Conference Session B8
Paper 76
Disclaimer—This paper partially fulfills a writing requirement for first year (freshman) engineering students at the University
of Pittsburgh Swanson School of Engineering. This paper is a student, not a professional, paper. This paper is based on publicly
available information and may not provide complete analyses of all relevant data. If this paper is used for any purpose other
than these authors’ partial fulfillment of a writing requirement for first year (freshman) engineering students at the University
of Pittsburgh Swanson School of Engineering, the user does so at his or her own risk.
ROTARY LiDAR SENSORS FOR USE IN FULLY AUTONOMOUS VEHICLES
Josh Vinoya, [email protected], Sanchez, 5:00, Andrew Parker, [email protected], Mena Lora, 1:00
Abstract - Notable innovations in the automotive field help
reduce human error by monitoring and regulating a car’s
internal systems in order to protect riders from preventable
accidents. The next significant improvement in automobile
safety will come from real-time digital image renderings
provided by 3D Light Detection and Ranging (LiDAR) sensors.
Velodyne’s model HDL-64E LiDAR sensor is a candidate for
use in fully autonomous driving because its patented rotary
head can generate up to 2.2 million data points every second,
while still being compact enough to fit on a car’s roof. Using
the HDL-64E as a data source, engineers are able to program
its on-board computer system safely guide it through
challenging and diverse environments.
A complete understanding of how the model HDL-64E
works was collected using the product guide, manual, and 3D
CAD models. Labeled diagrams and detailed explanations of
the sensor’s components are provided for the reader. The
paper then notes the sensor’s application and significance to
autonomous driving, with emphasis on recent publications due
to the ongoing innovations within this topic. Finally, the paper
presents and discusses the ethical dilemmas within this field to
complete the investigation into LiDAR sensors for use in selfdriving cars in addition to its potential role in improving the
quality of life.
Key Words—Automobile Safety, Autonomous, HDL-64E,
LiDAR Sensor, Self-Driving, Velodyne
LiDAR SENSORS AND SELF-DRIVING
CARS
Many similarities can be drawn between LiDAR systems
and radar. Instead of using radio waves however, the lasers
within the LiDAR housing emit a carefully timed pulse of light
which bounces off an object and is returned to the sensor.
Velodyne’s HDL-64E gathers millions of these data points
every second, generating a very dense point-cloud of
information which is then fed into the car’s on-board
computer, allowing it to safely navigate through dangerous
environments.
There were more than 6 million car accidents on US
roads last year, with distracted driving as the number one cause
of crashes [1]. LiDAR sensors are the future of fully
autonomous driving, with the power to considerably reduce the
University of Pittsburgh Swanson School of Engineering 1
Submission Date 10.02. 2017
tragic results that often accompany these accidents by having
a significantly lower accident rate than even the most
experienced driver. Automobiles affect all of us almost every
day, and eliminating human error from the dangerous task of
driving has the potential to save thousands of lives every year.
It is always important to consider ethics when new
technologies are created. The ethical dilemmas surrounding
autonomous driving are complicated because although it will
save lives, accidents will occur and the liability for those
accidents is still unclear. This paper takes an in-depth look at
the technology itself, the application of LiDAR to autonomous
driving, the significance of self-driving cars, and the ethics
behind them.
SCIENCE BEHIND LiDAR SENSORS
Light Detection and Ranging (LiDAR) sensors are based
around time-of-flight measurements to generate a digital
image of the sensor’s surroundings. A 2016 article in
Automotive Industries titled Laser vision for self-driving cars
details how LiDAR sensors function, as well as their role in
driver assistance systems [2]. It describes how the sensor emits
a carefully timed pulse of light which exits the sensor, hits an
object, then reflects back to a detector panel. The HDL-64E
emits a Class 1 Eye-Safe laser with a wavelength of 903nm
[3]. (A Class 1 Eye-Safe laser is a sound choice for use in this
application because it does not damage human eyes unless an
iris is exposed to it for an extended period) The sensor then
knows the time of flight of the light, as well as the speed of the
light (299,792,458 m/s). It calculates the distance to the object
using the equation
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦 ∗ 𝑇𝑖𝑚𝑒
or
𝑥 =𝑣∙𝑡
[Eq. 1]
[Eq. 1]
The computer within the sensor makes millions of these
calculations every second in order to generate a dense pointcloud of information about its environment. The HDL-64E has
64 of these carefully aligned lasers inside its housing [4].
Thirty-two reside in the upper half, and thirty-two are in the
lower half. Currently, one of the main factors contributing to
the high cost of the sensor is the extensive manual labor that is
required to install, and then align the lasers [5]. The materials
Andrew Parker
Josh Vinoya
themselves are not the largest cost when constructing the
sensors.
According to Velodyne’s product manual page, the HDL64E can generate up to 2.2 million data points every second in
dual return mode, or 1.3 million every second in singular return
mode [3]. The sensor has a range of up to 120 m and a typical
accuracy of ±2 cm [3]. This results in an extremely detailed
point-cloud of data to be used in the car’s on-board computer
systems.
The HDL-64E can detect objects from +2.0o to -24.9o for
an impressive vertical field of view of 26.9o. Shown below is
Velodyne’s model HDL-64E S3. As shown below, it stands
283 mm tall and has a diameter of 223 mm. Because of its
design, it can easily be secured to almost any vehicle without
needing to significantly modify the vehicle’s construction.
driving. While they are adequate in basic pattern recognition,
an IEEE article discussing On Board 3D Object Perception in
Dynamic Urban Scenes explains that they are prone to error in
low illumination and diverse weather conditions [6]. In
addition, they provide only a video feedback, not a 3-D point
cloud of data, so the computer systems make mistakes more
frequently than those powered by LiDAR sensors. Flash
LiDAR systems are smaller, stationary units that are placed in
several locations around the outside of a vehicle [2]. According
to an Automotive Industries article, these systems operate by
sending out a flash of light and registering each pixel as it
returns to the sensor [2]. This is a more cost-effective solution,
however they do not gather as much data as rotary LiDAR
systems, and also are more prone to blind spots. These sensors
are currently being used for driver-assistance systems such as
adaptive cruise control and self-parking vehicles. However, to
be used in fully autonomous driving, the sensor must be
powerful enough to provide enough data for the car to make
the thousands of decisions every second necessary to operate a
motor vehicle.
APPLYING LiDAR TO AUTONOMOUS
DRIVING
A sensor of complex caliber will undergo a series of
linear steps set by a scripted code that will command the
computer to execute the commands at a given moment. To be
seriously considered for use in autonomous driving, a sensor
must be able to provide enough information to that computer
system for it to be able to detect and identify its surroundings,
classify the type of situation that the automobile is in, and
execute the specific command prompted. We will focus on the
process of surveying and detecting undertook by the LiDAR
sensor.
FIGURE 1 [3]
HDL-64E Rotary LiDAR Sensor
The sensor can operate effectively in the temperature
range of -10oC to +65oC [3]. This allows for it to be used in
many driving conditions, and thus there is not a great danger
of it failure due to a temperature malfunction in regular use.
The HDL-64E is a rotary LiDAR sensor, so its patented
rotating head can spin anywhere from 300 RPM (5 Hz) to 900
RPM (20 Hz), allowing the user to select the density of the data
point cloud, and therefore the image resolution created. A
slower rotation speed will result in a denser point cloud, while
a faster rotation speed will have a higher refresh rate. The
sensor will continue to gather the same number of data points
per second independent of spin rate. An important advantage
of the rotary design is the horizontal field of view of 360o. In a
SAE International article entitled “Lower-Cost LiDAR is Key
to Self-Driving Future,” Bradley Berman proposes rotary
LiDAR sensors as the best choice for autonomous driving. [5]
He compares the sensor to a castle built on top of a hill. “The
king can see who’s coming from any direction.”
Mechanical Components
In order for LiDAR to be fully functional and useful in
modern automobiles, the mechanical aspect of the sensor must
meet high demands. The sensor has to be able to scan the whole
image in real-time, therefore needing the mechanical
capability of being able to spin so that it can image up to two
million data points using its 64 lasers [3]. The Velodyne HDL64E, in particular, has 64 individual lasers to accurately scan
and pinpoint different points onto its laser printed image,
giving accurate recitations of the situation. The sensor then
feeds this wealth of information to the car’s on-board
computer. The more information that the car has about its
surroundings, the better it can navigate through them. This
point-cloud of information generated by the HDL-64E is the
first step in the automated driving process, and without these
powerful LiDAR sensors, fully autonomous driving would not
be possible.
Other Options for Data Acquisition
Currently, the use of one singular rotary LiDAR sensor is
generally accepted as the future of the fully autonomous
driving industry, however there are two other main schools of
thought for use in the data acquisition; conventional camera
systems and flash LiDAR. Conventional camera systems were
the first generation of sensors to be used in autonomous
2
Andrew Parker
Josh Vinoya
The dark circle directly surrounding the vehicle is its
blind spot where the sensor cannot see. That is one of the main
disadvantages to using a singular sensor located on the car’s
roof; it cannot see anything directly beside the car. One
potential solution is to use several smaller flash LiDAR
sensors in conjunction with the HDL-64E. They would help to
fill in the blind spot accompanying a rotary sensor. Consumers
will desire an overwhelming sense of safety when first
purchasing self-driving vehicles, and the redundancy provided
by using additional sensors will provide that peace of mind.
Image Computations
The LiDAR sensor must be able to analyze its
surroundings, much like a human eye surveying the road, and
store its 3-D map in its memory for analyzation [7]. Because
3-D mapping of unknown objects cannot be analyzed by the
sensor itself, the sensor will identify objects based on preexisting 3-D models of objects set into the computer through
algorithms in its code. For example, a tree would be identified
by the sensor when it matches the 3-D model with the preset
3-D model coded into the computer [6]. Engineers are also
working on “deep learning” neural networks that learn as they
drive, which will help to account for the erratic nature of other
drivers. The more time those cars spend on the roads, the better
they will become at adapting to other car’s behavior and
keeping their passengers safe.
Next, a preset laser-scanned image is put through a series
of modeling techniques that will enhance the computer’s
understanding of the situation that it is currently driving in.
The modeling technique utilized by the Velodyne HDL-64E is
called Two-Level Adaptive Grid Modeling (L2 AGM) [6].
This entails that after the LiDAR detector scans its surrounding
and takes the flash image into its memory, it will then separate
all of the miniscule image pixels and will classify the
background of the image first. This multistep-process will
allow the computer to completely digest the information. The
background is then put into a two-dimensional Cartesian
coordinate plane and is evenly distributed onto the graph as an
array of mathematical values representing the background
objects [7]. Once the geographical input is set, the system will
then take the rest of the laser images as smaller points within
the bigger background. This will be used to process and
execute the command set for the car to take.
Below is an example of the data input that would be fed
into the computer system from a single HDL-64E LiDAR
sensor. The road surface can be clearly seen, as well as the
other cars on the road and any obstacles that the vehicle has to
navigate around. This image illustrates just how much data can
be quickly gathered using the HDL-64E.
Erratic Human Driving
The hardest part of programming an autonomous vehicle
is accounting for other humans driving erratically. A computer
does not know how to react to a driver making a right hand
turn from the left hand lane because it defies all logical
guidelines. City driving is especially difficult because of the
influx of pedestrians, sharp turns, and other vehicles often
stuck in a gridlock. Another driver may wait until 100 feet
before their turn to get into the proper lane. This leaves very
little time for the computer to sense that the other car is shifting
lanes and adjust its speed accordingly. Self-driving cars are
currently being heavily tested in Pittsburgh because it was
deemed one of the most difficult cities to drive in. There, the
vehicles would run into many situations in which they would
not know how to respond, making it a good city to learn in.
Also, LiDAR sensors struggle to drive on bridges due to the
lack of background cues. This is one of the problems that
engineers are currently working on improving for future
generations of autonomous driving.
SIGNIFICANCE OF FULLY AUTONOMOUS
VEHICLES
According to a Lawcore article on car accident statistics,
someone dies because of a car accident every 12 minutes and
someone else is injured every 14 seconds, with distracted
driving being the number one cause of accidents [1]. In 94%
of collisions, driver error is the problem [8]. Driving is an
extremely serious task; however, many underestimate the
danger associated with it because they have driven thousands
of miles in the past, and believe themselves to be superior
drivers. As phones and computers worm their way deeper and
deeper into our lives, thousands of drivers are putting
themselves and others at risk every day. Self-driving cars have
the potential to drastically reduce the number of traffic
fatalities each year by eliminating human error from the
equation of driving. A LiDAR sensor in conjunction with an
advanced on-board computer system can make tens of
thousands of decisions every second and react much faster than
a human. So far, in every accident that self-driving cars have
been involved in, the crash was the result of another human
driver, not the autonomous vehicle. The majority of those
crashes were the autonomous vehicle getting rear-ended. In
FIGURE 2 [4]
Sample Data Point Cloud
3
Andrew Parker
Josh Vinoya
congestion will cost $192 billion” [9]. With these astounding
figures, the ties to economic sustainability also begin to
emerge. Since there is such a substantial waste associated with
daily drive times, there is a definite economic goal when
researching improvements in LiDAR. Companies are willing
to spend the time and resources to further this technology
because it truly does solve a problem that millions of
Americans face every day.
addition, every American wastes dozens of hours every year
driving. Autonomous vehicles could allow for people to be
much more productive behind the wheel.
The true significance of fully autonomous driving
depends entirely on how deep the implementation becomes. A
highway with just one or two self-driving vehicles would
improve the safety for those few passengers, as well as any
surrounding cars. Imagine for a moment however, a highway
completely filled with autonomous vehicles. Vehicles that
could potentially communicate with each other via Bluetooth.
This is the scenario that has the potential to save thousands of
lives every year by making driving safer. This scenario is not
in our near future however. There are still millions of miles
that autonomous vehicles must drive with technicians behind
the wheel, ready to take action, before they become available
for commercial use.
OTHER APPLICATIONS OF LiDAR
The impressive data-gathering power of LiDAR sensors
had drawn attention from areas outside of the autonomous
driving industry. For example, LiDAR is currently being used
by the National Ocean Service to examine and map the surface
of the earth [10]. In an article published by the U.S.
Department of Commerce entitled “What is LiDAR?”, other
applications such as shoreline mapping, hydrodynamic
modeling, and coastal vulnerability analysis are explained
[10]. They use LiDAR sensors in conjunction with an
integrated GPS system to assign a unique 3-D coordinate to
each data point collected; latitude, longitude, and elevation. In
addition, they can measure the seafloor using bathymetric
LiDAR sensors that emit green light to permeate the water.
Another important application is the mapping of large
scale urban environments. According to an Institute of Electric
and Electronics Engineers article entitled “GPS Precision
Time Stamping for the HDL-64E LiDAR Sensor and Data
Fusion,” efficient mapping of urban areas is possible through
the conjoined use of LiDAR sensors and differential GPS
systems [7]. After their internal clocks are aligned, a vehicle
can drive the GPS and sensors through complicated areas and
gather an extremely rich data set from which an incredibly
detailed 3-D model of the area can be reconstructed. This
would be extremely helpful to city planners, traffic workers,
and emergency response teams.
Social and Economic Sustainability
Often, sustainability is simply taking a step back and
trying to see how a concept will interact with the world when
put into practice. The idea of sustainability, however, can be
viewed from many different angles, as it is such a broad topic.
The most areas of sustainability which most readily apply to
autonomous driving are social and economic sustainability.
Social sustainability can be defined as assisting to
maintain or further the wellbeing of society. A product that is
socially sustainable would be one that improves the quality of
life of those using it. Velodyne’s LiDAR, and autonomous
driving in general would aid in improving the quality of life in
a multitude of ways. First, as the number of self-driving cars
on the road increased, the number of automobile deaths would
decrease. This is due to the proven safety record of
autonomous vehicles. As computers began to take over the task
of driving from humans, driver error could begin to slowly be
eradicated as a cause of crashes. It is impossible to think that
there will ever exist a world with no automobile fatalities,
however the current numbers are likely to see a harsh decline
following the widespread implementation of self-driving
vehicles. In addition, autonomous vehicles would improve the
quality of life for millions by reducing the stress and time
wasted stuck in traffic. A thirty-minute commute to work may
seem insignificant, however that quickly adds up to 250 hours
a year. (Note two weeks are subtracted due to vacation time,
illness etc.)
𝟏
𝟐
ETHICAL IMPLICATIONS
The ethical dilemmas faced by the autonomous driving
industry are a very controversial topic. In this section, we will
propose a scenario in which the car is forced to make a decision
that will have a significant impact on the lives of not only its
passengers, but others as well. We will then discuss the
different schools of thought on what the car should do in that
situation, as well as who would be responsible in the event of
the accident. Before the widespread implementation of fully
self-driving cars, a standard set of ethical guidelines must be
agreed upon.
In a TED-Ed talk on “The Ethical Dilemma of SelfDriving Cars”, Patrick Lin proposes a scenario in which an
automated vehicle is driving on the highway [11]. The car is
boxed in on all sides. Suddenly, a large box falls off of the
truck in front of it and the car does not have time to stop, so
the car must make a decision; swerve left into a SUV, or
𝒅𝒂𝒚𝒔
𝒉𝒐𝒖𝒓 × 𝟐 𝒕𝒓𝒊𝒑𝒔
× 𝟓 𝒘𝒆𝒆𝒌
× 𝟓𝟎 𝒘𝒆𝒆𝒌𝒔
= 𝟐𝟓𝟎 𝒉𝒐𝒖𝒓𝒔
𝒅𝒂𝒚
𝒚𝒆𝒂𝒓
𝒚𝒆𝒂𝒓
[Eq. 2]
Adults who work for 40+ years of their life are throwing
away weeks and weeks of time that could be better spent
working, learning, or just relaxing. The problem isn’t getting
any better either. An ABC news article predicts that by 2020,
“the total nationwide delay time will grow to 8.3 billion hours
-- an increase of 1.4 billion hours in just 5 years -- and
4
Andrew Parker
Josh Vinoya
swerve right into a motorcycle. If the car swerves into the
motorcycle, it will protect the passenger of the car but most
likely kill the defenseless cyclist. If it swerves left, the collision
will be less severe, however a SUV may have multiple
passengers, putting more lives at risk. Finally, it could not
swerve at all and directly collide with the box, potentially
sacrificing the passenger’s life, but not putting any others in
harm’s way [8].
Patrick points out that if a human were driving the car,
whichever way they swerved would be accepted as just a split
decision, and they would not be blamed for choosing one over
the other. They had no time to think about it; their choice was
just an instantaneous, panicked reaction [11]. This is a strong
contrast with a decision that would be made prior to the event.
In autonomous driving, a programmer must tell the computer
that given this specific set of inputs, it should do a certain
output. The result of who lives and who dies could be decided
months, or even years beforehand by how the computer system
was programmed to respond. This is where the true ethical
dilemmas of computer-assisted driving arise. Autonomous
vehicles would make the roads overall safer, however those
who are injured would be chosen beforehand. The Talk
concludes by stating that the autonomous driving system is
working as a “targeting algorithm” [11]. It is favoring certain
types of objects to crash into over others. Should a car swerve
into a motorcyclist who is wearing a helmet, or one with no
helmet? The one with the helmet is more likely to survive,
however the algorithm is punishing the cyclist for being more
responsible. This, and many other complicated situations arise
when discussing how to program computers for use in
autonomous driving.
The programmers must compile a list of precedence and
definitively state for example that a 20% chance of saving the
life of person X is more important than a 60% chance of
injuring person Y. Also, should an autonomous vehicle take
the life of its passengers as more important if it knows
information about them? For example, a mother driving five
young children to soccer practice versus a senior citizen
driving by themselves. Then the question arises of who should
make these decisions. Even seemingly simple guidelines such
as “minimize harm” break down quickly when these complex
situations are proposed [8]. Currently, we as a society have no
definitive answer. As technology continues to accelerate at
unprecedented rates, ethics will become more and more
important in how we navigate these uncharted waters.
expensive than most cars themselves, and thus very
impractical for commercial use. Dr Wolfgang Juchmann,
Velodyne’s director of sales and marketing, believes that
LiDAR will follow a similar downward trend in price to radar
[5]. “If you look at 10 or so years ago, radar sensors were
$10,000. I’m sure the first people said, ‘radar? Putting six of
those on every car is completely unbelievable. But everybody
saw the benefits. The market got there, and the price came
down” [5]. The current limiting reactant in terms of the cost is
the labor required to build the sensors. There is nothing about
the materials that will keep the price above acceptable
consumer levels. There is no excess of raw materials, or
hazardous components associated with the construction of the
sensors. Thus, the worldwide implementation of LiDAR
would not put an excessive drain on the environment. As
market demand continues to rise as self-driving cars become
more popular, the cost will decline as well. In addition, this
problem is not unique to LiDAR. Every technology from the
combustion engine to the light bulb has a period where it is at
first impractical for widespread use. As years go by and new
advancements get made, the price of LiDAR will continue to
fall, so the question is not IF the technology will be made
available for commercial use, but WHEN.
Also, the sensor needs to get smaller. The HDL-64E,
while powerful, is not very aesthetically pleasing. Consumers
may be much more willing to purchase a fully autonomous
vehicle if the sensor was smaller and less bulky. The high cost
and the bulky nature of the HDL-64E are the two main
challenges facing the autonomous driving industry currently.
THE FUTURE OF DRIVING
The substantial data-gathering power of LiDAR sensors
has propelled them to the forefront of technology in many
disciplines. Velodyne’s model HDL-64E rotary LiDAR sensor
is currently used for fully autonomous driving applications.
The rotary head, in conjunction with the 64 independent
channels allow for an extremely dense 3-D point cloud of
information to be gathered, and then used in the car’s computer
system. If implemented on a large scale, fully autonomous
vehicles have the potential to save thousands of lives every
year. Finally, accidents, though uncommon, will still occur,
and the actions to be undertaken, as well as who is responsible
for them is still a topic of debate within our society.
SOURCES
WHAT’S NEXT FOR LiDAR
[1] “Car Accident Statistics.” Lawcore.com. 2016. Accessed
1.8.2017.
http://www.lawcore.com/caraccident/statistics.html
[2] C. Parish. “Laser vision for self-driving cars.” Automotive
Industries.
12.2016.
Accessed
1.8.2017.
http://web.a.ebscohost.com/bsi/detail/detail?vid=5&sid=5c69
8436-5603-4251-8880-
Every day, engineers are learning more about how to
improve LiDAR systems and make them safer for use in fully
autonomous vehicles. While they are currently very
technologically advanced, some improvements still need to be
made before their widespread implementation. In order for
LiDAR to be put into use in fully autonomous, commercial
vehicles, the cost of the sensor needs to come down. Right
now, the HDL-64E retails for $75,000 [5]. This is more
5
Andrew Parker
Josh Vinoya
763dfb7150b2%40sessionmgr4010&hid=4201&bdata=JnNp
dGU9YnNpLWxpdmU%3d#AN=120239294&db=bth
[3] “Velodyne LiDAR HDL-64E High Definition Real-Time
3D LiDAR.” Velodyne LiDar. 2016. Accessed 1.9.2017.
http://velodynelidar.com/docs/datasheet/63-9194%20RevE_HDL-64E_S3_Spec%20Sheet_Web.pdf
[4] T. Deyle. “Velodyne HDL-64E Laser Rangefinder PseudoDisassembled.” Hizook Robotics News for Academics &
Professionals.
1.4.2017.
Accessed
2.25.2017.
http://www.hizook.com/blog/2009/01/04/velodyne-hdl-64elaser-rangefinder-lidar-pseudo-disassembled
[5] B. Berman. “Lower-cost LiDAR is key to self-driving
future.” Automotive. 11.2.2015. Accessed 1.9.2017.
http://articles.sae.org/13899/
[6] A. Borcs. “On board 3D object perception in dynamic
urban areas.” IEEE Xplore Digital Library. 2.12.2013.
Accessed
1.8.2017.
ieeexplore.ieee.org.pitt.idm.oclc.org/document/6719301/
[7] A. Moreno. “GPS Precision time stamping for the HDL64E LiDAR sensor and data fusion.” IEEE Xplore Digital
Library.
19.11.2012.
Accessed
1.8.2017.
ieeexplore.ieee.org.pitt.idm.oclc.org/document/6524554/
[8] D. Muller. “The Real Moral Dilemma of Self-Driving
Cars.” Veritasium. 1.19.2017. Accessed 1.25.2017.
https://www.youtube.com/watch?v=WBjY3QGNdAw
[9] E. Dooley. “Here’s How Much Time Americans Waste in
Traffic.” ABC News. 8.26.2015. Accessed 3.29.2017.
http://abcnews.go.com/US/time-americans-wastetraffic/story?id=33313765
[10] “What is LiDAR?” National Ocean Service. 5.29.2015.
Accessed
1.9.2017.
http://oceanservice.noaa.gov/facts/lidar.html
[11] P. Lin. “The ethical dilemma of self-driving cars.” TED
Ed. Accessed 1.25.2017. http://ed.ted.com/lessons/the-ethicaldilemma-of-self-driving-cars-patrick-lin#watch
[12] M. Staff. “Ford, Baidu invest $150 million for LiDAR
sensors to improve self-driving cars.” Christian Science
Monitor.
8.16.2016.
Accessed
1.9.2017.
http://web.b.ebscohost.com/ehost/detail/detail?sid=bef5b6ada94f-45ef-950dff6626287404%40sessionmgr106&vid=0&hid=101&bdata=J
kF1dGhUeXBlPWlwLHVpZCZzY29wZT1zaXRl#AN=1174
75944&db=aph
ACKNOWLEDGMENTS
We would like to take the time to thank the tireless efforts
of our both our conference chair and co-chair. Their dedication
and passion for engineering, as well as for helping others
helped our paper on a grand scale. We would also like to thank
the writing center and library helpers for their detailed
explanation of the assignment set forth before us, as well as for
answering questions as they arose throughout the process.
Without the aforementioned persons, this paper would be
lacking in both quality and direction.
6