfaces which are curved or tilted in
respect to the building platform. The effects strongly depend on the
orientation of a part surface inside the building process. Some
printable polymers such as ABS, allow the surface finish 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
off the metal substrate after deposition. A new process for the GMAW 3D
printing allows for substrate surface modifications to remove aluminum
or steel. Processes and printers A large number of additive processes
are available. The main differences 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 offer a choice of powder and polymer for the material used to
build the object. Others sometimes use standard, off-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 defines
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 filament 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 filament of thermoplastic, metal wire, or other material is
fed into an extrusion nozzle head (3D printer extruder), which heats the
material and turns the flow on and off. 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) filled with a liquid
photo-polymerizing resin; the solidified resin d) is progressively
dragged up by a lifting platform e) Other methods cure liquid materials
using different 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 µm) 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 solidified 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 “window”), which
causes the resin to solidify. The object rises slowly enough to allow
resin to flow 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 efforts to develop affordable 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. Applications 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. 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 three-dimensional objects. A
large variety of foods are appropriate candidates, such as chocolate and
candy, and flat foods such as crackers, pasta, and pizza.
A 3D selfie in 1:20 scale printed by Shapeways using gypsum-based
printing.
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-fit shoes for athletes. 3D
printing has come to the point where companies are printing consumer
grade eyewear with on-demand custom fit and styling (although they
cannot print the lenses). On-demand customization of glasses is possible
with rapid prototyping.
A Jet Engine turbine printed from the Howard Community College Makerbot.
Vanessa Friedman, fashion director and chief fashion critic at The New
York Times, says 3D printing will have a significant value for fashion
companies down the road, especially if it transforms into a
print-it-yourself tool for shoppers. “There’s real sense that this is
not going to happen anytime soon,” she says, “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: “Certainly
some of the fabrications that brands can use will be dramatically
changed by technology.” In cars, trucks, and aircraft, Additive
Manufacturing is beginning to transform both (1) unibody and fuselage
design and production and (2) powertrain design and production. For
example: 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 first car in the world car mounted
using the technology 3D printing (its bodywork and car windows were
“printed”), in 2014, Local Motors debuted Strati, a functioning
vehicle that was entirely 3D Printed using ABS plastic and carbon fiber,
except the powertrain. In May 2015 Airbus announced that its new Airbus
A350 XWB included over 1000 components manufactured by 3D printing, in
2015, a Royal Air Force Eurofighter Typhoon fighter jet flew 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, 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. AM’s impact on firearms involves two dimensions: new
manufacturing methods for established companies, and new possibilities
for the making of do-it-yourself firearms. In 2012, the US-based group
Defense Distributed disclosed plans to design a working plastic 3D
printed firearm “that could be downloaded and reproduced by anybody
with a 3D printer.” After Defense Distributed released their plans,
questions were raised regarding the effects that 3D printing and
widespread consumer-level CNC machining may have on gun control
effectiveness. 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 fit
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 efficiently
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 firms 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 first 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. Off 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 offer an unprecedented
“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 scientific
equipment from open hardware designs forming open-source labs. Future
applications for 3D printing might include creating open-source
scientific equipment. In the last several years 3D printing has been
intensively used by in the cultural heritage field 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 file 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 effective
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. Legal aspects Intellectual property 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-profit 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
finite 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, fixed 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 different 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.
Gun legislation and administration The US Department of Homeland
Security and the Joint Regional Intelligence Center released a memo
stating that “significant advances in three-dimensional (3D) printing
capabilities, availability of free digital 3D printable files for
firearms components, and difficulty regulating file sharing may present
public safety risks from unqualified gun seekers who obtain or
manufacture 3D printed guns” and that “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 files will be as difficult to
control as any other illegally traded music, movie or software files.”
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 file 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 firearms are not as easily obtainable. Officials 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 effect would increase. Aerospace regulation 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 “to
develop a robust quality system that adequately reflects regulatory
needs for quality assurance.” Health and safety 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 filament peaked a few minutes after printing started and
returned to baseline levels 100 minutes after printing ended. Emissions
from fused filament printers can include a large number of ultrafine
particles and volatile organic compounds (VOCs). The toxicity from
emissions varies by source material due to differences 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 filament printers have also been linked to asthma. Based on animal
studies, carbon nanotubes and carbon nanofibers sometimes used in fused
filament printing can cause pulmonary effects including inflammation,
granulomas, and pulmonary fibrosis 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 environments 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 filament
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 off the printer if it jammed, and using lower emission printers
and filaments. At least one case of severe injury was noted from an
explosion involved in metal powders used for fused filament. 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
finish parts after they have been printed. These post-processing
activities can include chemical baths, sanding, polishing, or vapor
exposure to refine surface finish, 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 specific to 3D printer emissions exist, certain source
materials used in 3D printing, such as carbon nanofiber 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. Impact Additive manufacturing, starting
with today’s infancy period, requires manufacturing firms to be
flexible, 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 affordable 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
files for anyone to print, allowing for decreased transaction cost of
sharing 3D files. 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 low-cost 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 “some of the least developed parts of the world need some of the
most advanced technologies,” Commons-based peer production and 3D
printing may offer the necessary tools for thinking globally but acting
locally in response to certain needs. Larry Summers wrote about the
“devastating consequences” of 3D printing and other technologies
(robots, artificial intelligence, etc.) for those who perform routine
tasks. In his view, “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 artificial intelligence to replace
white-collar as well as blue-collar work will increase rapidly in the
years ahead.” Summers recommends more vigorous cooperative efforts to
address the “myriad devices” (e.g., tax havens, bank secrecy, money
laundering, and regulatory arbitrage) enabling the holders of great
wealth to “avoid paying” income and estate taxes, and to make it more
difficult to accumulate great fortunes without requiring “great social
contributions” in return, including: more vigorous enforcement of
anti-monopoly laws, reductions in “excessive” protection for
intellectual property, greater encouragement of profit-sharing schemes
that may benefit workers and give them a stake in wealth accumulation,
strengthening of collective bargaining arrangements, improvements in
corporate governance, strengthening of financial regulation to eliminate
subsidies to financial 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—e.g., in energy production and transportation. Michael
Spence wrote that “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—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. “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 fixed
costs of design and testing, the incentives to invest [in digital
technologies] are compelling.” Spence believes that, unlike prior
digital technologies, which drove firms to deploy underutilized pools of
valuable labor around the world, the motivating force in the current
wave of digital technologies “is cost reduction via the replacement of
labor.” For example, as the cost of 3D printing technology declines, it
is “easy to imagine” that production may become “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 effective, 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 “the world we are entering is one in
which the most powerful global flows 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.
Self-driving car Outline 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 benefits include reduced
costs, increased safety, increased mobility, increased customer
satisfaction and reduced crime. Safety benefits include a reduction in
traffic collisions, resulting injuries and related costs, including for
insurance. Automated cars are predicted to increase traffic flow;
provide enhanced mobility for children, the elderly, disabled, and the
poor; relieve travelers from driving and navigation chores; lower fuel
consumption; significantly 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. History
General Motors’ Firebird II of the 1950s was described as having an
“electronic brain” that allowed it to move into a lane with a metal
conductor and follow it along.
Waymo Chrysler Pacifica Hybrid undergoing testing in the San Francisco
Bay Area.
Experiments have been conducted on automating driving since at least the
1920s; trials began in the 1950s. The first 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 off-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 “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 first production car to reach level 3
automated driving, and Audi would be the first 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. Terminology There is some inconsistency in terminology used
in the self-driving car industry. Various organizations have proposed to
define an accurate and consistent vocabulary. Such confusion has been
documented in SAE J3016 which states that “Some vernacular usages
associate autonomous specifically with full driving automation (level
5), while other usages apply it to all levels of driving automation, and
some state legislation has defined it to correspond approximately to any
ADS at or above level 3 (or to any vehicle equipped with such an ADS).”
Words definition and safety considerations Modern vehicles provide
partly automated features such as keeping the car within its lane, speed
controls or emergency braking. Nonetheless, differences 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 confident, leading to crashes, while fully self-driving cars
are still a long way off in the UK. Autonomous vs. automated Autonomous
means self-governing. Many historical projects related to vehicle
automation have been automated (made automatic) subject to a heavy
reliance on artificial aids in their environment, such as magnetic
strips. Autonomous control implies satisfactory performance under
significant 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 influences in the decision process reduce an
individual vehicle’s autonomy, while still not requiring human
intervention. Wood et al. (2012) wrote, “This Article generally uses
the term ‘autonomous,’ instead of the term ‘automated.’ ” The term
“autonomous” was chosen “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,
“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 defines
autonomous in “Autonomous Emergency Braking” as: “the system acts
independently of the driver to avoid or mitigate the accident.” which
implies the autonomous system is not the driver. Autonomous versus
cooperative 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-sufficiently, but
if they depend on communication and/or cooperation with outside
entities, they should be considered cooperative rather than autonomous.
Self-driving car Techemergence says. “Self-driving” is a rather vague
term with a vague meaning —?Techemergence PC mag definition is: A
computer-controlled car that drives itself. Also called an “autonomous
vehicle” and “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 definition 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 “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
definition 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). Classification A classification
system based on six different levels (ranging from fully manual to fully
automated systems) was published in 2014 by SAE International, an
automotive standardization body, as J3016, Taxonomy and Definitions for
Terms Related to On-Road Motor Vehicle Automated Driving Systems. This
classification 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 Traffic Safety Administration (NHTSA) released a formal
classification system, but abandoned this system in favor of the SAE
standard in 2016. Also in 2016, SAE updated its classification, called
J3016_201609. Levels of driving automation In SAE’s automation level
definitions, “driving mode” means “a type of driving scenario with
characteristic dynamic driving task requirements (e.g., expressway
merging, high speed cruising, low speed traffic jam, closed-campus
operations, etc.)” Level 0: Automated system issues warnings and may
momentarily intervene but has no sustained vehicle control. Level 1
(“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 (“hands off”): 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
“hands off” is not meant to be taken literally. In fact, contact
between hand and wheel is often mandatory during SAE 2 driving, to
confirm that the driver is ready to intervene. Level 3 (“eyes off”):
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,
specified by the manufacturer, when called upon by the vehicle to do so.
As an example, the 2018 Audi A8 Luxury Sedan was the first commercial
car to claim to be capable of level 3 self-driving. This particular car
has a so-called Traffic Jam Pilot. When activated by the human driver,
the car takes full control of all aspects of driving in slow-moving
traffic at up to 60 kilometers per hour (37 mph). The function works
only on highways with a physical barrier separating one stream of
traffic from oncoming traffic. Level 4 (“mind off”): 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 traffic 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
(“steering wheel optional”): No human intervention is required at all.
An example would be a robotic taxi. In the formal SAE definition 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 final
aspect of the “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. Legal
definition In the district of Columbia (DC) code, “Autonomous vehicle”
means a vehicle capable of navigating District roadways and interpreting
traffic-control devices without a driver actively operating any of the
vehicle’s control systems. The term “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, lane-departure warning, or traffic-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: An autonomous vehicle may
operate on a public roadway; provided, that the vehicle: (1) Has a
manual override feature that allows a driver to assume control of the
autonomous vehicle at any time; (2) 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 (3) Is capable of operating
in compliance with the District’s applicable traffic laws and motor
vehicle laws and traffic control devices. Technical challenges 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 off-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 “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 Artificial 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. Human factor challenges 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. Testing 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 specific 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 “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
“disengagements”, when the automated system is turned off, 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 figures. 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 final 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 first electric driverless racing
car “Robocar” completed 1.8 kilometers track, using its navigation
system and artificial intelligence.
Self-driving cartesting results. Source: Wang, Brian (25 March 2018).
“Uber’ self-driving system was still 400 times worse [than] Waymo
in 2018 on key distance intervention metric”. NextBigFuture.com.
Retrieved 25 March 2018.
Fields of application Automated trucks 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 first 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 field 100-200 trucks as part of a rapid-fielding
program. Transport systems 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
traffic. In 2015, the UK launched public trials of the LUTZ Pathfinder
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 first 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 first year with
commercial automated service in China. Those minibuses should be at
level 4, that is driverless in closed roads. Potential advantages Safety
Driving safety experts predict that once driverless technology has been
fully developed, traffic 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 firm McKinsey &
Company estimated that widespread use of autonomous vehicles could
“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 “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
benefits asserted by proponents will manifest in practice cannot be
assessed. Confounding factors that could reduce the net effect on safety
may include unexpected interactions between humans and partly or fully
automated vehicles, or between different 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 effect of the bugs and flaws that inevitably occur in
complex interdependent software systems; sensor or data shortcomings;
and successful compromise by malicious interveners. Welfare 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—along with the remaining driver interface and the
requirement for any occupant to assume a forward-facing position—would
give the interior of the cabin greater ergonomic flexibility. Large
vehicles, such as motorhomes, would attain appreciably enhanced ease of
use. Traffic Additional advantages could include higher speed limits;
smoother rides; and increased roadway capacity; and minimized traffic
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% (~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 significant impact in traffic congestion,
particularly in urban areas, and even effectively end highway congestion
in some places. The ability for authorities to manage traffic flow would
increase, given the extra data and driving behavior predictability
combined with less need for traffic police and even road signage. Lower
costs Safer driving is expected to reduce the costs of vehicle
insurance. Reduced traffic congestion and the improvements in traffic
flow due to widespread use of automated cars will also translate into
better fuel efficiency. Additionally, self-driving cars will be able to
accelerate and brake more efficiently, meaning higher fuel economy from
reducing wasted energy typically associated with inefficient changes to
speed (energy typically lost due to friction, in the form of heat and
sound). Parking space 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 traffic flow, 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. Related effects 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 effects. Assuming the increased efficiency is not fully offset
by increases in demand, more efficient traffic flow 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. Potential limits or obstacles The
sort of hoped-for potential benefits 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 “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
non-automated 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 self-driving 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 difficult), and possibly
insufficient understanding of verbal sounds, gestures and non-verbal
cues by police, other drivers or pedestrians. Possible technological
obstacles for automated cars are: artificial Intelligence is still not
able to function properly in chaotic inner-city environments, a car’s
computer could potentially be compromised, as could a communication
system between cars, susceptibility of the car’s sensing and navigation
systems to different types of weather (such as snow) or deliberate
interference, including jamming and spoofing, avoidance of large animals
requires recognition and tracking, and Volvo found that software suited
to caribou, deer, and elk was ineffective with kangaroos, 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, competition for the radio spectrum desired
for the car’s communication, field programmability for the systems will
require careful evaluation of product development and the component
supply chain, current road infrastructure may need changes for automated
cars to function optimally, 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 differences of opinion, 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 widen the recruitment pool. Potential
disadvantages 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 suffer 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 traffic 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 find difficult to answer (as do ordinary people, and ethicists)
is “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. Different 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-sacrifice or criminal
negligence, to routine decisions good enough to keep the traffic flowing
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
difficult to predict or assess. This difficulty 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 effects. 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 affairs’ capacity to deal with the potential for liability
for a significant proportion of traffic accidents. This may have the
effect of narrowing the number of different system operators, and
eroding the presently quite diverse global vehicle market down to a
small number of system suppliers.