B8
Paper #114
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 be 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.
THE USE OF COLLISION AVOIDANCE SYSTEMS IN AUTONOMOUS CARS
DURING EMERGENCY SITUATIONS
Daniel Quiroga, [email protected], Budny, 10:00am, Joshua Ryan, [email protected], Budny, 10:00am
Abstract — For autonomous cars to find a place on today’s
roads, developers must ensure the safety of onboard
passengers. One of the main ways that developers will keep
passengers safe is through the utilization of collision
avoidance systems. This research paper compares various
approaches developers take to collision avoidance systems in
autonomous cars, specifically when dealing with emergency
situations. The first approach uses both Model Predictive
Control (MPC) and Electronic Stability Control (ESC)
algorithms to safeguard car riders in these dangerous
situations. This method focuses on sensor data from
surroundings to make clear, calculated decisions in terms of
steering. An alternative approach involves the Markov
Decision Process (MDP) to steer the vehicle. This approach
centers around continuously optimizing the car’s position on
the road based on the updating sensor data. Yet another
approach considers the people surrounding the car so that
both the passengers onboard and the pedestrians can remain
safe when lives are put into question. This paper also
addresses some of the speculation surrounding autonomous
cars and speaks to ethical dilemmas that might occur on the
roadways during these difficult situations. Finally, this paper
shows how collision avoidance systems in autonomous cars
benefit sustainability in society.
Key Words—Collision Avoidance, Electronic Stability
Control, Evasive Maneuvers, Markov Decision Process,
Model Predictive Control, Sustainability.
BRINGING SAFETY TO THE ROADS OF
TOMORROW
As autonomous cars enter the public roadways, these
vehicles must guarantee the safety of onboard passenger. The
street-legal autonomous vehicles being sold commercially
today tackle the issue of safety using various central control
systems, but the concern remains on how these programmed
controls react in dangerous crash scenarios. The
unpredictability of human driving on top of high speeds in
crash sequences pose developers the problem of altering
trajectories just enough to keep both onboard passengers and
the oncoming party safe. This collision avoidance challenge
in times that both parties cannot be saved becomes more of an
University of Pittsburgh Swanson School of Engineering 1
3.3.2017
ethical question as to which lives are more valued by the
autonomous vehicle and therefore by its developer. The aim
of the autonomous vehicle is to better the quality of driving
on the road, and ultimately better the quality of life and bring
further safety and sustainability to society. However, in
critical situations, drivers question the adaptability of the
vehicle and even may prefer to take collision avoidance into
their own hands. Through the use of collision avoidance
systems in emergency situations—specifically Model
Predictive Control (MPC), Markov Decision Process (MPC),
and evasive maneuver systems—the autonomous car will
improve upon the possible human errors while driving.
HOW AUTONOMOUS CARS THINK
Model Predictive Control
One approach to avoiding collisions in emergency
situations is the use of Model Predictive Control (MPC) and
Electronic Stability Control (ESC) systems. Model Predictive
Control, specifically, is calculated through a process known
as receding horizon control. The concept behind receding
horizon control centers around approximate calculations of
input data over short time spans to dictate the autonomous
car’s route planning. Luigi del Re and his team of mechanical
engineers at Johannes Kepler University in Linz, Austria
describe MPC and receding horizon control, “MPC does not
solve the general optimal control problem, but yields an
approximate ‘receding horizon’ solution. This approximate
solution is in many cases close to the real optimal solution at
each time instant. The receding horizon optimal sequence of
control inputs to the plant is computed over a limited number
of steps to minimize a cost function under constraints, but
only the values corresponding to the next sample time are
used” [1]. This methodology of approximate calculation
allows for quicker processing and shorter time intervals as
opposed to optimal calculations every time interval.
Joshua Ryan
Daniel Quiroga
FIGURE 1 [1]
MPC system chart
As shown in Figure 1 above, the overall MPC system is
organized into components, namely: “a system model, a
dynamic optimizer and an evaluation block, which includes
the cost function and the constraints on the inputs, outputs,
and states” [1]. The system model, the pivotal component of
the MPC system, is the core code that defines how the
autonomous car will react to the incoming variable data and
map out trajectories. This element of the MPC model can be
seen as ‘the brain’ of the autonomous car and varies by car
model since each developer programs the source code in a
different fashion. As to complement the system model, the
dynamic optimizer performs nearly constant optimizations to
the variable data as decided upon by the system model. The
cost function, the final aspect of the MPC Controller,
integrates the constraints of the vehicle and the road to the
dynamic optimizer and the system model. This cost function
accounts for maintenance features such as gas, oil, and tire
upkeep on top of the car’s limitations due to maximum
outputs by the car power system. Simply put, the cost function
ensures that the vehicle is in conditions to execute the
decisions produced by the MPC controller unit. The MPC
controller unit as a whole proceeds to deliver the updated and
optimized trajectory to the plant, the device that transmits and
applies the MPC controller’s decision to the vehicle. Lastly,
the state estimator closes the loop by updating the variable
data every time instant so the MPC controller can re-evaluate
a steering decision. This entire process shapes the trajectories
autonomous cars travel and keeps the vehicle away from
possible collisions.
FIGURE 2 [2]
Diagram illustrating variables considered by ESC.
As demonstrated in Figure 2b [2], several dynamic variables
affect the tires’ ability to maintain stability. One of the main
factors that ESC takes into account is speed denoted by V. The
speed of the vehicle determines how extreme the reactive
measures must be from ESC system. For instance, if the
vehicle is traveling at 60 mph while trying to make a turn, the
modifications made by the ESC system to attempt to maintain
balance must be more severe in comparison to a vehicle
making a turn at the speed of 10-15 mph. Similar to velocity,
the turning angle or the sideslip angle represented by alpha 𝝰
considerably affects the force on the tires and thus, the
balance of the car. Figure 2a demonstrates that as the turning
angle increases, the magnitude of the latitudinal force
experienced by the tires increases, placing the car further in
jeopardy. The tire forces do lower after the saturation angle 𝜌
, the angle where the latitudinal force on the tire begins to
revert back into longitudinal force on the tire due to such a
drastic sideslip angle. Besides speed and sideslip angle, the
static variables m, a, and b affect the balance of the vehicle.
The variable m denotes the mass of the vehicle; a represents
the distance from the center of mass to the front tire; b marks
the distance from the center of mass to the rear tire. These
constant variables influence the center of mass of the vehicle
which designates the reference frame for all of the
calculations executed by the ESC system. As these variables
continuously are inputted, the ESC system can establish the
autonomous car’s stability and safety.
j
Electronic Stability Control
Electronic Steering Control (ESC) is, in essence, a
complementary system to the steering command of the car.
As the steering wheel is turned, the forces experienced by the
tire are altered and further place the car at risk of side slipping.
Side slipping is the act of the car sliding latitudinally (left and
right in relation to vehicle) due to insufficient friction between
the tires and road in the horizontal direction. The main
function of this ESC system is to secure the vehicle’s balance
and traction against the road in addition to preventing this
side-slipping action from occurring. The figure below
describes the forces involved in the process.
Fusion of MPC and ESC Models
In non-autonomous cars, the steering command of the car
is the driver’s hands on the steering wheel, so ESC systems
simply serve the purpose of monitoring the tires’ traction to
the road and modifying steering to avoid slipping. However,
in this new age of technology, the ESC system responds to a
steering command dictated by an MPC system. In non-
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threatening situations, the MPC system serves the vehicle by
planning out the possible steering options and trajectories in
the split-second future, while the ESC system examines the
vehicle’s general stability and contact with the road. Under
hazardous conditions, “while [MDP] approaches may
generate a collision free trajectory, it becomes unclear
whether the control actions modified by the underlying ESC
systems lead to a collision-free trajectory for the actual
vehicle” [3]. This dynamic between the two collision
avoidance systems necessitates coordinated modifications
from both systems. These systems, as to ensure the safety of
onboard passengers, must collaborate in steering adjustments
or the car might not avert the danger.
FIGURE 3 [4]
A standard MDP search tree model.
In any type of situation where MDP models are implemented,
there will always be an MDP search tree that will look similar
to figure 3 [4]. Generally, in a MDP search tree (or stochastic
tree) there will be three different groups of variables, as
shown in the figure above. There will be beliefs, denoted by
‘b,’ that serve as the believed and predicted outcomes from
each of the different actions, which are denoted by ‘a’ [4].
Finally, there are observations denoted by ‘o’ that are also
referred to as chance nodes above; these are other variables in
the system that could influence the overall system [4]. This
final group of variables introduces a randomization aspect of
the MDP approach. The decisions made through the
utilization of an MDP approach are partially randomized in
the sense that these chance nodes generate uncertain changes
to the overall system. Through this understanding we see that
MDP is more of a theoretical process than an exact system
like MPC or ESC. Since the MDP approach is always subject
to uncertain changes, the implementation of artificial
intelligence helps significantly because this intelligent
computer software will be able to actively change along with
the system that it is dealing with at the time, thus being able
to make an incredibly close if not the best decision at every
moment in operation [4].
MDP-BASED APPROACH TO COLLISION
AVOIDANCE
Basics of MDP Modeling
The Markov Decision Process (MDP) serves as an
alternative approach to collision avoidance and it involves not
so much an actual system within the autonomous vehicle, but
a theoretical and methodological approach. Various codes
make up MDP, hence it originates from a computer science
school of thought. The thought process behind MDP comes
across a lot simpler than looking at the actual algorithms and
equations that truly define the procedure. Generally, MDP
analyzes numerous variables and determines the best course
of action out of all of the possible opportunities. The primary
goal of MDP is to be able to make decisions faster than
humans can, and this is achieved through the use of artificial
intelligence. Artificial intelligence is computer software that
is programmed to be able to learn and complete operations far
more efficiently than humans can. Therefore, artificial
intelligence comes into play with MDP in that a computer that
is programmed with an MDP approach will be able to sort
through all of the different variables that it receives and come
to a conclusion for the optimal outcome faster than a human
would be able to.
The Brains Behind the MDP-based Approach
When dealing with collision avoidance in autonomous
cars, MDP serves as an extremely viable option whether it be
an alternative or an addition to MPC and ESC systems. After
first researching and learning the basics of MDP and what a
standard MDP approach looks like, an MDP approach to
collision avoidance in autonomous cars especially when it
comes to emergency situations makes perfect sense. Since
situations on the road are always changing, having a system
that is based on an artificial intelligence MDP model would
be able to help substantially towards improving the safety of
human drivers and passengers.
Primarily, the goal for a system based on MDP modeling
is for the autonomous car to be able to react quicker, more
efficiently, and more consistently than human drivers can.
The authors from a research paper conducted at Oklahoma
State University engineering school state that these systems
based on MDP modeling “were developed to surpass the
human in time of reaction or excellence of sensors. Because
of the use of modern detectors and fast computer logic, such
systems had many successful implementations and prevented
up to 80% of simulated collisions” [5]. These types of results
display that MDP modeling could help to advance the
development of collision avoidance in autonomous cars as
well as providing an opportunity to convince customers of the
autonomous car’s safety.
Many people are still skeptical about the consistency of
these autonomous cars and these MDP-based systems truly
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of the vehicle. For this situation, there are “six parameters to
describe the real vehicle and environment:
m : Mass of vehicle [kg]
a : Distance from front axle to Center of Gravity [m]
b : Distance from rear axle to Center of Gravity [m]
Cx : Longitudinal tire stiffness [N]
Cy : Lateral tire stiffness [N/rad]
CA : Air resistance coefficient [1/m]” [5]
To then formulate differential equations for this situation, the
researchers took three different states of the vehicle into
account: Longitudinal velocity [m/s], Lateral velocity [m/s],
and Yaw rate[rad/s]. The first two states refer to the vehicle's
vertical and horizontal velocity—which takes into account
speed and direction—respectively. Yaw rate relates to the
vehicle's angular velocity with respect to its vertical axis.
answer that skepticism. The paper mentioned above provides
an example of a system based on MDP modeling, known as
ALVINN [5]. ALVINN utilizes the 360 view cameras on the
car as well as Neural Networks to formulate its decisions for
the car’s reactions. The cameras are able to gather information
from the system that the car resides in, which will consist of
the road as well as other nearby surroundings such as
sidewalks. From these cameras, the system can obtain the
many variables that will be necessary for the system to be able
to make its decision and react. When thinking back to the
basics of MDP modeling, the main variables required will be:
‘a,’ the actions the car can make, ‘b,’ the beliefs of the
different outcomes depending on the car’s reaction, and ‘o,’
the observations of the surrounding system, specifically the
other cars and more importantly, the improbable movements
of the other cars on the road. These cameras will be greatly
beneficial because the car will be able to see in all 360 degrees
simultaneously, while us humans only have a view of about
half that at any given time. Once these variables are obtained
by the car’s cameras, the Neural Networking will then take
control and make the next steps in the decision-making
process. Neural Networking is where the artificial intelligence
aspect of this system comes into play. In simple terms, Neural
Networking is basically a system of software and/or
computers that are able to gather data, analyze data,
communicate with one another, and make decisions and
commands in response. Since after all, artificial intelligence
exists to execute human tasks faster than humans can, the
Neural Networking in ALVINN or other similar systems will
be able to process more data (because more data will be
received due to the car’s camera’s increased vision) in a
shorter amount of time, therefore leading to impressive
numbers such as preventing up to 80% of simulated
collisions.
FIGURE 4 [5]
Discrete-state transition model.
Once these ordinary differential equations were solved, in
order for them to be used alongside the MDP equations, the
researchers had to translate them to a discrete-state transition
model, like the model shown in figure 4 [5].
Lastly, there are three main algorithmic codes that go into
the MDP approach and each plays a vital role. These
algorithms are very difficult to explain, however they each
have a clear and concise purpose. The first algorithm is for the
Neural Networking system to learn the transition model, this
will help determine which state should be followed. The
second algorithm takes into account the transition model as
well as the reward model—variables T and R from above—
and after running these variables through the code will return
the optimal course of action. The third algorithm is a
simulation and is used to replicate interactions on the road as
close as possible, whether the conditions are normal and
constant, or improbable like most emergency situations are;
this final algorithm again incorporates this partially
randomized aspect of the MDP approach.
It does not require much depth of research to see that an
artificial intelligence MDP model-based system would
significantly assist overall collision avoidance in autonomous
Equations and Algorithms for the MDP-based Approach
Although the process and the goals for the system can be
explained simply, the development of such systems proves to
be extremely advanced and complex. The first set of
equations that must be determined are the equations for the
artificial intelligence MDP model. Although the variables
described above hold true, the MDP model for autonomous
cars described in this paper provide four variables: S, A, T,
and R. There descriptions are as follows: “S is the set of
discrete states of the car, A is the set of desired actions, T (s,
a, s ) is the transition model from any state s ∈ S to any other
state s′ ∈ S when the action a ∈ A is taken, and denotes the
conditional probability of transition p(s′ |a, s). R is the model
of the reward obtained by the transition (s, a, s′ )” [4]. As we
can see, the variables without a doubt relate to the variables
of a basic MDP model, but have been focused to work
specifically for a system of an autonomous car.
The second group of equations is a set of ordinary
differential equations that are used to determine the dynamics
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cars. In comparison to human drivers, this type of system
would be able to make decisions much faster because of the
various technology implemented between the cameras and
Neural Networking. The one main problem that still exists
with and MDP-based system is that at times, it reacts—in an
attempt to avoid danger—when it does not have to. So even
though the car will be moving with less jolt than a human
driver would when we slam on the brakes, it still might make
evasive maneuvers when in fact there is no serious danger and
this comes as an annoyance to some passengers. With time,
developers will work on and eventually fix this problem and
continue improving upon this already brilliant technology
until it makes its way onto our roads.
Although RSU and OBU present themselves as the
primary systems that work in contribution to the evasive
maneuver process, there are other parts of this overall system
that help to detect the pedestrian’s intention and trigger
evasive maneuvers. The developers from the University of
Applied Sciences Aschaffenburg use a “Motion Contour
image based HOG-like (MCHOG) descriptor in combination
with Support Vector Machine (SVM) classification for
recognizing the pedestrian’s gait initiation” [6]. With the help
of these two systems, the autonomous car can formulate an
idea for what the pedestrian might do; once it has this idea it
will then verify this idea and continue to track the pedestrian;
finally, when the pedestrian is verified and tracked, the
MCHOG method will be applied to the situation which will
determine the decision and maneuver the car will make.
EVASIVE MANEUVERS TO PEDESTRIAN
SCENARIOS
One of the grey areas of collision avoidance that
MPC/ESC and MDP-based systems sometimes fail to
consider is the improbable intentions of pedestrians. In a
paper written by researchers from the University of Applied
Sciences Aschaffenburg (Germany), the authors state that
“Pedestrians account for 65% of the fatalities out of the 1.17
million worldwide traffic-related deaths” and, “83.7% of the
accidents with injuries in an urban traffic environment caused
by a pedestrian error are attributable to misbehavior when
crossing the street” [6]. Statistics such as these served as these
researchers’ primary motivation to develop a system for
autonomous cars to be able to reduce the number of these
dangerous accidents.
The solution to this problem dealing with the uncertainties
of pedestrian intentions comes in a system consisting of two
main components. The first is known as a mobile Road Side
Unit (RSU) which will be the first set of eyes for the system
and will identify if the pedestrian has the intention to to cross
the street. These cameras will produce a 3D image of the
pedestrian and this image will then be analyzed and ran
through an algorithm that will begin formulating predictions
of the pedestrian’s intentions. The second component of this
system is known as an On Board Unit (OBU) which receives
the information in the car from the RSU. Once the OBU has
received this communication, it initiates an autonomous
evasive maneuver. It triggers this maneuver with the help of
algorithms that have been developed for evasive trajectory
planning and lateral vehicle control. In order for every
element of this system to be able to operate, the vehicle’s
current position is necessary and the computer receives this
information from a Differential Global Navigation Satellite
System (DGNSS). The various information gathered helps
algorithms determine the ideal path for the autonomous car to
take so that it does not hit the pedestrian and keeps the
passengers of the car safe as well.
FIGURE 5 [6]
MCHOG Chart.
The developers provide a chart that illustrates the MCHOG
method (shown in figure 5) [6]. The next step in this process
of determining the pedestrian’s intention is referred to by the
developers as “Feature Extraction” [6]. During this stage of
the process, “A Motion History Image (MHI) is composed of
ten consecutive foreground images” [6].
FIGURE 6 [6]
Sample MHI images.
MHIs look like those shown in figure 6 above [6]. Once the
MHIs are assembled, the next stage of the process begins, and
the images will then get classified by the SVM. The
developers trained the classifier by putting together 63 videos
of pedestrians in different scenarios before or while they
Recognizing Dangerous Pedestrian-related Situations
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attempted to cross a street. These videos portrayed the
pedestrians standing upright at a curb, or standing off the curb
and from there the pedestrians performed “typical scenarios,
e.g., standing still before crossing, approaching and crossing
with a short intermediate stop, or freestyle scenarios, where
the test person can perform any kind of action” [6]. This helps
prepare the autonomous car for any type of pedestrian
movement.
faster than the autonomous car. The results of the case study
showed that almost 50% of the time, the autonomous car
responded faster than the human [6]. Although at first glance
these results do not seem astonishing, what must be
considered is that the humans in the situation were able to
focus all of their attention of the pedestrian and in real life
scenarios, this would of course not be the case. So, this case
study shows that when on the road, the autonomous car would
respond much faster, and in a smoother and safer manner to
emergency situations involving pedestrians than a human
driver would, or possibly even could.
Executing Evasive Maneuvers
Once the autonomous car has a sense of the pedestrians’
intentions, it is ready to begin planning its evasive trajectory.
The precise determination of this maneuver comes as a result
of multiple equations that take the variables of the system into
account. The execution of the equations and analyzation of
the variables takes place in the OBU once it receives this
necessary information from the RSU: “the position of the
pedestrian, the geodetic coordinates LongR and LatR of the
curbside, the angle φ, which describes the orientation of the
road with respect to the north axis, and the lane width w” [5].
For each situation where the car must perform an evasive
maneuver, there are certain maximum values that the car
cannot exceed in the equations that take place in the ODU. If
these maximum values are exceeded—for example, if the
angle the steering wheel must turn is too large—then the
evasive trajectory will not be attainable and in this unfortunate
case, a collision cannot be avoided.
ETHICAL CONCERNS WITH THE
SUSTAINABILITY OF AUTONOMOUS
PROGRAMMING
The development of autonomous vehicles today is shaping
the sustainability of future roadways. Though sustainability
usually refers to environmental matters such as pollution or
certain gas emissions, sustainability in a general sense focuses
on the quality of life of involved parties. In terms of the
environment, the involved lives are humans and nature, but
sustainability in the context of autonomous cars is determined
by the guarantee of car passengers’ safety on the roads.
However, developers of these autonomous cars today have
been presented with a set of ethical questions during
inevitable emergency crash situations where safety cannot be
guaranteed to all car passengers. Ideally, autonomous cars
could avoid all collisions and be perfectly sustainable, but the
unpredictability of the road environment has led these
developers to realize that, “Not all crashes will be avoided,
though, and some crashes will require AVs to make difficult
ethical decisions in cases that involve unavoidable harm” [7].
Autonomous cars must be equipped with the decisions to
handle these situations involving ‘unavoidable harm’,
meaning that developers must decide how their autonomous
car will respond when lives are questioned in hypothetical,
but possible scenarios.
FIGURE 7 [6]
An evasive trajectory maneuver.
Most of the time however, the car will be able to execute these
evasive maneuvers similar to figure 7 [6]. And while doing
so, since it will be making the optimal maneuver and has
lateral control systems in place, will make this maneuver in a
manner that is smoother than a maneuver executed by a
human driver.
In a case study conducted by the researchers at the
University of Applied Sciences Aschaffenburg, human
drivers tried to identify a pedestrian and react to the situation
FIGURE 8 [8]
Three cases involving lives of pedestrians, passers-by, or
passengers.
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Figure 8 provides three separate cases involving the
endangerment of the lives of pedestrians, passers-by, or
passengers. The car must decide between (A) killing several
pedestrians or one passerby, (B) killing one pedestrian or its
own passenger, and (C) killing several pedestrians or its own
passenger.
In situation A, developers are faced with sparing the lives
of either a single passerby or a group of pedestrians. From the
point of view of the passerby, the passerby is innocently
walking on the side of the road when he/she is unjustly hit by
an autonomous car due to the sizable group of pedestrians that
are crossing the street. From the point of view of the
pedestrians, the amount of lives endangered in the crosswalk
is far greater than the one life on the sidewalk. In situation B,
the developers must decide which life is more valuable: the
onboard passenger or the single pedestrian. Through the eyes
of the pedestrian, he/she, as a member of the general public,
deserves to be protected. Through the eyes of the passenger,
he/she, as a client to the automotive corporation, deserves to
be protected. Lastly, situation C provides a more extreme
example of situation B with now many pedestrians. Again, the
client’s perspective involves a factor of loyalty in his/her
interest to be protected. Now, the general public incorporates
more than one life into the mix and again pleads that the
public should be kept out of harm. Scenario A prompts the
question: Is a passerby’s life more or less valued than a of
group pedestrians’ lives? Scenarios B and C beg the question:
Is pedestrian life more or less valued than an onboard
passenger’s life?
As developers’ answer these questions with their own
codes of ethics, one prevalent ideal is the use of utilitarianism.
Utilitarianism, as defined by the Merriam-Webster dictionary,
is, “a theory that the aim of action should be the largest
possible balance of pleasure over pain or the greatest
happiness of the greatest number” [9]. Utilitarian cars value
and attempt to save the lives of the most people possible, even
if the straying off the road threatens the onboard passengers.
Utilitarian vehicles will veer off the road in situation A, B,
and C because the most amount of people and/or the general
public will be guarded from harm. This concept may seem
unjust is some situations, but the general consensus of the
public is for autonomous cars to safeguard the maximum
amount of lives as possible. When developers program
autonomous cars with utilitarianism theory, they are
programming the cars for sustainability. They are ensuring
that in emergency situations where lives could be at stake, the
next generation is always first priority and the maximum
amount of people will be saved. Because utilitarianism works
towards benefiting as many people as possible, it is an idea
that works directly alongside sustainability.
The impending integration of autonomous cars into
tomorrow’s society shows promise for safer roadways in the
future. Through the use of Model Predictive Control
(MPC), the Markov Decision Process (MDP), and
pedestrian-evasive maneuver systems, autonomous car
developers plan to correct the natural human error in driving.
As these developers produce more and more vehicles,
society approaches a nearly collision-free world. In order for
autonomous cars to be proven sustainable and make the
ultimate leap into everyday life, society must accept the
ethics involved in autonomous cars. Once these moral
dilemmas are solved and autonomous cars become more
approachable, the general public can begin to benefit from
safer roads in the future.
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THE FUTURE OF AUTONOMOUS
VEHICLES
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ACKNOWLEDGEMENTS
We would like to thank our chair and co-chair for the
guidance in the development of our ideas. In addition, we
would like to thank our writing instructors for their crucial
feedback throughout the writing process. Lastly, we would
like to thank Uber ATC for test-driving their line of
autonomous vehicles here in Pittsburgh. Uber ATC’s testing
inspired us to learn more about autonomous vehicles and
delve further into the autonomous control field’s literature.
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