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Autonomous Vehicles: Conceivable Transition or Science Fiction?
We’ve all seen science-fiction movies depicting future technological settings and found
them to be quite inspirational and promising. Despite the cynical nature of many such movies, we
frequently cling to the notion that the solution to all problems faced by humanity at large lie in
the advancement of technology. After all, the primary objective of engineers is to use scientific
advances to improve quality of life. This narrative contains a great deal of truth and plausibility,
and has been fed repeatedly by the transformative technological breakthroughs in human history.
For example, The Atlantic acknowledged that the invention of modern sanitation systems is a
largely responsible for increased life expectancy (Fallows, 2013). In the same way that modern
sanitation systems addressed the problems of post-urban communities, it is entirely conceivable
that autonomization of ground-based passenger vehicles will address a major issue of the
contemporary era: fatal vehicle accidents. According to the National Highway Traffic Safety
Administration (NHTSA), in 2014 vehicle accidents were the direct cause of more than 30,000
fatalities in the United States (2014 Motor Vehicle, 2016, p. 1). An NHTSA study also indicated
that from 2005 to 2007, 94 percent of all vehicle-related crashes were attributed to drivers with
only 2 percent attributed to vehicles (Singh, 2015, p. 1-2). Such a sharp contrast between human
failure and machine failure suggests that the latter are better suited to perform the task of vehicle
navigation. Companies such as Google Inc. and Tesla Motors Inc. have spearheaded research and
development in autonomous vehicles (AV) navigation with promising results. Throughout this
work, an AV is defined as an intelligent robot capable of visually processing the road as well as
directing the occupants to target destinations (Pessin et al., 2014, p. 3047). This work will
examine the need for vehicle autonomization and uncover the technologies that can be utilized to

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make it a reality. It will continue by identifying areas in which further research must be
conducted and conclude with thoughts on the future of AV technology.
The Need for Machine Control
In order to evolve as intelligent beings, we must be objective and honest about our
inherent limitations and shortcomings. To this end, we must not underestimate the potential of
machines to increase our quality of life by allowing them to perform tasks for which they are
better suited. The NHTSA statistical summary by Singh (2015) examines the causes of driver-
induced crashes from 2005 to 2007. According to table two, 85 percent of these causes are
attributed to recognition, decision, and performance errors. These errors refer to distractions,
inattention, lack of awareness and compensation for road conditions, and illegal maneuvers (p.
2). An objective analysis of results would lead to the inevitable realization that machine control
should be increased due to machines’ natural inability to exhibit such behaviors. Machines are
purpose-built devices that are immune to distractions. Their image analysis capabilities are
limited to processing only stimuli relevant to their immediate task, eliminating distractions of any
kind. Wireless technology can enable machines to continuously stream up-to-date information
regarding weather, traffic and legal regulations. For each such scenario, compensation
mechanisms can be built with error tolerances to ensure safety. The simplicity of machines
relative to human beings is their very strength in performing the mundane and simplistic task that
is vehicle navigation.
Promising Technologies
Technologies exist which can mimic human senses and responses. While machines
possess computational speeds that far surpass their human counterparts, to safely navigate a

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vehicle they would require intelligence similar to that of human beings. Few technologies have
advanced artificial intelligence (AI) as much as artificial neural networks (ANN). The inspiration
behind ANN creation and use is entirely biological in nature, as they were designed to mimic the
functions of the human brain by incorporating artificial “neurons” composed of transfer functions
(Ding et al., 2011, p. 252). When applied to AVs, ANNs can be trained to identify visual cues in
much of the same manner that human beings do.
To employ ANNs to autonomously perform vehicle navigation, they must be trained to
follow the road by identifying which areas are navigable and which areas are not. Shinzato and
Wolf (2010) made impressive use of ANN combinations to achieve this task. An image captured
from an on-board camera was first classified into blocks based on frame resolution (p. 529). The
images were then classified by ANNs that use a multilayer perceptron (MLP) model that “maps
sets of input data onto specific outputs” (p. 531). Results showed that more than 90 percent of the
images received by the controller were identified by the ANNs correctly (p. 534).
After an ANN translates visual input into usable navigation data, it can then map those
inputs to a set of outputs that represent vehicle commands such as acceleration, deceleration,
steering and braking. Pessin et al. (2014) addressed two out of the three vehicle command
outputs, steering and acceleration, by designing a control model composed of two ANNs. The
first ANN was responsible for image-analysis of the on-board camera video feed whereas the
second ANN was in charge of creating acceleration and steering commands based on the results.
The test vehicle was successfully able to navigate the path required with an impressive average
steering and velocity error of only 0.0052 (p. 3056). Experiments by Alonso et al. (2011)
performed small-scale AV navigation via decision-making algorithms by guiding a test vehicle

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through multiple traffic scenarios. The vehicle was fitted with a communication system which
provided information regarding the intentions of the surrounding manually-driven vehicles as
well as navigation data (p. 1095). Alonso et al. experimented with two methods of priority
attribution: priority levels and charts. The control system would either create vectors
representative of obstacle locations or assign low, medium and high priority levels to each
surrounding vehicle depending on which method was used (p. 1102). Both experiments were
successful, with the system correctly identifying which vehicle had right-of-way and yielding as
expected. The success of the experiments adds credibility to the idea of AVs operating alongside
semi-autonomous vehicles without reliance on costly infrastructure changes.
Promising as they may be, ANNs are known to exhibit unpredictable behavior due to
their mutative abilities, and therefore non-ANN approaches to artificial vision and guidance are a
valuable component of AV research. Sundra Murthy and Varaprasad’s (2013) article
demonstrates the accuracy of image analysis without use of ANNs. The proposed system scanned
images of the oncoming road surface and used edge detection techniques to identify distinct
objects from their surroundings, allowing the test vehicle to identify potholes and cracks (p. 546).
Such road surface irregularities demonstrate different light-absorption characteristics when
compared with normal road surfaces, making them clearly visible in an illuminated setting.
MATLAB’s image acquisition and processing toolbox was used to analyze images based on
grey-scale pixel values and depressions were successfully identified in various scenarios (p. 547).
Zhao et al. (2012) similarly showcased the robustness of non-ANN solutions by using a
proportional integral derivative (PID) controller with automatically changing parameters to
perform vehicle path guidance. Classic control system theory was used to create an adaptive

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control algorithm and vehicle trajectory was compared with competing vehicles in The Future
Challenge of Intelligent Vehicles (p.2). Results showed that Zhao et al.’s system resulted in
lower track error and trajectory deviation than the competition, most of which relied on modern
technologies such as ANNs and genetic algorithms (p. 9).
Research Gaps
Since errors and shortcomings in autonomous vehicle function can lead to loss of life, it
is foreseeable that any legislative inclination to integrate AVs into the road network would
compare them to perfection and not their manned counterparts. Therefore, advancing AV
research and development requires us to objectively identify all sources of error and seek to
minimize them. We must also identify all gaps that exist in the AV research spectrum and fill
them with viable experimental results. For example, the lack of laser-based camera systems in
experiments conducted by Sundra Murthy and Varaprasad (2013), Pessin et al. (2014), and
Shinzato and Wolf (2010) suggest that the AVs do not possess a depth-perception capability. In
order to navigate the complex environment in question, depth is perceivably a critical component
of artificial vision. While the aforementioned experiments hint at future works involving laser
cameras, little research exists that identifies road contours based on laser imaging. Depth
perception must be enabled so that AVs can accurately analyze the three-dimensional operating
environment.
Additionally, it must be noted that no viable method is proposed that solves the problem
of providing required braking commands to an AV using data from image-analysis ANNs. While
Pessin et al. (2014) masterfully demonstrated the use of ANNs to control steering and
acceleration, the subject of braking was largely absent from the experiment and was presumably

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conducted manually. On the other hand, while experiments conducted by Alonso et al. (2010)
include brake-control, the vehicle relies on a vehicle-to-vehicle communication network which
transmits the telemetry and intentions of surrounding vehicles. The fact that artificial vision is
not currently capable of determining intentions of other vehicles, or determining their telemetry
suggests that braking may be a separate obstacle altogether.
Conclusion
Historical trends prove that groups often resist change for no reason other than fear.
Mainstream media devices designed to attract viewers by feeding on fears often do so at the
expense of technological progress. Nevertheless, an optimistic response to a cynical world would
conclude that this cynicism works in the favor of technological change. It calls for additional
pressure and scrutiny in the design and testing phase, both of which prepare new technologies for
a dynamic and interactive world. Comparison of AVs against perfection forces engineers to
create additional fail-safes and optimize designs in a way that ultimately guarantees long-term
AV implementation. When coupled with additional funding and research, the technologies
reviewed in this work provide a promising future for autonomous vehicle integration. Vehicle
fatalities are tragic in that they are created by the technological marvel that is the automobile. It is
therefore morally incumbent on engineers to reform this transformative invention in a way that
minimizes destruction and better serves humanity.

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References
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vehicle control systems for safe crossroads. Transportation Research Part C: Emerging
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Ding, S., Li, H., Su, C., Yu, J., & Jin, F. (2013). Evolutionary artificial neural networks: A
review. Artificial Intelligence Review, 39(3), 251-260. doi:10.1007/s10462-011-9270-6
Fallows, J. (2013). THE 50 GREATEST BREAKTHROUGHS SINCE THE WHEEL. Atlantic,
312(4), 56-68.
National Center for Statistics and Analysis. (2016, March). 2014 motor vehicle crashes:
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Pessin, G., Souza, J. R., Osório, F. S., Faiçal, B. S., Filho, G. P., Ueyama, J., & ... Wolf, D. F.
(2014). Investigation on the evolution of a robotic controller for autonomous vehicle
navigation. Journal Of Intelligent & Fuzzy Systems, 27(6), 3047-3058. doi:10.3233/IFS-
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Shinzato, P. Y., & Wolf, D. F. (2011). A road following approach using artificial neural
networks combinations. Journal of Intelligent & Robotic Systems, 62(3-4), 527-546.
doi:http://dx.doi.org.mutex.gmu.edu/10.1007/s10846-010-9463-2
Singh, S. (2015, February). Critical reasons for crashes investigated in the National Motor
Vehicle Crash Causation Survey. (Traffic Safety Facts Crash•Stats. Report No. DOT HS
812 115). Washington, DC: National Highway Traffic Safety Administration.

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Sundra Murthy, S. B., & Varaprasad, G. (2014). Detection of potholes in autonomous
vehicle. IET Intelligent Transport System, 8(6), 543-549. doi:10.1049/iet-its.2013.0138
Zhao, P., Chen, J., Song, Y., Tao, X., Xu, T., & Mei, T. (2012). Design of a control system for an
autonomous vehicle based on adaptive-PID. International Journal of Advanced Robotic
Systems, 9(44), Retrieved from
http://search.proquest.com/docview/1524236991?accountid=14541