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.
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.
Traffic
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 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 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 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,
- 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 iden
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 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.
References
Brian Wang. Uber' self-driving system was still 400 times worse [than] Waymo in 2018 on key distance
intervention metric. NextBigFuture.com, 2018.