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 (Fig. 7).
Terminology
There is some inconsistency in terminology used
in the self-driving car industry. Various organizations have proposed to
define an accurate and consistent vocabulary. Such confusion has been
documented in SAE J3016 which states that “Some vernacular usages
associate autonomous specifically with full driving automation (level
5), while other usages apply it to all levels of driving automation, and
some state legislation has defined it to correspond approximately to any
ADS at or above level 3 (or to any vehicle equipped with such an ADS).”
Words definition and safety considerations
Modern vehicles provide
partly automated features such as keeping the car within its lane, speed
controls or emergency braking. Nonetheless, differences remain between a
fully autonomous self-driving car on one hand and driver assistance
technologies on the other hand. According to the BBC, confusion between
those concepts leads to deaths. Association of British Insurers
considers the usage of the word autonomous in marketing for modern cars
to be dangerous, because car ads make motorists think ‘autonomous’ and
‘autopilot’ means a vehicle can drive itself, when they still rely on
the driver to ensure safety. Technology alone still is not able to drive
the car. When some car makers suggest or claim vehicles are
self-driving, when they are only partly automated, drivers risk becoming
excessively confident, leading to crashes, while fully self-driving cars
are still a long way off in the UK.
Autonomous vs. automated
Autonomous
means self-governing. Many historical projects related to vehicle
automation have been automated (made automatic) subject to a heavy
reliance on artificial aids in their environment, such as magnetic
strips. Autonomous control implies satisfactory performance under
significant uncertainties in the environment and the ability to
compensate for system failures without external intervention.
One
approach is to implement communication networks both in the immediate
vicinity (for collision avoidance) and farther away (for congestion
management). Such outside influences in the decision process reduce an
individual vehicle’s autonomy, while still not requiring human
intervention.
Wood et al. (2012) wrote, “This Article generally uses
the term ‘autonomous,’ instead of the term ‘automated.’ ” The term
“autonomous” was chosen “because it is the term that is currently in
more widespread use (and thus is more familiar to the general public).
However, the latter term is arguably more accurate. ‘Automated’ connotes
control or operation by a machine, while ‘autonomous’ connotes acting
alone or independently. Most of the vehicle concepts (that we are
currently aware of) have a person in the driver’s seat, utilize a
communication connection to the Cloud or other vehicles, and do not
independently select either destinations or routes for reaching them.
Thus, the term ‘automated’ would more accurately describe these vehicle
concepts.” As of 2017, most commercial projects focused on automated
vehicles that did not communicate with other vehicles or with an
enveloping management regime.
Put in the words of one Nissan engineer,
“A truly autonomous car would be one where you request it to take you
to work and it decides to go to the beach instead.”
EuroNCAP defines
autonomous in “Autonomous Emergency Braking” as: “the system acts
independently of the driver to avoid or mitigate the accident.” which
implies the autonomous system is not the driver.
Autonomous versus
cooperative
To make a car travel without any driver embedded within the
vehicle some system makers used a remote driver. But according to SAE
J3016, some driving automation systems may indeed be autonomous if they
perform all of their functions independently and self-sufficiently, but
if they depend on communication and/or cooperation with outside
entities, they should be considered cooperative rather than autonomous.
Self-driving car
Techemergence says.
“Self-driving” is a rather vague
term with a vague meaning
—?Techemergence
PC mag definition is: A
computer-controlled car that drives itself. Also called an
“autonomous
vehicle” and “driverless car,” self-driving cars date back to the
1939 New York World’s Fair when General Motors predicted the development
of self-driving, radio-controlled electric cars.
UCSUSA definition is:
Self-driving vehicles are cars or trucks in which human drivers are
never required to take control to safely operate the vehicle. Also known
as autonomous or “driverless” cars, they combine sensors and software
to control, navigate, and drive the vehicle. Currently, there are no
legally operating, fully-autonomous vehicles in the United States.
NHTSA
definition is: These self-driving vehicles ultimately will integrate
onto U.S. roadways by progressing through six levels of driver
assistance technology advancements in the coming years. This includes
everything from no automation (where a fully engaged driver is required
at all times), to full autonomy (where an automated vehicle operates
independently, without a human driver).
Classification
A classification
system based on six different levels (ranging from fully manual to fully
automated systems) was published in 2014 by SAE International, an
automotive standardization body, as J3016, Taxonomy and Definitions for
Terms Related to On-Road Motor Vehicle Automated Driving Systems. This
classification system is based on the amount of driver intervention and
attentiveness required, rather than the vehicle capabilities, although
these are very loosely related. In the United States in 2013, the
National Highway Traffic Safety Administration (NHTSA) released a formal
classification system, but abandoned this system in favor of the SAE
standard in 2016. Also in 2016, SAE updated its classification, called
J3016_201609. Levels of driving automation In SAE’s automation level
definitions, “driving mode” means “a type of driving scenario with
characteristic dynamic driving task requirements (e.g., expressway
merging, high speed cruising, low speed traffic jam, closed-campus
operations, etc.)” Level 0: Automated system issues warnings and may
momentarily intervene but has no sustained vehicle control. Level 1
(“hands on”): The driver and the automated system share control of the
vehicle. Examples are Adaptive Cruise Control (ACC), where the driver
controls steering and the automated system controls speed; and Parking
Assistance, where steering is automated while speed is under manual
control. The driver must be ready to retake full control at any time.
Lane Keeping Assistance (LKA) Type II is a further example of level 1
self-driving. Level 2 (“hands off”): The automated system takes full
control of the vehicle (accelerating, braking, and steering). The driver
must monitor the driving and be prepared to intervene immediately at any
time if the automated system fails to respond properly. The shorthand
“hands off” is not meant to be taken literally. In fact, contact
between hand and wheel is often mandatory during SAE 2 driving, to
confirm that the driver is ready to intervene. Level 3 (“eyes off”):
The driver can safely turn their attention away from the driving tasks,
e.g. the driver can text or watch a movie. The vehicle will handle
situations that call for an immediate response, like emergency braking.
The driver must still be prepared to intervene within some limited time,
specified by the manufacturer, when called upon by the vehicle to do so.
As an example, the 2018 Audi A8 Luxury Sedan was the first commercial
car to claim to be capable of level 3 self-driving. This particular car
has a so-called Traffic Jam Pilot. When activated by the human driver,
the car takes full control of all aspects of driving in slow-moving
traffic at up to 60 kilometers per hour (37 mph). The function works
only on highways with a physical barrier separating one stream of
traffic from oncoming traffic. Level 4 (“mind off”): As level 3, but
no driver attention is ever required for safety, i.e. the driver may
safely go to sleep or leave the driver’s seat. Self-driving is supported
only in limited spatial areas (geofenced) or under special
circumstances, like traffic jams. Outside of these areas or
circumstances, the vehicle must be able to safely abort the trip,
i.e. park the car, if the driver does not retake control. Level 5
(“steering wheel optional”): No human intervention is required at all.
An example would be a robotic taxi. In the formal SAE definition below,
note in particular what happens in the shift from SAE 2 to SAE 3: the
human driver no longer has to monitor the environment. This is the final
aspect of the “dynamic driving task” that is now passed over from the
human to the automated system. At SAE 3, the human driver still has the
responsibility to intervene when asked to do so by the automated system.
At SAE 4 the human driver is relieved of that responsibility and at SAE
5 the automated system will never need to ask for an intervention. Legal
definition In the district of Columbia (DC) code, “Autonomous vehicle”
means a vehicle capable of navigating District roadways and interpreting
traffic-control devices without a driver actively operating any of the
vehicle’s control systems. The term “autonomous vehicle” excludes a
motor vehicle enabled with active safety systems or driver- assistance
systems, including systems to provide electronic blind-spot assistance,
crash avoidance, emergency braking, parking assistance, adaptive cruise
control, lane-keep assistance, lane-departure warning, or traffic-jam
and queuing assistance, unless the system alone or in combination with
other systems enables the vehicle on which the technology is installed
to drive without active control or monitoring by a human operator. In
the same district code, it is considered that: An autonomous vehicle may
operate on a public roadway; provided, that the vehicle: (1) Has a
manual override feature that allows a driver to assume control of the
autonomous vehicle at any time; (2) Has a driver seated in the control
seat of the vehicle while in operation who is prepared to take control
of the autonomous vehicle at any moment; and (3) Is capable of operating
in compliance with the District’s applicable traffic laws and motor
vehicle laws and traffic control devices. Technical challenges The
challenge for driverless car designers is to produce control systems
capable of analyzing sensory data in order to provide accurate detection
of other vehicles and the road ahead. Modern self-driving cars generally
use Bayesian simultaneous localization and mapping (SLAM) algorithms,
which fuse data from multiple sensors and an off-line map into current
location estimates and map updates. Waymo has developed a variant of
SLAM with detection and tracking of other moving objects (DATMO), which
also handles obstacles such as cars and pedestrians. Simpler systems may
use roadside real-time locating system (RTLS) technologies to aid
localization. Typical sensors include Lidar, stereo vision, GPS and IMU.
Udacity is developing an open-source software stack. Control systems on
automated cars may use Sensor Fusion, which is an approach that
integrates information from a variety of sensors on the car to produce a
more consistent, accurate, and useful view of the environment.
Driverless vehicles require some form of machine vision for the purpose
of visual object recognition. Automated cars are being developed with
deep neural networks, a type of deep learning architecture with many
computational stages, or levels, in which neurons are simulated from the
environment that activate the network. The neural network depends on an
extensive amount of data extracted from real-life driving scenarios,
enabling the neural network to “learn” how to execute the best course
of action. In May 2018, researchers from MIT announced that they had
built an automated car that can navigate unmapped roads. Researchers at
their Computer Science and Artificial Intelligence Laboratory (CSAIL)
have developed a new system, called MapLite, which allows self-driving
cars to drive on roads that they have never been on before, without
using 3D maps. The system combines the GPS position of the vehicle, a
“sparse topological map” such as OpenStreetMap, (i.e. having 2D
features of the roads only), and a series of sensors that observe the
road conditions. Human factor challenges Alongside the many technical
challenges that autonomous cars face, there exist many human and social
factors that may impede upon the wider uptake of the technology. As
things become more automated, the human users need to have trust in the
automation, which can be a challenge in itself. Testing Testing vehicles
with varying degrees of automation can be done physically, in closed
environments, on public roads (where permitted, typically with a license
or permit or adhering to a specific set of operating principles) or
virtually, i.e. in computer simulations. When driven on public roads,
automated vehicles require a person to monitor their proper operation
and “take over” when needed. Apple is currently testing self-driven
cars, and has increased the number of test vehicles from 3 to 27 in
January 2018, and to 45 in March 2018. One way to assess the progress of
automated vehicles is to compute the average distance driven between
“disengagements”, when the automated system is turned off, typically
by a human driver. In 2017, Waymo reported 63 disengagements over
352,545 miles (567,366 km) of testing, or 5,596 miles (9,006 km) on
average, the highest among companies reporting such figures. Waymo also
traveled more distance in total than any other. Their 2017 rate of 0.18
disengagements per 1,000 miles (1,600 km) was an improvement from 0.2
disengagements per 1,000 miles (1,600 km) in 2016 and 0.8 in 2015. In
March, 2017, Uber reported an average of 0.67 miles (1.08 km) per
disengagement. In the final three months of 2017, Cruise Automation (now
owned by GM) averaged 5,224 miles (8,407 km) per disruption over 62,689
miles (100,888 km). In July 2018, the first electric driverless racing
car “Robocar” completed 1.8 kilometers track, using its navigation
system and artificial intelligence.