Session C8
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THE BENEFITS OF LIDAR TO ADVANCEMENTS IN DRIVER ASSISTANCE
SYSTEMS AND THE FUTURE OF DRIVER SAFETY
Stephen Gian, sgg15@pitt.edu, Lora 1:00, Garrett Davey, gld14@pitt.edu, Budny 10:00

1
University of Pittsburgh Swanson School of Engineering
1/27/2017

 Stephen Gian
Garrett Davey
Abstract — The automobile industry is undergoing one of the
largest revolutions in its history thanks to the implementation
of advanced driver assistance systems (ADAS) in cars today.
These systems are composed of lasers, cameras, and radar
that work with the driver to improve safety and efficiency on
our roads. One standout system that may revolutionize the
way we think about transportation is called LIDAR (light
detecting and ranging). This system works by beaming high
speed lasers 360 degrees around the vehicle from a module
positioned atop the car (for maximum sight lines); which
then measures the time it takes between the emittance of the
laser and the reflection on the object it collides with, creating
a virtual 3-D representation of the surroundings. Once the
module has completed its analysis of the surroundings
(approximately every five nanoseconds), pre-programmed
algorithms detect obstacles, lanes, and other cars and direct
the car in such a way that resembles the traditional human
driver. Although these systems are still relatively unheard of
to the masses, significant progress has been made in the
evolution of said technologies within the past two years
bringing engineers closer to the solution for self-driving
cars. Autonomous cars can be seen being tested in
Pittsburgh, PA by the worldwide online transportation
company Uber. Although we are on the horizon of the peak of
this technology, there are still some ethical questions to be
answered about unmanned two-ton machines on our roads.
The autonomous car will also transform our pre-conceived
conceptions about transportation and show us that much will
change with coming of the autonomous vehicle. LIDAR
affords the opportunity for innovators to utilize its technology
in the advancement of the autonomous car and a safer more
efficient road in our future.
Key Words – Advanced Driver Assistance Systems (ADAS),
Automobile Industry, Autonomous Driving, Laser, LIDAR,
Sensor, 3-D Mapping

SELF-DRIVING CARS ARE THE FUTURE
The 2016 TIME Magazine article “Forget the Distant
Future, Smarter Cars Are Already Here” states that “Cars are
well into the biggest automotive revolution since Henry Ford
debuted his assembly line. This historic transition from
analog to digital promises to do to driving what the iPod and
streaming did to music” [1]. Anyone with a cell phone would
agree that getting music today is much more convenient than
it was when they were a child. Automobiles are going
through a parallel transition. In only ten years, the automotive
industry has transformed by applying the newest technology
to the cars they build. Advancements in radar, camera, and
laser systems have led the revolution in Advanced Driver
Assistance Systems (ADAS) [2].

A developing technology, called LIDAR (Light Detecting
and Ranging), is helping to improve the safety and efficiency
of our cars today, and will be essential for advancements in
driver assistance systems and autonomous technology.
LIDAR works by beaming rays of light 360 degrees around
the vehicle and measuring the time it takes for the light to
reflect to create a virtual 3-D representation of the
surroundings. This map of surroundings allows the car to
recognize obstacles, hazards, and lanes of travel with preprogrammed algorithms. LIDAR systems are also beginning
to be manufactured in more affordable and compact
packages, allowing this technology to become available for
more in depth testing and research. Research and
experimentation with vehicles using LIDAR is proving to be
a path that will lead us to the widespread use of autonomous
vehicles in the future.

THE TECHNOLOGY BEHIND LIDAR IN
AUTONOMOUS VEHICLES
The technology behind LIDAR was designed to tackle the
first problem encountered by engineers in the quest for a fully
autonomous vehicle: vision. Computers lack the sense of
sight that many drivers take for granted which is arguably the
most crucial sense to the operation of a vehicle. LIDAR
modules work by emitting lasers from a centrally located unit
and measuring the amount of time it takes for the system to
detect a reflection from the laser off an object. Once the
module takes these measurements and makes calculations
about the distance to the objects, it will create a threedimensional map of its surroundings and convert that into a
digital copy. The module also detects static and stationary
objects in its vicinity and accounts for them appropriately in
the mapping of its surroundings. The onboard computer of
many devices utilizing LIDAR will then interpret the digital
copy of its real-time surroundings. And from there, in
automobiles specifically, software with pre-programmed
algorithms guides the vehicle as if a human were driving
behind the wheel by staying on the road, within the lanes, and
turning with the curves. But above all, one of the most
important aspects to LIDAR in autonomous vehicles is that it
will keep passengers and pedestrians safe by recognizing
them before a human and avoiding them at all costs. This will
save lives and money by preventing accidents on our roads.
LIDAR is composed of many different parts that make it a
revolutionary technology capable of changing the way we
view transportation.
LIDAR in autonomous vehicles consists of what is
basically a rotating module positioned at a high, central
location on a vehicle for maximum sight lines and greatest
distance viewed. The LIDAR module itself can be compared
to a small potted plant with openings for the lasers to be
emitted. The system works by sending out very bright pulses
of infrared light while rotating to ensure that three hundred
and sixty degrees of the vehicle are mapped at all times.

 Stephen Gian
Garrett Davey
These sensors are running at approximately nine-hundred and
five nanometers, which means that this light is invisible to the
human eye and is extremely densely packed. Every five
nanoseconds a seventy-five to one-hundred watt burst of light
is emitted from the module. This light goes out as a very
tightly packed and collimated burst so it does not spread. It is
necessary for the light to be in this form because the LIDAR
must be able to keep track of the laser it emits so that it can
produce an accurate map of its surroundings. Once the lasers
reach their respective obstacles, the first object in its path will
reflect the laser beam and that is what registers with the
module back atop the vehicle. A timer begins when each laser
is emitted and ends when the reflection of light is detected by
the extremely sensitive sensors in the module. Since the
speed of the light is very constant at

a three-dimensional manner, it can become a practical
alternative to constant human attention. And although this
technology only makes up a fraction of the autonomous car, it
is critical to its success because of its bridge between the
natural and mechanical worlds.

3.00 x 108 m/s ,

this yields a distance from the vehicle that an obstacle lies.
The computer uses the equation

S peed

( ms )∗Time ( s )=Distance ( m )

FIGURE 1 [3]
Distance derived from speed and time measurements.
to find the distance from the vehicle the object lies. As the
LIDAR fires these beams over and over in a vertical line in
one single rotation, it takes in the world as we see it, but
instead in very short periods of time and distance, therefore
creating a view similar to what we see with our eyes. LIDAR
distinguishes itself from cameras because it is able to detect
depth, therefore making a three-dimensional map possible. It
is also capable of interpreting information over multiple
rotations and then integrating that over time to detect moving
objects in its path. No matter the size of the obstacle or the
slightest change of depth, the LIDAR will be able to detect it
with its extremely sensitive sensors and produce a threedimensional image that a computer can read and interpret [3].
Senior Engineer, Scott Boehmke of Uber Advanced
Technology Group (ATG), is well versed in the hardware of
LIDAR and agreed to speak with us about this developing
technology. He gave us insight into the future of autonomous
cars when he said “You can imagine if you drove around
town and you did this enough times, you can get a good idea
of what is static and what is stationary, what is something
that’s new, or something that’s most likely moving around. It
might be a pedestrian, it might be a car, it could be a box in
the road. Those comparisons help you to navigate the world”
[3]. The software also includes a section called “prediction”
where it will recognize moving objects and predict their path.
While this is how LIDAR functions in an autonomous
vehicular environment, LIDAR functions similarly in other
applications such as agriculture and tunnel mapping. This
technology when alone is nearly useless, but when paired to
machines that require the observations of its surroundings in

FIGURE 2 [3.5]
Figure [2] represents the car that Mr. Boehmke and his
team of engineers have developed for Uber Advanced
Technology Group (ATG). It can often be seen patrolling
the streets of Pittsburgh with a driver in the driver’s seat
in case of disengagement, and another engineer in the
passenger seat collecting data.

THE APPLICATIONS OF LIDAR TO THE
AUTOMOBILE INDUSTRY
LIDAR’s revolutionary technological capabilities can be
applied to a magnitude of different applications from
surveying to cartography, but one of the foremost
applications of LIDAR is the autonomous vehicle. In Yong
In, Korea researchers are currently working on the answer to
the autonomous vehicle at the Hyundai MOBIS smart car
research center. And in recent years, the “practical
achievement on autonomous driving tasks of a personal
vehicle becomes the most concerned subject for car industries
in the world” per the article “Experimental Studies of
Autonomous Driving of a Vehicle on the Road Using LiDAR
and DGPS” [4]. Leading automotive companies of the world
such as Mercedes-Benz, Volvo, Ford, and more are currently
competing against each other in the development of the
autonomous car. This competition does not only include these
companies, but also global IT companies such as Google,
Apple, and Uber. This hyper competition between multibillion dollar firms for the world’s first autonomous vehicle
will bring this technology to the roads before many citizens

 Stephen Gian
Garrett Davey
would expect to be chauffeured by their own vehicle. For the
past 8 years or so, fleets of autonomous vehicles have been
tested in unison with human drivers, which presents itself as
an engineering miracle alone. Most experts would agree that
engineers are eighty-five to ninety percent of the way to
perfecting the hardware, guidance systems, and software that
can reliably and safely drive themselves [5]. But they would
also agree that the last ten percent is going to be the hardest
to overcome. This last ten percent includes rare situations
where the technology is not capable of seeing due to bright
light exposure or when a road is poorly marked. Other
questions looming in the future of autonomous vehicles might
be who is at fault when an accident does occur? Or who will
an autonomous car put in danger first: the driver or the
pedestrian in an emergency situation? But for those of you
reading who aren’t quite ready to give up your driving rights
yet, don’t worry. Consumer Reports estimates it will take
decades before autonomous cars replace human driven cars in
significant numbers [5]. Even Mr. Boehmke agrees that
autonomous vehicles will not become part of our daily
commutes for many decades [3]. But to say the least, the use
of this technology in vehicles is one of the most exciting
innovations to come in our lifetimes. It will without a doubt
revolutionize the way people think about all transportation.

LIDAR in Modern Automobiles
Engineers have struggled to harness the powerful
capabilities of the human brain like the senses of sight, smell,
hearing, and touch in a system and translate them into data.
Although there is nothing manufactured quite like the human
brain (yet), LIDAR seems to be the solution when talking
about sight and autonomous vehicles because it serves as the
first step in the mechanical interpretation of the vehicle's
surroundings. The technology is revolutionary because it can
replicate the sense of sight that is taken for granted by many
humans. Cameras have been able to do this for over a century
now, but the challenge arises when translating that image into
data for a computer to read while also incorporating depth.
The first challenge posed to engineers while developing
an autonomous vehicle is how will a machine see its
surroundings just as a human would behind the wheel, and
interpret this data like the human brain? LIDAR is a practical
solution to this problem because it goes farther than a camera
and takes depth and distance into account. The hardware
observes its surroundings in three-dimensions (x,y,z), acting
as the eye, and the software interprets the moving image,
acting as the brain. And although the LIDAR itself only
makes up a portion of the system used in autonomous cars, it
is still one of the most important components because of its
ability to imitate the human eye.
LIDAR in vehicles has the possibility of making a car
completely autonomous by observing its surroundings and
translating that image into interpretable data for the computer
to read. Once the LIDAR has a valid image and the computer

is able to completely understand the received image, the
algorithms in the software guide the car through a complex
series of programs based off the image the LIDAR transmits
to the computer. LIDAR is being used in automobiles for the
sole purpose of creating the autonomous car. People have
already begun designing LIDAR packages fit for the use in
cars. According to the research proposal “A 2D Resonant
MEMS Scanner with an Ultracompact Wedge-like Multiplied
Angle Amplification for Miniature LIDAR Application”, this
application has already “…recently created a new demand for
low-cost, low-power and small-package three-dimension
LIDAR” [6]. This article proposes an ultra compact LIDAR
system that minimizes voltage usage, cost, and maximizes
reliability. This demonstrates the industries seriousness about
the autonomous car and how LIDAR plays an important role
in the development and design of autonomous systems.
The Benefits of LIDAR and Autonomous Vehicles to
Society
In 2014, ninety-four percent of accidents were attributed
to human error or choice, causing over thirty thousand deaths
in the United States and another one point two million
worldwide [7]. This statistic must be lowered, and can be
done with the help of autonomous vehicles. In an ideal world,
a computer is the safest driver of them all: it does not get
distracted behind the wheel, it does not drive drunk, and it
does not speed. All of which are the leading factors in
accidents caused by humans. So now Imagine a world where
the roads are free of accidents and deaths, because it is
possible with the help of autonomous vehicles.
Another benefit of autonomous vehicles is the time freed
up when spent as a passenger rather than a driver.
Autonomous vehicles utilize DGPS to guide them from point
a to point b. This means that drivers will suddenly no longer
be a driver and rather a passenger. That allows drivers to
spend their time doing something they’d rather do which
prevents accidents and gives the driver the opportunity to
spend that time lost driving on another task.
Autonomous cars can also be the solution to a greener
highway when paired with the developing clean vehicle
market. The EPA has pushed for a greener highway in hopes
of raising the average mile-per-gallon to fifty miles per gallon
by twenty twenty-five, more than double the average today
[8]. “This is possible because the combustion engine-based
cars are now gradually transformed into motor-driven electric
cars, which can be thought of as mobile robots. Eventually,
once mobile robot technologies for unmanned navigation
have been intensively developed they can be directly applied
to autonomous driving tasks of electric cars” according to the
article, “Experimental studies of autonomous driving of a
vehicle on the road using LiDAR and DGPS” [4]. This will
benefit society by making the future a healthier place for
coming generations, sparing them of terminal lung diseases
and cancer from pollution directly created from the millions
of vehicles in use today.

 Stephen Gian
Garrett Davey
A final benefit to autonomous vehicles may be a connected
grid that could possibly make commutes faster while still
obeying the all traffic laws. If all cars were synced to a
common grid and programmed to their final destination, most
predictable congestion could theoretically be eliminated from
commutes. This would be possible because the main source
of congestion, human incapability of driving, would be
eliminated by the computer driver and a streamlined flow of
vehicles could be possible. Cars connected in a common
system would rather move as one system then individually
unrelated cars, making it possible for the aforementioned
congestion to be diminished making everyone’s driving
experiences less stressful and safer.
Local LIDAR – Uber Technology Inc. in Pittsburgh
While researching this intriguing technology, we had the
opportunity to speak with a hardware engineer, Scott
Boehmke (as mentioned before), working for Uber ATG here
in Pittsburgh. He was able to give us a great deal of
information on the LIDAR system and how it worked.
Without his insight our paper would be significantly less
thorough and comprehensive. Uber chose Pittsburgh as its
testing site for autonomous vehicles because of its advanced
terrain like tight turns, steep and frequent hills, and oddly
shaped and marked roads. The data collected in Pittsburgh
will help in the development of the autonomous car one day
available for sale by the public. It is an incredible opportunity
for us to be able to see the technology of the future on our
streets being spearheaded by leaders in the engineering field
like Mr. Boehmke. He was able to give us invaluable insight
into the developing autonomous vehicle industry.

FIGRURE 2 [8.5]
An example of what LIDAR observes and translates to
computer image
This would be an example of what the LIDAR hardware
observes and translates that into something readable by
computer code. The software then makes the decisions on
how the car drives itself.

THE ETHICS OF AUTONOMOUS
VEHICLES
Ethical Dilemmas Raised by the Possibility of
Pedestrian/Driver Injuries

The biggest concern about autonomous vehicles or
autonomous technology is the question of whether we should
trust technology to safely maneuver a two-ton machine with a
human inside. Even with the most updated LIDAR system
and complex coding, the computer in a self-driving car lacks
one thing that humans have: the ability to think for itself.
Scott Boehmke cites a specific example of a time when critics
of this technology might make a case against it, a situation
where a driverless vehicle is in a position where it must
choose between injuring two different pedestrians, or choose
between injuring the driver of another vehicle and the person
inside itself [3]. This ethical dilemma is recurring throughout
media, as seen in the article “Driverless Cars Will Face Moral
Dilemmas” by Larry Greenemeier of Scientific American,
which opens with “A self-driving car carrying a family of
four on a rural two-lane highway spots a bouncing ball ahead.
As the vehicle approaches a child runs out to retrieve the ball.
Should the car risk its passengers’ lives by swerving to the
side—where the edge of the road meets a steep cliff? Or
should the car continue its path, ensuring its passengers’
safety at the child’s expense?” [9].
In a situation like this where a vehicle with autonomous
technology seriously injures someone, would result in
lawsuits and harsh litigation, and probably jeopardize the
future of autonomous vehicles. Last year, a crash involving a
Tesla in its autopilot mode resulted the fatality of the driver.
Per the article “Nation’s first known self-driving car fatality
happened in Williston” from The Gainesville Sun, “...the
vehicle was on a divided highway with Autopilot engaged
when a tractor trailer drove across the highway perpendicular
to the Model S. Neither Autopilot nor the driver noticed the
white side of the tractor” [10]. After the crash, Tesla said that
“Had the car hit the front or rear of the trailer, even at high
speed, its advanced crash safety system would likely have
prevented serious injury as it has in numerous other similar
incidents, the company added” [10]. This specific example
illustrates why critics may raise ethical concerns; Would the
driver had noticed the trailer if he didn’t put his trust in the
autopilot mode of the Tesla? Someone may argue that
autonomous vehicles are more dangerous this way in that
they give the driver a false sensation that they don’t need to
pay attention to the road as much as they normally would.
Analyzing situations in which human intuition is replaced
with artificial intelligence using LIDAR and autonomous
technology effectively illustrates that there is rationalism for
ethical concerns. However, the future of LIDAR shows
promise for minimizing these concerns. New developments
from the engineering community and automobile industry
show that specific ethical dilemmas can be targeted with
advancements in LIDAR systems to ensure the continuing
growth of autonomous vehicles.
Solutions to Ethical Dilemmas Through Research on New
LIDAR Methods

 Stephen Gian
Garrett Davey
In reference to his example about LIDAR systems having
to calculate a decision about which person to injure in a
worst-case scenario situation, Scott Boehmke continues; “...I
think that we are striving to a solution to the problem that
doesn't get into these type of situations, where it needs to
make ethical decisions like that. You can be a very defensive
driver and never ever have an accident, even as a human. So
why can't we get the cars to be that careful also?” [3]. This is
one specific example of how the research and development of
LIDAR is contributing to the solution of an ethical concern.
Engineers at companies like Uber Technologies Inc. are
striving to ensure that LIDAR can ensure the safety of the
driver of an autonomous vehicle and obstacles this vehicle
must face when in motion. For example, a July 2016 article
from Institute of Systems and Robotics, Department of
Electrical and Computer Engineering, University of Coimbra
- Polo II, called “3D Lidar-based Static and Moving Obstacle
Detection in Driving Environments: An Approach Based on
Voxels and Multi-Region Ground Planes,” proposes a
solution to obstacle avoidance [11]. This in-depth article
details the analysis of a Velodyne LIDAR, the same system
Uber uses in their autonomous vehicles, and how this
LIDAR, along with computing power, can utilize different
methods to determine the mobile status of different obstacles
and predict where these obstacles are going to be in relation
to the vehicle to avoid collisions.
Two contributions researched in this article are: 1) A
piecewise surface fitting algorithm, based on a ‘multi-region’
strategy and Velodyne LIDAR scans behavior, applied to
estimate a finite set of multiple surfaces that fit the road and
its vicinity, and 2) A 3D voxel-based representation, using
discriminative analysis, for obstacle modeling [11]. Various
experiments were conducted using this two-part method, and
results were analyzed so it could be evaluated. In a total of
eight experiment sequences, the proposed method was tested
in moving vehicles, stationary vehicles, vehicles that would
stop and go, and data was collected in both rural and urban
areas, from highways to alleyways. This research turned out
to be very valuable and successful. Per the article,
“Experiments on the KITTI dataset, using point-cloud data
obtained by a Velodyne LIDAR and localization data from an
Inertial Navigation System (GPS/IMU), demonstrate the
applicability of the proposed method for the representation of
dynamic scenes. The system was proven robust and accurate,
as the result of both a quantitative and a qualitative
evaluation” [11].
The results of these experiments are important because
they show that there is a vast potential for new methods of 3D
perception of dynamic environments using laser and sensor
technology, which is one of the key components for
intelligent vehicles to operate in real-world environments.
The highly descriptive 3D representation created using an
intelligent vehicle equipped with a Velodyne LIDAR and
Inertial Navigation System (GPS/IMU) “...has an application
in safety systems of the vehicle to avoid collisions or

damages to the other scene participants” [11]. If an
autonomous vehicle is already equipped with smart enough
technology that avoids collisions in the first place, it has a
higher potential to completely avoid situations where the
system must choose between the safety of the driver and the
driver of another car, or choose between hitting a young child
or an elderly adult. This is just one example of a solution to
the ethical dilemma cited by Scott Boehmke, where the
LIDAR system on an intelligent vehicle replaces a careful,
defensive driver.

THE FUTURE OF LIDAR AND
AUTONOMOUS VEHICLES
The last section explored a specific example of a team of
researchers that developed and analyzed an experiment to test
new methods for obstacle detection and tracking using a
LIDAR system. Research like this is extremely valuable to
the future of autonomous vehicles, not just for technical
purposes, but because it de-mystifies the futuristic idea of
vehicles that drive themselves. It shows that our current
systems can be improved upon indefinitely; similarly to the
way Apple updates the iPhone, existing LIDAR can be
researched and experimented upon to make the old version
obsolete. In the timeline of universal autonomous driving,
one of the biggest limiting factors is money. For example, one
of the key points mentioned in the article “3D Lidar-based
Static and Moving Obstacle Detection in Driving
Environments: An Approach Based on Voxels and MultiRegion Ground Planes” was that the 3D measurements in the
form of a point-cloud required large memory and high
computational power, which is expensive to produce [11].
Even Scott Boehmke is skeptical about a speedy transition to
autonomous vehicles for this reason. He stated that the
LIDAR used on their self-driving cars at Uber has been
quoted around $75,000 a piece, not including the automobile
[3]. This is a substantial cost, considering the transaction
price for a new car in March 2016 was $33,666 [12]. When
asked “Do you foresee a future where our vehicles are
driverless or have a driverless feature?” Boehmke responds “I
guess way down the road that may be true, but the technology
is still fairly expensive, and not necessarily very sexy. You
probably wouldn't run out and buy a car that looks like one of
our cars, and pay the premium to have it be self-driving. So,
we’re not going to see everyday vehicles for a consumer
driving everywhere for a while still. But it’ll happen” [3]. The
end of his statement gives an optimistic look to the future, as
does the IEEE article “Lidar That Will Make Self-Driving
Cars Affordable” by Evan Ackerman. This article argues that
solid-state LIDAR, which “...uses an optical phased array to
steer laser pulses rather than a rotating system of mirrors and
lenses” can revolutionize the autonomous vehicle industry
because of its affordability and compactness [13].

 Stephen Gian
Garrett Davey
At the Massachusetts Institute of Technology, researchers
and students are in the process of developing a system of
solid-state LIDAR that can be condensed onto a single 0.5by 6-millimeter chip that can be fabricated in commercial
CMOS foundries. Although MIT’s prototype has a range of
only a few meters, “...there is a clear development path
toward a 100-meter range and a per-chip cost of just $10”
[13]. The market for small, affordable LIDAR systems such
as the one being researched and developed by MIT is
immense compared to the market for the standard bulky,
expensive Velodyne HDL-64E systems. Therefore, Velodyne
is working with investments from larger companies such as
Ford and Baidu to “...make a $100 automotive LIDAR sensor
available within the next few years” [13]. If a large wellknown company like Velodyne can produce compact,
ergonomic LIDAR systems at a cost of $100, it could mean
the commercialization of affordable self-driving cars soon.

SUSTAINABILITY AND ITS IMPACT ON
SOCIETY
According to the 2012 Rio+20 United Nations Conference
on Sustainable Development, sustainability is defined as
such: “Sustainable development emphasizes a holistic,
equitable and far-sighted approach to decision-making at all
levels. It emphasizes not just strong economic performance
but intragenerational and intergenerational equity. It rests on
integration and a balanced consideration of social, economic
and environmental goals and objectives in both public and
private decision-making” [14]. There are two deeper
meanings derived from this definition: 1) For a development
or innovation to be sustainable, it must satisfy the interest of
quality of life pertaining to modern society without taking
away from other generations, the environment, or the
economy and 2) It must promote forward-moving decision
making in public and private society. In our modern society,
for an engineering topic to be valuable it most likely has
advantages that pertain to some form of sustainability. Since
Henry Ford’s assembly line began rolling in 1913,
automobiles have been notoriously unsustainable until
modern developments. The gases released from the exhaust
pollute our air and earth, collisions result in countless injuries
and fatalities, and traffic congests our streets and obstructs
the natural beauty of the world. Autonomous vehicles driven
by LIDAR systems present sustainable solutions to these
unsustainable tendencies.
Imagine a technology smart enough to replace what was
once only able to be completed by hand. It has been done
before with fully automated assembly lines and can be done
again with autonomous vehicles. Although the current focus
of autonomous systems is on the personal vehicle, many other
vehicles are operated by humans and have the potential for
full automation. Public buses and commercial trucks are

some of these vehicles that have the potential to one day be
autonomous. This opens a whole realm of possibilities to
transform transportation on a basic level. Autonomous
vehicles will be able to reinvent the definition of
transportation; instead of a person being responsible for every
aspect of transportation from one place to another, such as
gas, directions, focus, and concentration, a new era of
transportation will show them that one day it will be as
simple as telling your car where you want to go and it will
drop you off steps away from the entrance and even park
itself. When you’re ready to leave, just hail the car with your
smart phone and once again you’re off. The possibilities are
endless when you think about how much this invention could
transform our world. Autonomous vehicles will revolutionize
public transportation by fully automating bus routes in
endless cycles that never run late and are always reliable.
Large scale mass autonomous transportation of goods could
also become a possibility eliminating the complicated
logistics of drivers and captains, completely replacing the
human truck driver allowing for quicker delivery and a faster
economy. Public transportation can be greatly improved with
autonomous systems allowing more extensive roots, longer
hours, and increased reliability.
Autonomous driving integrates modern, upcoming
generations, and older generations of society because of the
combination of computers with mechanically-driven cars.
Automobiles have been around since before our grandparents
can remember, but LIDAR is a relatively new technology.
Each generation is more familiar with each technology,
respectively.
Therefore,
intragenerational
and
intergenerational equity is possible because both young and
old people have something to relate to and be excited about.
This first step towards sustainable development is exciting
because it promotes decision-making towards environmental
and economical productiveness. Once society understands the
benefits of autonomous driving, the sustainable
characteristics of the topic will result in forward thinking and
actions from society towards a result of a cleaner
environment and better overall quality of life.
Holistically the basics of transportation will be redefined
at the most acute level when all types of transportation are
automated. People will eventually never know the
inconvenience of a full parking lot when they’re car could
have driven itself to the next town and back by the time they
were done in the store and ready to be picked up at the door.
This technology will also revolutionize the preconceived
stereotypes of a two, three, or even four car family when one
car could theoretically replace all four. The improvements for
society are boundless when introduced to the autonomous
vehicle. The next hardest part will be convincing the world
that autonomous vehicles are a safer, cleaner, more efficient
mode of transportation compared to their current daily
commute.

 Stephen Gian
Garrett Davey

WHY LIDAR IS SO IMPORTANT
Throughout history, cars that drive themselves have always
been something out of a science fiction movie or a children’s
cartoon. Autonomous vehicles have been projected to be a
thing of the distant future; something humans will not
achieve until the time of the Jetsons. In a way, common
media has created a stigma around the idea of self-driving
vehicles, that they are something fun to think about, but not
realistic or productive. However, modern developments in
LIDAR systems suggest that this is not necessarily the case.
Autonomous driving using LIDAR is a perfect example of
how engineers use science for real-world applications.
Although LIDAR may seem very complicated and futuristic
to the average person, the physics behind it are easy to
understand. The simple scientific idea of using a laser, sensor,
and basic kinematics equation to calculate the distance to a
certain point in space is the basis of 3D mapping. This
kinematics equation is simple enough for a regular nonscientist to understand, when explained in layman’s terms.
With this background knowledge, the idea of a 3D map
generated by a rapidly spinning laser and sensor system can
be visualized.
If the average person can visualize the idea that an
engineered device can visualize and interpret the world the
same way humans do, understanding autonomous vehicles is
not far away. The reason why all of this is important is
because the ability of society to understand something in the
scientific community makes that topic seem more relevant
and less futuristic. The truth is that we are not far away from
commercializing and normalizing self-driving cars, and while
the scientific community may understand this, the average
person might still think this concept is irrelevant. That’s why
all this matters.
LIDAR is the key to the normalization of the autonomous
vehicle industry. Research that results in the advancement of
LIDAR systems not only strengthens the technology, but
creates solutions to ethical dilemmas that come along with
autonomous technology. Eventually, we will get to a point
where most or all automobiles are equipped with some type
of LIDAR system that will provide the vehicle with either
Advanced Driver Assistance Systems or some type of selfdriving or autopilot mode. The benefits to society of this
revolution are endless; some of the benefits include safer
streets and highways, time-efficient traffic, and reduction in
pollution, just to name a few. LIDAR has the potential to
change the automobile industry and our lives for the better,
and we’re just steps away from the peak of the revolution.

SOURCES
[1] K. Steinmetz. “Forget the Distant Future, Smarter Cars
Are Already Here.” Time Magazine. 4.7.2016. Accessed
1.10.2017.

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XS&ContentCustomer=dGJyMO7f8oy549%2BB7LHfi
%2B4A
[2] “Advanced Driver Assistance Systems (ADAS).”
AutoBlog 1.1.2016. Accessed 1.10.2017.
http://www.autoblog.com/driver-assist-technology/
[3] S. Boehmke. Conference call on Zoom. Hardware
Engineer. Uber Technologies, Inc. Advanced Technology
Group. Pittsburgh, Pa. 2.2.2017.
[3.5] Abboud, Sarah. “Self-Driving Uber Sensor Suite”. Uber
ATG.
[4] J. Ku Kim, J. Wook Kim, J. Hyung Kim, T. Hyung Jung,
Y. Jun Par, Y. Ho K, and S. Jung. “Experimental Studies of
Autonomous Driving of a Vehicle on the Road Using LiDAR
and DGPS”. 10.16.2015. Accessed 1.26.2017.
http://ieeexplore.ieee.org/document/7364852/
[5] Plungis, Jeff. “Driving Into the Future.” Consumer
Reports. 4.2017. p.13-17.
http://www.consumerreports.org/autonomous-driving/selfdriving-cars-driving-into-the-future/
[6] L. Ye, G. Zhang, Z. You. “A 2D Resonant MEMS Scanner
with an Ultracompact Wedge-like Multiplied Angle
Amplification for a Miniature LIDAR Application.” Sensors,
2016 IEEE. Accessed 1.26.2017.
http://ieeexplore.ieee.org/document/7808932/
[7] “On the Road.” Waymo. Accessed 3.2.2017.
https://waymo.com/ontheroad/
[8] D. Kiley. “EPA Firms to Move up Fuel Economy
Regulations Before Trump Takes Office.” Forbes.
11.30.2016. Accessed 3.2.2017.
https://www.forbes.com/sites/davidkiley5/2016/11/30/obamas
-epa-moves-to-firm-up-fuel-economy-regs-before-trumptakes-office/#2f1a6ce6c482
[8.5]
“HDL-64E”.
Velodyne
Lidar.
http://velodynelidar.com/hdl-64e.html
[9] L. Greenemeier. “Driverless Cars Will Face Moral
Dilemmas.” Scientific American. 6.23.2016. Accessed
3.1.2017.
https://www.scientificamerican.com/article/driverless-carswill-face-moral-dilemmas/
[10] C. Swirko. “Nation’s First Known Self-Driving Car
Fatality Happened in Williston.” Gainesville.com 6.30.2016.
Accessed 1.10.2017.
http://www.gainesville.com/news/20160630/nations-firstknown-self-driving-car-fatality-happened-in-williston
[11] A. Asvadi, C. Premebida, P. Peixoto, U. Nunes. “3D
Lidar-based Static and Moving Obstacle Detection in Driving
Environments: An Approach Based on Voxels and MultiRegion Ground Planes.” 7.11.2016. Accessed 1.26.2017.
http://web.a.ebscohost.com/ehost/command/detail?
sid=f4847929-1358-4029-9297-360aafadbffb
%40sessionmgr4009&vid=20&hid=4209
[12] “New-Car Transaction Prices Up 2 Percent In March
2016, Along With Increases In Incentive Spend, According

 Stephen Gian
Garrett Davey
To Kelley Blue Book.” Kelley Blue Book. 4.1.2016.
Accessed 2.28.2017.
http://mediaroom.kbb.com/new-car-transaction-prices-up-2percent-march-2016
[13] E. Ackerman. “Lidar that will Make Self-Driving Cars
Affordable.” 10.14.2016. Accessed 2.28.2017.
http://rt4rf9qn2y.search.serialssolutions.com/?
genre=article&title=IEEE%20Spectrum&atitle=Lidar
%20that%20will%20make%20self-driving%20cars
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[14] Rio+20 United Nations Conference on Sustainable
Development. Accessed 23.30.2017.
https://sustainabledevelopment.un.org/rio20.html

ADDITIONAL SOURCES
J. K. Gurney. “Crashing into the Unknown: An Examination
of Crash-Optimization Algorithms Through the Two Lanes of
Ethics and Law.” 3.8.2016. Accessed 1.10.2017.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2622125

C. H. Jang, C. S. Kim, K. C. JO and M. Sunwoo. “Design
Factor Optimization of 3D Flash Lidar Sensor Based on
Geometrical Model for Automated Vehicle and Advanced
Driver Assistance System Applications.” International Journal
of Automotive Technology, Vol. 18, No. 1, 147-156.
10.12.2016. Accessed 1.26.2017.
http://link.springer.com/article/10.1007/s12239-017-0015-7

ACKNOWLEDGEMENTS
We would first like to thank Dr. Daniel Budny for putting
together the Swanson School of Engineering First Year
Conference and giving us the priceless opportunity to
establish ourselves in the engineering community. We also
would like to thank our conference co-chair, Danielle
Broderick, and conference chair, Jared Andes for guiding us
throughout the writing and preparation processes. We would
like to thank Emma Solak and Amanda Brandt in the Pitt
Writing Center for getting us started and giving us helpful
advice for our paper. We’d also like to say thank you to our
friends and family for believing in us, and taking the time to
travel to Pittsburgh to watch our engineering careers blossom.
Finally, we would like to give special thanks to Scott
Boehmke, a Hardware Engineer at Uber Technologies, Inc.
who allowed us to interview him with questions and quote
him in our paper.