chains.
AM's impact on rearms involves two dimensions: new manufacturing methods for established companies,
and new possibilities for the making of do-it-yourself rearms. In 2012, the US-based group Defense Distributed
disclosed plans to design a working plastic 3D printed rearm \that could be downloaded and
reproduced by anybody with a 3D printer." After Defense Distributed released their plans, questions were
raised regarding the eects that 3D printing and widespread consumer-level CNC machining may have on
gun control eectiveness.
Surgical uses of 3D printing-centric therapies have a history beginning in the mid-1990s with anatomical
modeling for bony reconstructive surgery planning. Patient-matched implants were a natural extension of this
work, leading to truly personalized implants that t one unique individual. Virtual planning of surgery and
guidance using 3D printed, personalized instruments have been applied to many areas of surgery including
total joint replacement and craniomaxillofacial reconstruction with great success. One example of this is the
bioresorbable trachial splint to treat newborns with tracheobronchomalacia developed at the University of
Michigan. The use of additive manufacturing for serialized production of orthopedic implants (metals) is also
increasing due to the ability to eciently create porous surface structures that facilitate osseointegration.
The hearing aid and dental industries are expected to be the biggest area of future development using the
custom 3D printing technology.
In March 2014, surgeons in Swansea used 3D printed parts to rebuild the face of a motorcyclist who had
been seriously injured in a road accident. In May 2018, 3D printing has been used for the kidney transplant
to save a three-year-old boy. As of 2012, 3D bio-printing technology has been studied by biotechnology rms
and academia for possible use in tissue engineering applications in which organs and body parts are built
using inkjet printing techniques. In this process, layers of living cells are deposited onto a gel medium or
sugar matrix and slowly built up to form three-dimensional structures including vascular systems. Recently,
a heart-on-chip has been created which matches properties of cells.
In 2018, 3D printing technology was used for the rst time to create a matrix for cell immobilization in
fermentation. Propionic acid production by Propionibacterium acidipropionici immobilized on 3D-printed
nylon beads was chosen as a model study. It was shown that those 3D-printed beads were capable to promote
high density cell attachment and propionic acid production, which could be adapted to other fermentation
bioprocesses.
17
In 2005, academic journals had begun to report on the possible artistic applications of 3D printing technology.
As of 2017, domestic 3D printing was reaching a consumer audience beyond hobbyists and enthusiasts.
O the shelf machines were increasingly capable of producing practical household applications, for example,
ornamental objects. Some practical examples include a working clock and gears printed for home woodworking
machines among other purposes. Web sites associated with home 3D printing tended to include
backscratchers, coat hooks, door knobs, etc.
3D printing, and open source 3D printers in particular, are the latest technology making inroads into the
classroom. Some authors have claimed that 3D printers oer an unprecedented \revolution" in STEM
education. The evidence for such claims comes from both the low cost ability for rapid prototyping in
the classroom by students, but also the fabrication of low-cost high-quality scientic equipment from open
hardware designs forming open-source labs. Future applications for 3D printing might include creating
open-source scientic equipment.
In the last several years 3D printing has been intensively used by in the cultural heritage eld for preservation,
restoration and dissemination purposes. Many Europeans and North American Museums have purchased 3D
printers and actively recreate missing pieces of their relics. The Metropolitan Museum of Art and the British
Museum have started using their 3D printers to create museum souvenirs that are available in the museum
shops. Other museums, like the National Museum of Military History and Varna Historical Museum, have
gone further and sell through the online platform Threeding digital models of their artifacts, created using
Artec 3D scanners, in 3D printing friendly le format, which everyone can 3D print at home.
3D printed soft actuators is a growing application of 3D printing technology which has found its place in
the 3D printing applications. These soft actuators are being developed to deal with soft structures and
organs especially in biomedical sectors and where the interaction between human and robot is inevitable.
The majority of the existing soft actuators are fabricated by conventional methods that require manual
fabrication of devices, post processing/assembly, and lengthy iterations until maturity in the fabrication is
achieved. To avoid the tedious and time-consuming aspects of the current fabrication processes, researchers
are exploring an appropriate manufacturing approach for eective fabrication of soft actuators. Thus, 3D
printed soft actuators are introduced to revolutionize the design and fabrication of soft actuators with
custom geometrical, functional, and control properties in a faster and inexpensive approach. They also
enable incorporation of all actuator components into a single structure eliminating the need to use external
joints, adhesives, and fasteners.
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-prot distribution, or for sale.
Any of the mentioned legal regimes may prohibit the distribution of the designs used in 3D printing, or
the distribution or sale of the printed item. To be allowed to do these things, where an active intellectual
property was involved, a person would have to contact the owner and ask for a license, which may come with
conditions and a price. However, many patent, design and copyright laws contain a standard limitation or
exception for `private', `non-commercial' use of inventions, designs or works of art protected under intellectual
property (IP). That standard limitation or exception may leave such private, non-commercial uses outside
the scope of IP rights.
Patents cover inventions including processes, machines, manufactures, and compositions of matter and have a
nite duration which varies between countries, but generally 20 years from the date of application. Therefore,
if a type of wheel is patented, printing, using, or selling such a wheel could be an infringement of the patent.
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Copyright covers an expression in a tangible, xed medium and often lasts for the life of the author plus
70 years thereafter. If someone makes a statue, they may have copyright on the look of that statue, so if
someone sees that statue, they cannot then distribute designs to print an identical or similar statue.
When a feature has both artistic (copyrightable) and functional (patentable) merits, when the question has
appeared in US court, the courts have often held the feature is not copyrightable unless it can be separated
from the functional aspects of the item. In other countries the law and the courts may apply a dierent
approach allowing, for example, the design of a useful device to be registered (as a whole) as an industrial
design on the understanding that, in case of unauthorized copying, only the non-functional features may be
claimed under design law whereas any technical features could only be claimed if covered by a valid patent.
Gun legislation and administration
The US Department of Homeland Security and the Joint Regional Intelligence Center released a memo
stating that \signicant advances in three-dimensional (3D) printing capabilities, availability of free digital
3D printable les for rearms components, and diculty regulating le sharing may present public safety risks
from unqualied gun seekers who obtain or manufacture 3D printed guns" and that \proposed legislation
to ban 3D printing of weapons may deter, but cannot completely prevent, their production. Even if the
practice is prohibited by new legislation, online distribution of these 3D printable les will be as dicult to
control as any other illegally traded music, movie or software les."
Attempting to restrict the distribution of gun plans via the Internet has been likened to the futility of
preventing the widespread distribution of DeCSS, which enabled DVD ripping. After the US government
had Defense Distributed take down the plans, they were still widely available via the Pirate Bay and other
le sharing sites. Downloads of the plans from the UK, Germany, Spain, and Brazil were heavy. Some
US legislators have proposed regulations on 3D printers to prevent them from being used for printing guns.
3D printing advocates have suggested that such regulations would be futile, could cripple the 3D printing
industry, and could infringe on free speech rights, with early pioneer of 3D printing Professor Hod Lipson
suggesting that gunpowder could be controlled instead.
Internationally, where gun controls are generally stricter than in the United States, some commentators have
said the impact may be more strongly felt since alternative rearms are not as easily obtainable. Ocials in
the United Kingdom have noted that producing a 3D printed gun would be illegal under their gun control
laws. Europol stated that criminals have access to other sources of weapons but noted that as technology
improves, the risks of an eect would increase.
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 re
ects 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
19
concerns involve gas and material exposures, in particular nanomaterials, material handling, static electricity,
moving parts and pressures.
A National Institute for Occupational Safety and Health (NIOSH) study noted particle emissions from a
fused lament peaked a few minutes after printing started and returned to baseline levels 100 minutes after
printing ended. Emissions from fused lament printers can include a large number of ultrane particles and
volatile organic compounds (VOCs).
The toxicity from emissions varies by source material due to dierences in size, chemical properties, and
quantity of emitted particles. Excessive exposure to VOCs can lead to irritation of the eyes, nose, and
throat, headache, loss of coordination, and nausea and some of the chemical emissions of fused lament
printers have also been linked to asthma. Based on animal studies, carbon nanotubes and carbon nanobers
sometimes used in fused lament printing can cause pulmonary eects including in
ammation, granulomas,
and pulmonary brosis when at the nanoparticle size.
As of March 2018, the US Government has set 3D printer emission standards for only a limited number
of compounds. Furthermore, the few established standards address factory conditions, not home or other
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 lament printing. Other general health and safety concerns include the hot surface of UV
lamps and print head blocks, high voltage, ultraviolet radiation from UV lamps, and potential for mechanical
injury from moving parts.
The problems noted in the NIOSH report were reduced by using manufacturer-supplied covers and full
enclosures, using proper ventilation, keeping workers away from the printer, using respirators, turning o
the printer if it jammed, and using lower emission printers and laments. At least one case of severe
injury was noted from an explosion involved in metal powders used for fused lament. Personal protective
equipment has been found to be the least desirable control method with a recommendation that it only be
used to add further protection in combination with approved emissions protection.
Hazards to health and safety also exist from post-processing activities done to nish parts after they have been
printed. These post-processing activities can include chemical baths, sanding, polishing, or vapor exposure
to rene surface nish, as well as general subtractive manufacturing techniques such as drilling, milling, or
turning to modify the printed geometry. Any technique that removes material from the printed part has the
potential to generate particles that can be inhaled or cause eye injury if proper personal protective equipment
is not used, such as respirators or safety glasses. Caustic baths are often used to dissolve support material
used by some 3D printers that allows them to print more complex shapes. These baths require personal
protective equipment to prevent injury to exposed skin.
Although no occupational exposure limits specic to 3D printer emissions exist, certain source materials used
in 3D printing, such as carbon nanober and carbon nanotubes, have established occupational exposure limits
at the nanoparticle size.
Since 3-D imaging creates items by fusing materials together, there runs the risk of layer separation in some
devices made using 3-D Imaging. For example, in January 2013, the US medical device company, DePuy,
recalled their knee and hip replacement systems. The devices were made from layers of metal, and shavings
had come loose { potentially harming the patient.
Impact
Additive manufacturing, starting with today's infancy period, requires manufacturing rms to be
exible,
ever-improving users of all available technologies to remain competitive. Advocates of additive manufacturing
also predict that this arc of technological development will counter globalization, as end users will do much
20
of their own manufacturing rather than engage in trade to buy products from other people and corporations.
The real integration of the newer additive technologies into commercial production, however, is more a
matter of complementing traditional subtractive methods rather than displacing them entirely.
The futurologist Jeremy Rifkin claimed that 3D printing signals the beginning of a third industrial revolution,
succeeding the production line assembly that dominated manufacturing starting in the late 19th century.
Since the 1950s, a number of writers and social commentators have speculated in some depth about the social
and cultural changes that might result from the advent of commercially aordable additive manufacturing
technology. Amongst the more notable ideas to have emerged from these inquiries has been the suggestion
that, as more and more 3D printers start to enter people's homes, the conventional relationship between the
home and the workplace might get further eroded. Likewise, it has also been suggested that, as it becomes
easier for businesses to transmit designs for new objects around the globe, so the need for high-speed freight
services might also become less. Finally, given the ease with which certain objects can now be replicated, it
remains to be seen whether changes will be made to current copyright legislation so as to protect intellectual
property rights with the new technology widely available.
As 3D printers became more accessible to consumers, online social platforms have developed to support
the community. This includes websites that allow users to access information such as how to build a 3D
printer, as well as social forums that discuss how to improve 3D print quality and discuss 3D printing news,
as well as social media websites that are dedicated to share 3D models. RepRap is a wiki based website
that was created to hold all information on 3d printing, and has developed into a community that aims
to bring 3D printing to everyone. Furthermore, there are other sites such as Pinshape, Thingiverse and
MyMiniFactory, which were created initially to allow users to post 3D les for anyone to print, allowing for
decreased transaction cost of sharing 3D les. These websites have allowed greater social interaction between
users, creating communities dedicated to 3D printing.
Some call attention to the conjunction of Commons-based peer production with 3D printing and other lowcost
manufacturing techniques. The self-reinforced fantasy of a system of eternal growth can be overcome
with the development of economies of scope, and here, society can play an important role contributing
to the raising of the whole productive structure to a higher plateau of more sustainable and customized
productivity. Further, it is true that many issues, problems, and threats arise due to the democratization
of the means of production, and especially regarding the physical ones. For instance, the recyclability of
advanced nanomaterials is still questioned; weapons manufacturing could become easier; not to mention
the implications for counterfeiting and on IP. It might be maintained that in contrast to the industrial
paradigm whose competitive dynamics were about economies of scale, Commons-based peer production 3D
printing could develop economies of scope. While the advantages of scale rest on cheap global transportation,
the economies of scope share infrastructure costs (intangible and tangible productive resources), taking
advantage of the capabilities of the fabrication tools. And following Neil Gershenfeld in that \some of
the least developed parts of the world need some of the most advanced technologies," Commons-based peer
production and 3D printing may oer the necessary tools for thinking globally but acting locally in response
to certain needs.
Larry Summers wrote about the \devastating consequences" of 3D printing and other technologies (robots,
articial 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 articial intelligence
to replace white-collar as well as blue-collar work will increase rapidly in the years ahead." Summers
recommends more vigorous cooperative eorts to address the \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 dicult 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 prot-sharing schemes that
may benet workers and give them a stake in wealth accumulation, strengthening of collective bargaining
21
arrangements, improvements in corporate governance, strengthening of nancial regulation to eliminate
subsidies to nancial activity, easing of land-use restrictions that may cause the real estate of the rich to
keep rising in value, better training for young people and retraining for displaced workers, and increased
public and private investment in infrastructure development|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
xed costs of design and testing, the incentives to invest [in digital technologies] are compelling."
Spence believes that, unlike prior digital technologies, which drove rms to deploy underutilized pools of
valuable labor around the world, the motivating force in the current wave of digital technologies \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 eective, and that re-localization will appear
in both developed and developing countries. In his view, production will not disappear, but it will be
less labor-intensive, and all countries will eventually need to rebuild their growth models around digital
technologies and the human capital supporting their deployment and expansion. Spence writes that \the
world we are entering is one in which the most powerful global
ows will be ideas and digital capital, not
goods, services, and traditional capital. Adapting to this will require shifts in mindsets, policies, investments
(especially in human capital), and quite possibly models of employment and distribution."
Naomi Wu regards the usage of 3D printing in the Chinese classroom (where rote memorization is standard)
to teach design principles and creativity as the most exciting recent development of the technology, and more
generally regards 3D printing as being the next desktop publishing revolution.
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 benets include reduced costs, increased safety, increased mobility, increased customer satisfaction
and reduced crime. Safety benets include a reduction in trac collisions, resulting injuries and related costs,
including for insurance. Automated cars are predicted to increase trac
ow; provide enhanced mobility for
children, the elderly, disabled, and the poor; relieve travelers from driving and navigation chores; lower fuel
consumption; signicantly reduce needs for parking space; reduce crime; and facilitate business models for
transportation as a service, especially via the sharing economy.
Problems include safety, technology, liability, desire by individuals to control their cars, legal framework
22
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.
Figure 7: Waymo Chrysler Pacica 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 rst truly automated car was developed in 1977, by Japan's Tsukuba Mechanical Engineering Laboratory.
The vehicle tracked white street markers, which were interpreted by two cameras on the vehicle, using
an analog computer for signal processing. The vehicle reached speeds up to 30 kilometres per hour (19 mph),
with the support of an elevated rail.
Autonomous prototype cars appeared in the 1980s, with Carnegie Mellon University's Navlab and ALV
projects funded by DARPA starting in 1984 and Mercedes-Benz and Bundeswehr University Munich's EUREKA
Prometheus Project in 1987. By 1985, the ALV had demonstrated self-driving speeds on two-lane
roads of 31 kilometers per hour (19 mph) with obstacle avoidance added in 1986 and o-road driving in
day and nighttime conditions by 1987. From the 1960s through the second DARPA Grand Challenge in
2005, automated vehicle research in the U.S. was primarily funded by DARPA, the US Army and the U.S.
Navy yielding incremental advances in speeds, driving competence in more complex conditions, controls and
sensor systems. Companies and research organizations have developed prototypes.
The U.S. allocated $650 million in 1991 for research on the National Automated Highway System, which
demonstrated automated driving through a combination of automation, embedded in the highway with
automated technology in vehicles and cooperative networking between the vehicles and with the highway
infrastructure. The program concluded with a successful demonstration in 1997 but without clear direction
or funding to implement the system on a larger scale. Partly funded by the National Automated Highway
System and DARPA, the Carnegie Mellon University Navlab drove 4,584 kilometers (2,848 mi) across
America in 1995, 4,501 kilometers (2,797 mi) or 98% of it autonomously. Navlab's record achievement stood
unmatched for two decades until 2015 when Delphi improved it by piloting an Audi, augmented with Delphi
technology, over 5,472 kilometers (3,400 mi) through 15 states while remaining in self-driving mode 99%
23
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 rst production car to reach level 3 automated driving,
and Audi would be the rst manufacturer to use laser scanners in addition to cameras and ultrasonic sensors
for their system.
In November 2017, Waymo announced that it had begun testing driverless cars without a safety driver in the
driver position; however, there is still an employee in the car. In July 2018, Waymo announced that its test
vehicles had traveled in automated mode for over 8,000,000 miles (13,000,000 km), increasing by 1,000,000
miles (1,600,000 kilometers) per month (Fig. 7).
Terminology
There is some inconsistency in terminology used in the self-driving car industry. Various organizations have
proposed to dene an accurate and consistent vocabulary. Such confusion has been documented in SAE J3016
which states that \Some vernacular usages associate autonomous specically with full driving automation
(level 5), while other usages apply it to all levels of driving automation, and some state legislation has dened
it to correspond approximately to any ADS at or above level 3 (or to any vehicle equipped with such an
ADS)."
Words denition and safety considerations
Modern vehicles provide partly automated features such as keeping the car within its lane, speed controls
or emergency braking. Nonetheless, dierences remain between a fully autonomous self-driving car on one
hand and driver assistance technologies on the other hand. According to the BBC, confusion between those
concepts leads to deaths.
Association of British Insurers considers the usage of the word autonomous in marketing for modern cars to
be dangerous, because car ads make motorists think `autonomous' and `autopilot' means a vehicle can drive
itself, when they still rely on the driver to ensure safety. Technology alone still is not able to drive the car.
When some car makers suggest or claim vehicles are self-driving, when they are only partly automated,
drivers risk becoming excessively condent, leading to crashes, while fully self-driving cars are still a long
way o in the UK.
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 articial aids in their environment, such as magnetic
strips. Autonomous control implies satisfactory performance under signicant uncertainties in the environment
and the ability to compensate for system failures without external intervention.
One approach is to implement communication networks both in the immediate vicinity (for collision avoidance)
and farther away (for congestion management). Such outside in
uences in the decision process reduce
an individual vehicle's autonomy, while still not requiring human intervention.
Wood et al. (2012) wrote, \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
24
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 denes 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-suciently, 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 denition 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 denition is: Self-driving vehicles are cars or trucks in which human drivers are never required
to take control to safely operate the vehicle. Also known as autonomous or \driverless" cars, they combine
sensors and software to control, navigate, and drive the vehicle. Currently, there are no legally operating,
fully-autonomous vehicles in the United States.
NHTSA denition is: These self-driving vehicles ultimately will integrate onto U.S. roadways by progressing
through six levels of driver assistance technology advancements in the coming years. This includes everything
from no automation (where a fully engaged driver is required at all times), to full autonomy (where an
automated vehicle operates independently, without a human driver).
Classication
A classication system based on six dierent levels (ranging from fully manual to fully automated systems)
was published in 2014 by SAE International, an automotive standardization body, as J3016, Taxonomy and
Denitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. This classication
system is based on the amount of driver intervention and attentiveness required, rather than the vehicle
capabilities, although these are very loosely related. In the United States in 2013, the National Highway
Trac Safety Administration (NHTSA) released a formal classication system, but abandoned this system
in favor of the SAE standard in 2016. Also in 2016, SAE updated its classication, called J3016 201609.
Levels of driving automation
In SAE's automation level denitions, \driving mode" means \a type of driving scenario with characteristic
dynamic driving task requirements (e.g., expressway merging, high speed cruising, low speed trac jam,
closed-campus operations, etc.)"
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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 o"): The automated system takes full control of the vehicle (accelerating, braking, and
steering). The driver must monitor the driving and be prepared to intervene immediately at any time if the
automated system fails to respond properly. The shorthand \hands o" is not meant to be taken literally. In
fact, contact between hand and wheel is often mandatory during SAE 2 driving, to conrm that the driver
is ready to intervene.
Level 3 (\eyes o"): The driver can safely turn their attention away from the driving tasks, e.g. the driver
can text or watch a movie. The vehicle will handle situations that call for an immediate response, like
emergency braking. The driver must still be prepared to intervene within some limited time, specied by the
manufacturer, when called upon by the vehicle to do so. As an example, the 2018 Audi A8 Luxury Sedan
was the rst commercial car to claim to be capable of level 3 self-driving. This particular car has a so-called
Trac Jam Pilot. When activated by the human driver, the car takes full control of all aspects of driving in
slow-moving trac at up to 60 kilometers per hour (37 mph). The function works only on highways with a
physical barrier separating one stream of trac from oncoming trac.
Level 4 (\mind o"): As level 3, but no driver attention is ever required for safety, i.e. the driver may safely
go to sleep or leave the driver's seat. Self-driving is supported only in limited spatial areas (geofenced) or
under special circumstances, like trac jams. Outside of these areas or circumstances, the vehicle must be
able to safely abort the trip, i.e. park the car, if the driver does not retake control.
Level 5 (\steering wheel optional"): No human intervention is required at all. An example would be a robotic
taxi.
In the formal SAE denition below, note in particular what happens in the shift from SAE 2 to SAE 3: the
human driver no longer has to monitor the environment. This is the nal aspect of the \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 denition
In the district of Columbia (DC) code, \Autonomous vehicle" means a vehicle capable of navigating
District roadways and interpreting trac-control devices without a driver actively operating any of the
vehicle's control systems. The term \autonomous vehicle" excludes a motor vehicle enabled with active
safety systems or driver- assistance systems, including systems to provide electronic blind-spot assistance,
crash avoidance, emergency braking, parking assistance, adaptive cruise control, lane-keep assistance, lanedeparture
warning, or trac-jam and queuing assistance, unless the system alone or in combination with
other systems enables the vehicle on which the technology is installed to drive without active control or
monitoring by a human operator.
In the same district code, it is considered that:
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;
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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 trac laws and motor vehicle laws
and trac 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 o-line map into current location estimates and map updates. Waymo has developed a variant
of SLAM with detection and tracking of other moving objects (DATMO), which also handles obstacles such
as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) technologies
to aid localization. Typical sensors include Lidar, stereo vision, GPS and IMU. Udacity is developing an
open-source software stack. Control systems on automated cars may use Sensor Fusion, which is an approach
that integrates information from a variety of sensors on the car to produce a more consistent, accurate, and
useful view of the environment.
Driverless vehicles require some form of machine vision for the purpose of visual object recognition. Automated
cars are being developed with deep neural networks, a type of deep learning architecture with many
computational stages, or levels, in which neurons are simulated from the environment that activate the network.
The neural network depends on an extensive amount of data extracted from real-life driving scenarios,
enabling the neural network to \learn" how to execute the best course of action.
In May 2018, researchers from MIT announced that they had built an automated car that can navigate
unmapped roads. Researchers at their Computer Science and Articial Intelligence Laboratory (CSAIL)
have developed a new system, called MapLite, which allows self-driving cars to drive on roads that they
have never been on before, without using 3D maps. The system combines the GPS position of the vehicle, a
\sparse topological map" such as OpenStreetMap, (i.e. having 2D features of the roads only), and a series
of sensors that observe the road conditions.
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 specic set of operating principles)
or virtually, i.e. in computer simulations. When driven on public roads, automated vehicles require a person
to monitor their proper operation and \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 o, typically by a human driver. In 2017, Waymo
reported 63 disengagements over 352,545 miles (567,366 km) of testing, or 5,596 miles (9,006 km) on average,
the highest among companies reporting such gures. Waymo also traveled more distance in total than any
other. Their 2017 rate of 0.18 disengagements per 1,000 miles (1,600 km) was an improvement from 0.2
disengagements per 1,000 miles (1,600 km) in 2016 and 0.8 in 2015. In March, 2017, Uber reported an
average of 0.67 miles (1.08 km) per disengagement. In the nal three months of 2017, Cruise Automation
(now owned by GM) averaged 5,224 miles (8,407 km) per disruption over 62,689 miles (100,888 km). In July
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2018, the rst electric driverless racing car \Robocar" completed 1.8 kilometers track, using its navigation
system and articial intelligence (Table 1).
Maker Distance between disengagements Distance
BMW 638 miles (1027 km) 638 miles (1027 km)
Bosch 0.68 miles (1.09 km) 983 miles (1582 km)
Delphi Automotive Systems 14.9 miles (24.0 km) 2658 miles (4278 km)
Ford 196.6 miles (316.4 km) 590 miles (950 km)
General Motors 54.7 miles (88.0 km) 8156 miles (13126 km)
Mercedes Benz 2 miles (3.2 km) 673 miles (1083 km)
Nissan 263.3 miles (423.7 km) 6056 miles (9746 km)
Tesla 2.9 miles (4.7 km) 550 miles (890 km)
Waymo 5127.9 miles (8252.6 km) 635868 miles (1023330 km)
Table 1: Self-driving car testing results.
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
rst player in the self-driving truck game to drive in fully automated mode on a public road without a person
in the cab. In Europe, the truck Platooning is considered with the Safe Road Trains for the Environment
approach.Vehicular automation also covers other kinds of vehicles such as Buses, Trains, Trucks. Lockheed
Martin with funding from the U.S. Army developed an automated truck convoying system that uses a lead
truck operated by a human driver with a number of trucks following autonomously. Developed as part of
the Army's Autonomous Mobility Applique System (AMAS), the system consists of an automated driving
package that has been installed on more than nine types of vehicles and has completed more than 55,000
hours of driving at speeds up to 64 kilometres per hour (40 mph) as of 2014. As of 2017 the Army was
planning to eld 100-200 trucks as part of a rapid-elding program.
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 trac. In 2015,
the UK launched public trials of the LUTZ Pathnder automated pod in Milton Keynes. Beginning in
summer 2015, the French government allowed PSA Peugeot-Citroen to make trials in real conditions in the
Paris area. The experiments were planned to be extended to other cities such as Bordeaux and Strasbourg
by 2016. The alliance between French companies THALES and Valeo (provider of the rst self-parking car
system that equips Audi and Mercedes premi) is testing its own system. New Zealand is planning to use
automated vehicles for public transport in Tauranga and Christchurch.
In China, Baidu and King Long produce automated minibus, a vehicle with 14 seats, but without driving
seat. With 100 vehicles produced, 2018 will be the rst year with commercial automated service in China.
Those minibuses should be at level 4, that is driverless in closed roads.
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Potential advantages
Safety
Driving safety experts predict that once driverless technology has been fully developed, trac collisions (and
resulting deaths and injuries and costs), caused by human error, such as delayed reaction time, tailgating,
rubbernecking, and other forms of distracted or aggressive driving should be substantially reduced. Consulting
rm McKinsey & Company estimated that widespread use of autonomous vehicles could \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 benets asserted by proponents will manifest in practice cannot be assessed. Confounding factors that
could reduce the net eect on safety may include unexpected interactions between humans and partly or
fully automated vehicles, or between dierent types of vehicle system; complications at the boundaries of
functionality at each automation level (such as handover when the vehicle reaches the limit of its capacity);
the eect of the bugs and
aws that inevitably occur in complex interdependent software systems; sensor or
data shortcomings; and successful compromise by malicious interveners.
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
exibility. Large vehicles, such as motorhomes, would attain appreciably enhanced
ease of use.
Trac
Additional advantages could include higher speed limits; smoother rides; and increased roadway capacity; and
minimized trac congestion, due to decreased need for safety gaps and higher speeds. Currently, maximum
controlled-access highway throughput or capacity according to the U.S. Highway Capacity Manual is about
2,200 passenger vehicles per hour per lane, with about 5% of the available road space is taken up by cars. One
study estimated that automated cars could increase capacity by 273% (~8,200 cars per hour per lane). The
study also estimated that with 100% connected vehicles using vehicle-to-vehicle communication, capacity
could reach 12,000 passenger vehicles per hour (up 445% from 2,200 pc/h per lane) traveling safely at 120
km/h (75 mph) with a following gap of about 6 m (20 ft) of each other. Currently, at highway speeds drivers
keep between 40 to 50 m (130 to 160 ft) away from the car in front. These increases in highway capacity
could have a signicant impact in trac congestion, particularly in urban areas, and even eectively end
highway congestion in some places. The ability for authorities to manage trac
ow would increase, given
the extra data and driving behavior predictability combined with less need for trac police and even road
signage.
Lower costs
Safer driving is expected to reduce the costs of vehicle insurance. Reduced trac congestion and the
improvements in trac
ow due to widespread use of automated cars will also translate into better fuel
eciency. Additionally, self-driving cars will be able to accelerate and brake more eciently, meaning higher
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fuel economy from reducing wasted energy typically associated with inecient changes to speed (energy
typically lost due to friction, in the form of heat and sound).
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 trac
ow, could free up
tremendous amounts of land in urban areas, which could then be used for parks, recreational areas, buildings,
among other uses; making cities more livable.
Related eects
By reducing the (labor and other) cost of mobility as a service, automated cars could reduce the number of
cars that are individually owned, replaced by taxi/pooling and other car sharing services. This would also
dramatically reduce the size of the automotive production industry, with corresponding environmental and
economic eects. Assuming the increased eciency is not fully oset by increases in demand, more ecient
trac
ow could free roadway space for other uses such as better support for pedestrians and cyclists.
The vehicles' increased awareness could aid the police by reporting on illegal passenger behavior, while
possibly enabling other crimes, such as deliberately crashing into another vehicle or a pedestrian. However,
this may also lead to much expanded mass surveillance if there is wide access granted to third parties to the
large data sets generated. The future of passenger rail transport in the era of automated cars is not clear.
Potential limits or obstacles
The sort of hoped-for potential benets from increased vehicle automation described may be limited by
foreseeable challenges, such as disputes over liability (will each company operating a vehicle accept that
they are its \driver" and thus responsible for what their car does, or will some try to project this liability
onto others who are not in control?), the time needed to turn over the existing stock of vehicles from nonautomated
to automated, and thus a long period of humans and autonomous vehicles sharing the roads,
resistance by individuals to having to forfeit control of their cars, concerns about the safety of driverless in
practice, and the implementation of a legal framework and consistent global government regulations for selfdriving
cars. Other obstacles could include de-skilling and lower levels of driver experience for dealing with
potentially dangerous situations and anomalies, ethical problems where an automated vehicle's software
is forced during an unavoidable crash to choose between multiple harmful courses of action ('the trolley
problem'), concerns about making large numbers of people currently employed as drivers unemployed (at the
same time as many other alternate blue collar occupations may be undermined by automation), the potential
for more intrusive mass surveillance of location, association and travel as a result of police and intelligence
agency access to large data sets generated by sensors and pattern-recognition AI (making anonymous travel
dicult), and possibly insucient understanding of verbal sounds, gestures and non-verbal cues by police,
other drivers or pedestrians.
Possible technological obstacles for automated cars are:
• articial 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 dierent types of weather (such as snow)
or deliberate interference, including jamming and spoong,
• avoidance of large animals requires recognition and tracking, and Volvo found that software suited to
caribou, deer, and elk was ineective with kangaroos,
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• 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,
• eld 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 dierences 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 suer as the technology makes certain aspects of these occupations
obsolete. A frequently cited paper by Michael Osborne and Carl Benedikt Frey found that automated cars
would make many jobs redundant.
Privacy could be an issue when having the vehicle's location and position integrated into an interface in
which other people have access to. In addition, there is the risk of automotive hacking through the sharing
of information through V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols. There is also
the risk of terrorist attacks. Self-driving cars could potentially be loaded with explosives and used as bombs.
The lack of stressful driving, more productive time during the trip, and the potential savings in travel time
and cost could become an incentive to live far away from cities, where land is cheaper, and work in the
city's core, thus increasing travel distances and inducing more urban sprawl, more fuel consumption and an
increase in the carbon footprint of urban travel. There is also the risk that trac congestion might increase,
rather than decrease. Appropriate public policies and regulations, such as zoning, pricing, and urban design
are required to avoid the negative impacts of increased suburbanization and longer distance travel.
Some believe that once automation in vehicles reaches higher levels and becomes reliable, drivers will pay
less attention to the road. Research shows that drivers in automated cars react later when they have to
intervene in a critical situation, compared to if they were driving manually. Depending on the capabilities
of automated vehicles and the frequency with which human intervention is needed, this may counteract any
increase in safety, as compared to all-human driving, that may be delivered by other factors.
Ethical and moral reasoning come into consideration when programming the software that decides what
action the car takes in an unavoidable crash; whether the automated car will crash into a bus, potentially
killing people inside; or swerve elsewhere, potentially killing its own passengers or nearby pedestrians. A
question that programmers of AI systems nd dicult to answer (as do ordinary people, and ethicists) is
\what decision should the car make that causes the `smallest' damage to people's lives?"
The ethics of automated vehicles are still being articulated, and may lead to controversy. They may also
require closer consideration of the variability, context-dependency, complexity and non-deterministic nature
of human ethics. Dierent human drivers make various ethical decisions when driving, such as avoiding harm
to themselves, or putting themselves at risk to protect others. These decisions range from rare extremes
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such as self-sacrice or criminal negligence, to routine decisions good enough to keep the trac
owing but
bad enough to cause accidents, road rage and stress.
Human thought and reaction time may sometimes be too slow to detect the risk of an upcoming fatal crash,
think through the ethical implications of the available options, or take an action to implement an ethical
choice. Whether a particular automated vehicle's capacity to correctly detect an upcoming risk, analyze
the options or choose a `good' option from among bad choices would be as good or better than a particular
human's may be dicult to predict or assess. This diculty may be in part because the level of automated
vehicle system understanding of the ethical issues at play in a given road scenario, sensed for an instant from
out of a continuous stream of synthetic physical predictions of the near future, and dependent on layers of
pattern recognition and situational intelligence, may be opaque to human inspection because of its origins
in probabilistic machine learning rather than a simple, plain English `human values' logic of parsable rules.
The depth of understanding, predictive power and ethical sophistication needed will be hard to implement,
and even harder to test or assess.
The scale of this challenge may have other eects. There may be few entities able to marshal the resources
and AI capacity necessary to meet it, as well as the capital necessary to take an automated vehicle system to
market and sustain it operationally for the life of a vehicle, and the legal and `government aairs' capacity
to deal with the potential for liability for a signicant proportion of trac accidents. This may have the
eect of narrowing the number of dierent system operators, and eroding the presently quite diverse global
vehicle market down to a small number of system suppliers.
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References
Brian Wang. Uber' self-driving system was still 400 times worse [than] Waymo in 2018 on key distance
intervention metric. NextBigFuture.com, 2018.
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