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.