lady_autonomous_vehicle_development

Running head: AUTONOMOUS VEHICLES 1
The Current State of Autonomous Vehicle Development
Kyle S. Lady
College of DuPage

AUTONOMOUS VEHICLES 2
Abstract
Autonomous (self-driving) vehicle technology, a relatively new field, has inspired thoughts of a
revolutionized transportation industry. The car industry is currently working on developing
various types of self-driving cars, however there are obstacles to both developing and
implementing this technology. A review of current technical reports, journal articles, and surveys
clarifies the current state of autonomous vehicle technology. In general, public opinion of
autonomous vehicle technology is positive, however the general public is not yet willing to fully
adopt this technology. The programming required to create a fully autonomous vehicle is
complex and cannot yet replicate human behavior. It is likely that a combination of autonomous
and semi-autonomous vehicles will be seen at one point, and this combination could have
negative impacts on driver safety. There is a lack of legislation concerning use of autonomous
vehicles, and differences in state and federal policies could prove to be an obstacle. In general,
there is some consensus on the implementation timeline for autonomous vehicles. Most sources
predict that fully autonomous vehicles will be available within approximately 30 years. The
degree to which they will be used will depend on how fast the technology evolves and how well
manufacturers respond to consumer concerns.
Keywords: Autonomous vehicles, AV technology, transportation, drivers, automation

The Current State of Autonomous Vehicle Development
Transportation is a key aspect of most modern societies. Cars in particular are used by
millions of people every day. The design of cars has changed significantly since their
introduction, however one particular concept offers to revolutionize the transportation industry.
This concept is the idea of an autonomous, or self-driving car. Throughout this paper, the term
“autonomous vehicle” or “AV technology” will refer specifically to cars, however this
technology certainly has the potential to be used for buses, trucks, etc.
The car industry is currently working on developing various types of self-driving cars,
however there are obstacles to both developing and implementing this technology. The purpose
of this paper is to provide a clear description of where autonomous vehicle technology stands
today and what the obstacles to implementing this technology are. This information will help
summarize and clarify the work being done to change the transportation industry. The following
aspects of autonomous vehicle technology will be addressed:
1. An overview of the field including a brief history of the technology and levels of
automation.
2. Public perception.
3. Technical issues facing designers.
4. Autonomous vehicle safety and possible social impact.
5. Government issues including legislation and infrastructure.
These aspects will be addressed using sources such as current technical reports, journal
articles, and surveys on autonomous vehicle technology.

Overview
Autonomous vehicles have been in development since around 1980 when researchers
explored the concept of cars that would be controlled by the highway in the same manner that
rollercoaster cars are controlled by the track. The next major step in perfecting AV technology
came when the U.S Defense Advanced Research Projects Agency (DARPA) promoted the field
by taking autonomous vehicles developed by several different universities and pitting them
against each other in three competitions held in both rural and urban environments. The
designing of autonomous vehicles continues to progress today. Google has developed several
versions of self-driving cars ranging from level 3 automation to level 4 automation, and many
other car companies such as Audi, Toyota, Nissan, and Tesla are currently designing their own
versions. (Anderson et. al, 2014, pp. xvii-xix).
Vehicles can be placed into various levels based on how advanced the automation is. As
seen in table 1, the NHTSA (2013) outlined four levels of automation.
Table 1. Four levels of automated vehicle automation as defined by the National Highway Traffic
Safety Administration in a 2013 report.
Adapted from Department of Transportation National Highway Traffic Safety
Administration (2013, May). Preliminary statement of policy concerning automated vehicles.
National Highway Traffic Safety Administration.
Level Definition
1 This level involves no vehicle assistance for throttle, brakes, and
steering.
2 This level may include cruise control, however the driver still controls
brakes and steering.
3 This level is where most modern development stands. The vehicle
controls the throttle, brakes, and steering, however the driver may need to
intervene in some scenarios.
4 This level allows the driver to input a destination and be transported
without having to intervene. The vehicle acts as a personal taxi.

Yang and Coughlin (2014) claimed that "the development of self-driving cars or
autonomous vehicles has progressed at an unanticipated pace." (p.333). Mladenovic &
McPherson (2014) compared the development of autonomous vehicles to the invention of the
internal combustion engine and microprocessor and stated that autonomous vehicle technology
has the potential to change society on a social level. Clearly this technology is exciting for some
people, and there are several reasons for this.
Autonomous vehicles offer benefits in several areas. In a 2008 report, the National
Highway Traffic Safety Administration investigated 5,741 crashes over a two and a half year
period and found that human error was the critical reason for around 85% of these crashes. These
errors included distractions, aggressive driving, and overcompensation for mistakes (Department
of Transportation National, 2008, pp. 1-2). Autonomous vehicles with carefully designed
technology offer the ability to greatly reduce the amount of accidents involving human error.
Autonomous vehicles could also potentially benefit those unable to drive due to a disability by
giving them more freedom in mobility than public transportation allows while reducing the cost
of paratransit services (Anderson et. al, 2014, p. xv). Cost is also a factor. In a 2013 report, the
Eno Center for Transportation suggested that autonomous vehicles had the potential to save up to
$450 billion annually in the United States (Eno Center for Transportation, 2013, p. 17).
Public Perception
Public perception of autonomous vehicle technology will likely be the driving force
behind the integration of autonomous vehicle technology. Autonomous vehicles challenge the
idea of cars being "docile to the driver's commands", and allow the vehicle to make more
decisions without driver input (Gerla, Lee, Pau, & Lee, 2014). Widespread adoption of
autonomous vehicles will undoubtedly require a shift in the way that drivers view their cars.

Some sources indicate that this shift in thinking may go over well. In a survey of 1,533
people in the U.S, the U.K, and Australia, Schoettle and Sivak (2014) found that most
respondents were optimistic about the future and benefits of autonomous vehicles as seen in
figure 1 (p. 26). However, there are several areas of concern.
Figure 1. General public opinion of autonomous vehicle technology from a 2014 survey of 1,533
people from the United States, United Kingdom, and Australia.
Adapted from Schoettle, B., & Sivak, M. (2014, July). A survey of public opinion
about autonomous and self-driving vehicles in the U.S., the U.K., and Australia (Research
Report No. UMTRI-2014-21).
The use of computer-like technology in autonomous vehicles has raised concerns about
data privacy. This is an important issue as data abuse is an important issue for many consumers.
In a survey of 5,000 people in 109 countries, Kyriakidis, Happee, & De Winter, (2014) found
that the biggest concern respondents had about autonomous vehicles was "software
hacking/misuse". However, differing state laws on autonomous vehicles could make this issue

complicated, which suggests that federal action will have to be taken to simplify regulations
(Eno Center for Transportation, 2013, p. 16).
Another important aspect of autonomous vehicles is how they will fare alongside
manually driven cars. Cars are not currently separated by type, however people feel differently
about autonomous vehicles. In the Berkeley, California survey, only 46% of those surveyed
"believe[d] that self-driving cars should operate with normal traffic" (Howard & Dai, 2013, p.
11).
Cost is another area of concern likely have a significant effect on adoption of autonomous
vehicles. Schoettle and Sivak (2014) found that a majority of respondents were concerned about
cost (p. 26). This was also found in the Berkeley, California survey (Howard & Dai, 2013, p.18).
In addition to concerns about cost, safety, and data privacy, there are significant
intangible differences between manually driving a car and riding in an autonomous vehicle. In a
survey of 5,000 people in 109 countries, Kyriakidis, Happee, & De Winter (2014) found that
manual driving was seen as more enjoyable than riding in an autonomous vehicle. Ramm,
Giacomin, Robertson, & Malizia (2014) conducted a survey of fifteen drivers in which the
drivers were asked in-depth questions about the "naturalness" of interaction with their cars. The
results showed that the drivers felt it was more natural for cars to assist with driving in a copilot
role as opposed to fully automating the process.
Car manufacturers will have to respond to these concerns, and there a variety of ways in
which consumer interest can be raised. Waytz, Heafner, and Epley (2014) discovered that the
addition of human characteristics such as a voice and a name to autonomous vehicles increased
the user's trust in the vehicle's capabilities (p. 116). This indicates that a part of public distrust in
the capabilities of autonomous vehicles may be due to the user experience, something that will

no doubt be improved as development continues. Aging drivers could also impact public opinion
of autonomous vehicles by raising demand. (Yang and Coughlin, 2014, p. 338).
Technical Issues
Developers of autonomous vehicles face many obstacles concerning technology. In order
to function well, there are four general areas in which an autonomous vehicle must achieve
mastery. The first area is “seeing” its environment through various sensors or cameras. Important
objects for the vehicle to recognize include other vehicles, pedestrians, traffic signs, and
obstacles. The second area is the development of algorithms allowing the vehicle to make
decisions based on the information obtained from its sensors or cameras. The third area is the
possession of accurate maps so that the vehicle can determine exactly where it is at all time. The
fourth area is the driver-vehicle interface, where the driver can intuitively interact with the
vehicle in order to achieve the best possible driving experience (Bernhart et al., 2014, p. 8).
The first area of mastery is a complex process. The process of “seeing” an immediate
environment could be done using images from cameras, as cameras contain a large amount of
information compared to other available sensors. (Lee, 2014, p. 158). However, the use of
cameras can become a concern when driving in inclement weather. Once this information is
obtained, the next step for the vehicle is to use that information.
Some of the difficulty in programming autonomous cars comes from the fact that
autonomous cars are faced with a greater variety of scenarios than other vehicles such as planes
or trains. For example, when faced with a hazardous situation, trains can only respond by
braking. Plane crashes are almost never trivial, however they occur very rarely. Car crashes,
however, range from trivial to deadly, and drivers are frequently faced with a wide variety of
different choices in crash situations. As Goodall (2014) explained, sometime injury can be

avoided, however the inevitability of injury would require certain moral considerations during
the decision making process. Due to the difficulty in "encod[ing] human morality in software",
Goodall proposed three stages of development for autonomous vehicles concerning decision-
making. The first stage, which is where today's development stands, would involve programming
the vehicle to attempt to minimize the damage done in a crash by basing the decision on simple
principles. This second stage, which is not currently feasible, would examine a wide variety of
crash scenarios in which humans simulate crashes and determine the best approach. This data, in
the form of a "neural network" would then somehow be programmed into the vehicle thus
enabling the vehicle to learn from virtual experience. The third stage would allow the vehicle to
somehow explain its decisions in "natural language" (pp. 63-64).
Finally, most of the algorithms used in today's autonomous vehicles assume that the car
will be driving in a defined lane (Kala & Warwick, 2015, p.60). Although this would not be a
problem in most cases, the possibility of an emergency situation involving off-road driving
suggest that the driver’s ability to manually override the vehicle may be necessary.
If an override occurs, a well-designed driver-vehicle interface can influence the ease of
this transition. For level 3 automation, drivers engage better with the transition between
autonomous driving and manual driving when the automation engages at fixed intervals,
indicating the future need for autonomous vehicles to have a warning system in place to give
drivers sufficient notice that manual driving mode will be engaged. (Merat, Jamson, Lai, Daly,
& Carsten, 2014, p. 274).
Safety and Social Impact
The idea of implementing autonomous vehicles has brought up safety concerns. In a
detailed observation of the behavior of twelve drivers of vehicles with both cruise control and

automatic steering, Llaneras, Salinger, & Green (2013) found that the time participants spent
looking away from the road increased by 33 percent (p. 97). In the event that a manual override
becomes necessary, drivers will need to be able to quickly return their focus to the road. In a
2014 report, the RAND Corporation stated that the driver's transition time for switching from
autonomous to manual modes should be "seconds or less" after being notified by the vehicle in
order to achieve maximum safety (Anderson, et. al, 2014, p. xx). However, one study found that
the average transition time for drivers switching from automatic to manual modes is around 35-
40 seconds. (Merat, Jamson, Lai, Daly, & Carsten, 2014, p. 274).
There is also a distinct possibility of a combination of autonomous vehicles and
manually driven vehicles operating on the same road. M and Schoettle (2015) reported that
autonomous vehicles may not actually be safer than "an experienced, middle-aged driver", and
that having both autonomous vehicles and manually driven vehicles on the road at the same time
could decrease safety for manually driven vehicles (p. 7). One small-scale study found that the
mixing of manually driven cars with autonomous vehicles in a platoon format caused nearby
human drivers to decrease their following distance (Gouy, Wiedemann, Stevens, Brunett, &
Reed, 2014, p. 264).
Social impacts of autonomous vehicle technology can also be considered. Mladenovic &
McPherson (2014) explained how despite the fact that technology affects the social decisions
people make, decisions made about the development of autonomous vehicles are focused on
technical rather than social implications. This could potentially lead to "tunnel vision" in
designing autonomous vehicle technology.
Government Issues
State / Federal Policy

Several states are developing laws concerning the use of autonomous vehicles, however
these laws vary from state to state. As of a 2014 report, Nevada, California, Florida, and
Washington D.C have passed legislation involving the testing of autonomous vehicles, while
fourteen other states have passed or are developing legislation defining autonomous vehicles for
use in future policies. However, this legislation for the most part defines autonomous vehicles
"as vehicles with the capability to self-drive without being actively controlled or monitored by a
human operator" (Anderson et. al, 2014, p. 41). This could prove to be an obstacle for
implementing semi-autonomous vehicles.
Despite the lack of legislative focus on AV technology, it is likely that the technology will
continue to progress, meaning states and the federal government will have to legally catch up to
progression in AV technology if action is not taken soon (Eno Center for Transportation, 2013, p.
15). Winston & Mannering (2014), stated that due to "political and bureaucratic impediments", it
is likely that the private sector will implement new technology faster than public policymakers
can, resulting in a "leapfrogging" effect (p. 158).
Infrastructure
Litman (2015) suggested that autonomous vehicles will "have only modest impacts" on
the transportation industry over "the next few decades" (p.16). However, other sources painted a
different picture, especially in the long term.
Often, 20% of space in urban areas is used simply for parking space, however with the
use of a certain networking strategy, autonomous vehicles can collaborate with each other to
move in a way that can reduce space needed for parking in urban areas by up to 50% (Ferreira et
al., 2014, pp. 7-8). Autonomous vehicles could also potentially be used as automated taxis,
traveling in fleets around cities. In a simulation of this transportation system used in a mid-sized

city, Martinez (2015) found that these fleets of autonomous vehicles have the potential to replace
most current public transportation systems (p. 33).
Timeline
Due to the many factors affecting autonomous vehicle development, there are a variety
of predicted timelines. However, several of these predicted timelines are similar. Yang and
Coughlin (2014) predicted that level 4 autonomous vehicles would be available "within the next
10-20 years" (p. 333). Levin and Boyles (2015) predicted that autonomous vehicles "may be
publicly available within the next two decades, within the span of 20 to 30 year planning
analyses". The Institute of Electrical and Electronics Engineers (2012) predicted that by 2040
autonomous vehicles would "account for 75 percent of cars on the road". A 2014 report from
Roland Berger Strategy Consultants predicted that level 3 automation would be available by
2018-2020 while level 4 automation would be initially available by 2020-2025, reaching a level
of "global adoption" by 2040 (Bernhart et al., 2014, p. 7).
Conclusion
The possibility of autonomous vehicles becoming a mainstream form of transportation
raises many important questions, most of which have not been answered. Autonomous vehicles
offer benefits in several areas, and public opinion is generally positive. However there are also
areas of concern such as cost, data privacy, and operation alongside manually driven vehicles
which will have a significant effect on integration of autonomous vehicles. Most technical issues
lie in programming, and improved safety is not a guarantee at this point. There is general
consensus concerning the timeline for implementing autonomous vehicles, however a lack of
legislation is an issue that will need to be resolved before autonomous vehicles can appear on
public roads. This technology is moving forward and despite the obstacles, the number of

organizations that are researching this technology and the speed at which technology has been
improving suggests that autonomous vehicles will play a major role in the future of
transportation.

References
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