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.
Technology
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.
UC SUSA
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,
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
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
Pprovider 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.
Introduction Technology Readiness Levels (TRLs) are a systematic metric/measurement system that supports assessments of the maturity of a particular technology and the consistent comparison of maturity between different types of technology. The TRL approach has been used on-and-off in NASA space technology planning for many years and was recently incorporated in the NASA Management Instruction (NMI 7100) addressing integrated technology planning at NASA. Figure 1 (attached) provides a summary view of the technology maturation process model for
NASA space activities
for which the TRL’s were originally conceived; other process models may be used. However, to be most useful the general model must include: (a) ‘basic’ research in new technologies and concepts (targeting identified goals, but not necessary specific systems), (b) focused technology development addressing specific technologies for one or more potential identified applications, (c) technology development and demonstration for each specific application before the beginning of full system development of that application, (d) system development (through first unit fabrication), and (e) system ‘launch’ and operations. Technology Readiness Levels Summary TRL 1 Basic principles observed and reported TRL 2 Technology concept and/or application formulated TRL 3 Analytical and experimental critical function and/or characteristic proof-ofconcept TRL 4 Component and/or breadboard validation in laboratory environment TRL 5 Component and/or breadboard validation in relevant environment TRL 6 System/subsystem model or prototype demonstration in a relevant environment (ground or space) TRL 7 System prototype demonstration in a space environment TRL 8 Actual system completed and “flight qualified” through test and demonstration (ground or space) TRL 9 Actual system “flight proven” through successful mission operations 028 Discussion of Each Level The following paragraphs provide a descriptive discussion of each technology readiness level, including an example of the type of activities that would characterize each TRL. TRL 1 Basic principles observed and reported This is the lowest “level” of technology maturation. At this level, scientific research begins to be translated into applied research and development. Examples might include studies of basic properties of materials (e.g., tensile strength as a function of temperature for a new fiber). Cost to Achieve: Very Low ‘Unique’ Cost (investment cost is borne by scientific research programs) TRL 2 Technology concept and/or application formulated Once basic physical principles are observed, then at the next level of maturation, practical applications of those characteristics can be ‘invented’ or identified. For example, following the observation of high critical temperature (Htc) superconductivity, potential applications of the new material for thin film devices (e.g., SIS mixers) and in instrument systems (e.g., telescope sensors) can be defined. At this level, the application is still speculative: there is not experimental proof or detailed analysis to support the conjecture. Cost to Achieve: Very Low ‘Unique’ Cost (investment cost is borne by scientific research programs) TRL 3 Analytical and experimental critical function and/or characteristic proof-of-concept At this step in the maturation process, active research and development (R&D) is initiated. This must include both analytical studies to set the technology into an appropriate context and laboratory-based studies to physically validate that the analytical predictions are correct. These studies and experiments should constitute “proof-of-concept” validation of the applications/concepts formulated at TRL 2. For example, a concept for High
Energy Density Matter (HEDM)
propulsion might depend on slush or super-cooled hydrogen as a propellant: TRL 3 might be attained when the concept-enabling phase/temperature/pressure for the fluid was achieved in a laboratory. Cost to Achieve: Low ‘Unique’ Cost (technology specific) 028 TRL 4 Component and/or breadboard validation in laboratory environment Following successful “proof-of-concept” work, basic technological elements must be integrated to establish that the “pieces” will work together to achieve concept-enabling levels of performance for a component and/or breadboard. This validation must devised to support the concept that was formulated earlier, and should also be consistent with the requirements of potential system applications. The validation is relatively “low-fidelity” compared to the eventual system: it could be composed of ad hoc discrete components in a laboratory. For example, a TRL 4 demonstration of a new ‘fuzzy logic’ approach to avionics might consist of testing the algorithms in a partially computer-based, partially bench-top component (e.g., fiber optic gyros) demonstration in a controls lab using simulated vehicle inputs. Cost to Achieve: Low-to-moderate ‘Unique’ Cost (investment will be technology specific, but probably several factors greater than investment required for TRL 3) TRL 5 Component and/or breadboard validation in relevant environment At this, the fidelity of the component and/or breadboard being tested has to increase significantly. The basic technological elements must be integrated with reasonably realistic supporting elements so that the total applications (component-level, sub-system level, or system-level) can be tested in a ‘simulated’ or somewhat realistic environment. From oneto-several new technologies might be involved in the demonstration. For example, a new type of solar photovoltaic material promising higher efficiencies would at this level be used in an actual fabricated solar array ‘blanket’ that would be integrated with power supplies, supporting structure, etc., and tested in a thermal vacuum chamber with solar simulation capability. Cost to Achieve: Moderate ‘Unique’ Cost (investment cost will be technology dependent, but likely to be several factors greater that cost to achieve TRL 4) TRL 6 System/subsystem model or prototype demonstration in a relevant environment (ground or space) A major step in the level of fidelity of the technology demonstration follows the completion of TRL 5. At TRL 6, a representative model or prototype system or system — which would go well beyond ad hoc, ‘patch-cord’ or discrete component level breadboarding — would be tested in a relevant environment. At this level, if the only ‘relevant environment’ is the environment of space, then the model/prototype must be demonstrated in space. Of 028 course, the demonstration should be successful to represent a true TRL 6. Not all technologies will undergo a TRL 6 demonstration: at this point the maturation step is driven more by assuring management confidence than by R&D requirements. The demonstration might represent an actual system application, or it might only be similar to the planned application, but using the same technologies. At this level, several-to-many new technologies might be integrated into the demonstration. For example, a innovative approach to high temperature/low mass radiators, involving liquid droplets and composite materials, would be demonstrated to TRL 6 by actually flying a working, sub-scale (but scaleable) model of the system on a Space Shuttle or International Space Station ‘pallet’. In this example, the reason space is the ‘relevant’ environment is that microgravity plus vacuum plus thermal environment effects will dictate the success/failure of the system — and the only way to validate the technology is in space. Cost to Achieve: Technology and demonstration specific; a fraction of TRL 7 if on ground; nearly the same if space is required TRL 7 System prototype demonstration in a space environment TRL 7 is a significant step beyond TRL 6, requiring an actual system prototype demonstration in a space environment. It has not always been implemented in the past. In this case, the prototype should be near or at the scale of the planned operational system and the demonstration must take place in space. The driving purposes for achieving this level of maturity are to assure system engineering and development management confidence (more than for purposes of technology R&D). Therefore, the demonstration must be of a prototype of that application. Not all technologies in all systems will go to this level. TRL 7 would normally only be performed in cases where the technology and/or subsystem application is mission critical and relatively high risk. Example: the Mars Pathfinder Rover is a TRL 7 technology demonstration for future Mars micro-rovers based on that system design. Example: X-vehicles are TRL 7, as are the demonstration projects planned in the New Millennium spacecraft program. Cost to Achieve: Technology and demonstration specific, but a significant fraction of the cost of TRL 8 (investment = “Phase C/D to TFU” for demonstration system) TRL 8 Actual system completed and “flight qualified” through test and demonstration (ground or space) By definition, all technologies being applied in actual systems go through TRL 8. In almost all cases, this level is the end of true ‘system development’ for most technology elements. Example: this would include DDT&E through Theoretical First Unit (TFU) for a new reusable launch vehicle. This might include integration of new technology into an existing system. Example: loading and testing successfully a new control algorithm into the onboard computer on Hubble Space Telescope while in orbit. Cost to Achieve: Mission specific; typically highest unique cost for a new technology (investment = “Phase C/D to TFU” for actual system) 028 TRL 9 Actual system “flight proven” through successful mission operations By definition, all technologies being applied in actual systems go through TRL 9. In almost all cases, the end of last ‘bug fixing’ aspects of true ‘system development’. For example, small fixes/changes to address problems found following launch (through ‘30 days’ or some related date). This might include integration of new technology into an existing system (such operating a new artificial intelligence tool into operational mission control at JSC). This TRL does not include planned product improvement of ongoing or reusable systems. For example, a new engine for an existing RLV would not start at TRL 9: such
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