# Experiential Learning in Vehicle Dynamics Education via  Motion Simulation and Interactive Gaming

Authors: Kevin Hulme; Edward Kasprzak; Ken English; Deborah Moore-Russo; Kemper Lewis
Journal: International Journal of Computer Games Technology
 (2009)

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## Abstract

Creating active, student-centered learning situations in postsecondary education is an ongoing challenge for engineering educators. Contemporary students familiar with visually engaging and fast-paced games can find traditional classroom methods of lecture and guided laboratory experiments limiting. This paper presents a methodology that incorporates driving simulation, motion simulation, and educational practices into an engaging, gaming-inspired simulation framework for a vehicle dynamics curriculum. The approach is designed to promote active student participation in authentic engineering experiences that enhance learning about road vehicle dynamics. The paper presents the student use of physical simulation and large-scale visualization to discover the impact that design decisions have on vehicle design using a gaming interface. The approach is evaluated using two experiments incorporated into a sequence of two upper level mechanical engineering courses.


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## Body

## 1. Introduction

The
field of vehicle dynamics has the advantage that every student has either
driven or been a passenger in an automobile. For most students, traveling in an
automobile has been a regular occurrence for their entire lives. Thus, vehicle
motions are inherently familiar to the student.
Also, with 30 million vehicles being manufactured each year worldwide, advances
in computing technology and electromechanical systems have expanded the
influence engineers have over the stability and control of vehicle dynamics. In
the field of automotive engineering, while a well-designed passive vehicle is
still necessary, augmenting systems such as antilock brakes, electronic skid
prevention, yaw control, active differentials, and similar systems improve
safety and performance over passive designs. This increased control over the
automobile will soon be inseparable from the increased complexity of the
subsystems, all of which will combine to determine handling characteristics of
the automobile. As a result, engineers are increasingly turning to simulation
and virtual prototyping, rather than physical prototyping, to explore new
design concepts. As the usage of simulation increases, the demand for students
with hands-on experience in configuring and executing simulation-based research
will also increase.The most authentic experience in
vehicle dynamics education would be to have students drive real automobiles,
perform specific driving maneuvers, use on-board instrumentation to collect
vehicle data and modify the vehicle to see resulting changes in its
characteristics. While not impossible, concerns about cost, time, space, safety,
and weather constraints make this impractical at most schools. An alternative solution is to make use of a
simulated environment. Many students have already experienced controlling a
sophisticated driving simulation environment in the form of a driving game like
Gran Turismo 4 [1]
or Race Pro [2],
but do not associate the gaming environment with the models and equations that
engineers use in designing a vehicle.This
paper presents the development and evaluation of a learning environment that
provides a game-based context for vehicle dynamics education. While
driving game-based approaches have been developed for mental health, driver
workload, and rehabilitation applications, gaming-based contexts have not been
used in vehicle dynamics education.  The
presented work describes an innovative methodology for coupling gaming, motion
simulation, and educational practices together in a cohesive pedagogical
approach.The project's theoretical underpinnings are based on
situated learning where new educational material is presented in an authentic
context, and social interaction and collaboration are required for learning to
occur. Through a learner-centered approach, students use a physical simulation
and large-scale visualization in a gaming-inspired format to discover the
impact that design decisions have on a dynamic system.While incorporating a gaming environment may
intuitively make sense from the perspective of creating a more engaging
experience for students, not all engaging experiences are educational. A
pedagogical basis must first be developed for including a gaming-based learning
environment in an engineering course. In Section2 of the paper, the educational
motivation for the work is presented, along with the pedagogy that supports the
development of the framework. The development of serious games is presented in
Section 3, providing a background for the development of the gaming context for
vehicle dynamics education. The learning environment, core methodology, and
accompanying gaming infrastructure are detailed in Section 4. Section 5
presents the incorporation of the simulation game environment into two
upper-level mechanical engineering courses, including assessments about the
impact of the experience on the students.
The paper is concluded with a set of collective insights and conclusions.


## 2. Experiential Learning

Relating
theoretical and analytical results to real-world phenomena is one of the most
difficult tasks in education. While text, equations, diagrams, and graphs are
an efficient means of presenting large amounts of information, such representations
are necessarily abstractions of reality. A significant part of a student’s learning
process is learning how to transform these abstractions into knowledge that
will allow them to applying their understanding to real-world products and
systems. Many attempts to cross this gap are used by educators, including
in-class demonstrations, laboratory experiments, videos, and computer graphic
simulations [3–6]. In a study of the application of information technology to
education, the President’s Information Technology Advisory Council (PITAC)
recommended the development of technologies for education and training that use
simulation, visualization, and gaming to actively engage students in the
learning experience [7]. In the same report, PITAC also
recommended the development of educational experiences that provide learners
with access to world-class facilities and experiences using either actual or
simulated devices.The
serious gaming approach presented in this paper concentrates on developing an
innovative means of incorporating items from the accreditation criteria to
assist in the development of educational experiences that will translate well
to industrial application [8]:(i)
an ability to apply knowledge of mathematics, science, and engineering;(ii)
an ability to design and conduct experiments as well as to analyze and interpret
data;(iii)
an ability to identify, formulate, and solve engineering problems;(iv)
an ability to use the techniques, skills, and modern engineering tools necessary
for engineering practice. In
addition to the guidance of the accrediting body, the National Survey of Student
Engagement (NSEE) provides an opportunity for senior college students to
evaluate their educational experience against five benchmarks [9].(i)
Level of
Academic Challenge: challenging
intellectual and creative work is central to student learning.(ii)
Student-Faculty
Interaction: contact with
professors offers students an opportunity to see how experts think about and
solve practical problems.(iii)
Active and
Collaborative Learning: students
learn more when intensely involved in the educational process and are
encouraged to apply their knowledge in many situations.(iv)
Supportive
Campus Environment: students
perform better when their college is committed to their success and cultivates
positive social relationships among different groups of people.(v)
Enriching
Educational Experiences: learning
opportunities inside and outside classroom (diversity, technology,
collaboration, internships, community service, capstones) enhance learning. These points of guidance
emphasize the need for students to not only study, but also to practice the
application of their knowledge in an active manner that is similar to what they
will experience after college.  One approach
to provide these experiences is simulation, which enables practicing engineers to
determine the performance of a design before the system is actually
constructed.  Consequently, an engineer
may explore the merits of alternative designs without physically building the
system, significantly reducing the cost of designing a product. Many
opportunities exist in engineering education to mimic how engineers in industry
have increasingly turned to simulation in product and systems design processes.However, the use of simulation does not ensure student
engagement. In order to provide an engaging experience for students to learn in
an educational context, the simulation must be developed with student
engagement in mind. Different types of simulators can be effective in teaching,
training, or demonstrating design concepts to students. Computer graphics-based
simulators that display the simulated system provide the user with a
meaningful understanding of the system’s behavior. Physical simulation tools,
such as motion simulators, augment the display of graphical information with
physical motion, providing learners with immediate feedback when designing
dynamic systems and the opportunity to both see
and experience the impact of their
decisions first hand.  Many students are
already familiar with computer games that are based on physically accurate
physics engines and high-fidelity graphics and have the ability to adjust some
parameters of the underlying simulation. In this situation, students may adjust
parameters, but not experience any lasting learning. A successful pedagogy will
couple authentic experiences for a student along with support from an
instructor to promote lasting learning.While serious gaming could be applied to a number of
engineering courses, vehicle dynamics education was identified as an area with
high potential for the development of a simulation game-based learning
environment.  Designers of automotive vehicles
often use motion simulation to help overcome difficulties in the development of
new vehicle designs. Creating a prototype vehicle is costly and controlling
environmental conditions is difficult, if not impossible. One alternative is to
simulate the vehicle dynamics through the use of a motion platform. Developing a serious game for vehicle dynamics
education would provide learners with an authentic engineering experience in an
engaging and relatable manner. The next section provides some background of the
evolution of games and their use as educational tools.


## 3. Serious Gaming for Education

Video games and gaming systems
have increasingly been found in applications more diverse than just
entertainment, including use in training, education, research, and simulation.  This emerging field of Serious Games (e.g., [10, 11])
is intended to provide an environment for an accurate and an engaging context
within which to motivate participants beyond the capability of conventional approaches.Though individual researchers may
each have different definitions for the term “Serious Games”, various
widely-accepted subclassifications of the genre exist. Of particular interest
for the research at hand are two such classifications: (a) games-based learning,
and (b) simulation games. Prensky [12]
develops the classification of Games-Based Learning as “games with defined
learning outcomes” and games that “are designed to balance subject matter with
game-play, and the ability of the player to retain and apply the subject matter
to the real world.” Ruohomaki [13]
classifies a game that “mixes skill, strategy, and chance to simulate some
aspect of reality” as a Simulation Game. The Simulation Game classification has
numerous relevant subclasses of its own, including “vehicle simulation” (i.e., providing
a participant with a realistic interpretation of operating a vehicle, such as
an automobile), and even more specifically, “racing games” (e.g., first- or
third-person driving games). The approach presented in this work relies on a
simulation game architecture to achieve the educational goals of the course.Numerous computer games, all of
which were developed primarily for entertainment purposes, have had ongoing
influence on the driving simulation developments in the present work. For
dissemination and realism, it is useful to have a simulation that allows for
multiple participants simultaneously [14].
It is also likely that these participants may not be colocated (i.e., in the
same room, the same city, the same state, or even in the same country).  Thus, an over-the-network capability is
certainly desirable.  An effective
example in the gaming world that addresses this need is the game called BZflag [15],
originally designed in 1992. BZflag, shown in Figure 1, is a simple networked 3D
multiplayer game, in which the objective is to destroy your opponent’s tank. BZflag was modeled after a 1980 Atari tank
combat game called Battlezone [16].
Battlezone is a game many consider to be the very first serious game, as a
version of it was used for baseline Military ground vehicle training [17].Figure 1
BZflag.Various gaming systems exist that
allow the player to generate a developer-defined environment within which to
interact. One example is SimCity [18], first released in 1989.  The objective of the game, quite simply, is
to design and build a virtual city.  The
developer is presented with an empty plot of land, within which buildings,
roadways, landscaping, and so forth. can be placed to form a representation of a
functional city. The popularity of the game was due, in large part, to its
open-endedness, reusability, and ease-of-use. These types of “Sim” games have
become immensely popular in recent years and have led to various similar games
that have ties to moving vehicles and transportation and can be “driven” from
a first-person perspective.Easily the most popular of these
newer breed of games is Roller Coaster Tycoon [19],
first released in 1999 and shown in Figure 2.
Similar to SimCity in that the player is presented with an empty plot of
land, the objective is to build a functioning amusement park, including
pathways, landscaping, and of course rides.
During game play, the player builds a virtual roller coaster
piece-by-piece using track segments and then rides the finished product.  The simulator includes a simplified real-time
physics engine, so the roller coaster ride behaves true to real-world form.
While these examples provide a small sample of the game-based learning
applications that have been implemented, they provide a sufficient model for
the creation of a simulation game to enhance learning about a specific topic. The
next section provides a description of the simulation game and the physical
environment where the students play the game.Figure 2
Roller coaster tycoon.


## 4. Driving Simulator Game for
Experiential Learning

This section describes the
methodology behind the implementation of a motion-based simulation game in the
form of a driving simulator geared toward undergraduate and graduate
engineering students. This simulation game was generated primarily for
education, research, and training, and for sake of authenticity and it exploits the
availability of an on-site motion simulator. A brief background on motion simulators
is presented first.


### 4.1. Motion Simulators

A
motion platform, parallel manipulator [20], or Stewart Platform [21, 22], is a powered, mechanical, self-contained
system for the execution of motion-based simulation and is described by the
number of degrees-of-freedom (DOF) that can be simulated by the hardware. Figure 3 shows the motion platform currently in use in NYSCEDII's simulator facility. The
motion platform has an on-board computer that converts incoming messages into
actuator commands that result in the movement of the top of the platform. Such
systems are commonly used in the automotive and other industries (e.g., flight,
training, and entertainment). NYSCEDII's passenger cabin located atop the
platform is a 1999 Ford Contour, cut in half to accommodate a front seat driver
and passenger.Figure 3
Moog 2000E motion platform and passenger cabin.One of the primary advantages of
motion simulators is that they are able to provide users with immediate
feedback when designing dynamic systems.
When a student drives or rides in the simulator, they experience many of
the same sensory inputs as in a real vehicle.


### 4.2. Methodology

A common complaint of graduating
undergraduate engineers is that while they are well versed in engineering
theory, they are under rehearsed in engineering application.  Many engineering curricula do not spend
enough time assuring that when students complete their studies that they enter
the work force with sufficient practical, experiential, and hands-on knowledge
of their craft. To this end, PITAC [7]
has indeed recommended the development of “technologies for education and
training that use simulation, visualization, and gaming” and “educational
experiences that provide learners with access to world-class facilities and experiences
using either actual or simulated devices.”One option for exposing
engineering students to hands-on driving experiences would be to use an actual
vehicle upon an actual roadway or test track.
While this is undoubtedly the most authentic means of exposing a student
to the practical principals of real world driving, clearly there are
insurmountable safety concerns that deem this option impractical.  As well, the adaptation of existing
high-fidelity driving games (e.g., Gran Turismo [1])
might seem to be a more straightforward option than the full development of a
complete games-based driving simulation.
However, such off-the-shelf games, geared primarily for an entertainment
audience, do not allow for the creation of custom learning scenarios that are
designed to focus a student's attention on the system dynamics rather than the
outcome of a particular race.  By
developing an engaging simulation game that focuses on the differences between
varying simulation models (e.g., vehicle model fidelities, vehicle types, tire
dynamics, etc.), students can develop a better understanding of the information
available as engineers make decisions at different stages of a vehicle design
project.  Data-logging can be performed
directly within the simulation, changes to the vehicle can be made very quickly—even while driving, and weather and climate conditions are operator
controlled.At its core, the experiments
developed can be classified as a serious game as they integrate gaming
elements, simulation elements, and pedagogical elements by including support
from system infrastructure, instructors, and peers [23].
They balance subject matter with game-play, focusing on the skills and
strategies of the driver to retain and apply the subject matter to the real
world. Much of the novelty in the simulator is its integration of computer
programming, computer graphics, linear algebra, numerical methods, and systems
analysis with the vehicle dynamics theory fundamental to the Road Vehicle Dynamics
(RVD) courses for
which the simulation was developed.The methodology employed, adapted
from the general structured process for education gaming and simulation [24],
is as follows.(1)
Define learning outcomes.(2)
Select a set of simulation
activities or gaming tasks to support this required learning outcome with an
appropriate form of assessment.(3)
Consider a strategic ordering
of the game tasks within the gaming session and assessment process.(4)
Implement the session with
proper consideration given to preparation, execution, and reflection.(5)
Assess the impact and
effectiveness of the session.(6)
Redesign the session
according to feedback given. The specific implementation of
this methodology is described in Sections 5 and 6. The underlying hardware and software
framework of the game-based vehicle simulation environment is described in the
next section.


### 4.3. Framework

The simulation game has been
implemented using the C++ programming language [25] and constructed on the Windows platform within Microsoft Visual Studio.  The entire gaming framework is outlined in
the following steps; refer to Figure 4 for a flowchart overview.Figure 4
Driving
simulation game—process flowchart.


#### 4.3.1. Establish Network Connection with Motion Platform (MP) (Process a)

The computer integrated into the MP can send/receive motion
commands, in the form of datagrams [26], from another computer on the network.
Figure 5 presents a screen capture of Moog’s Base Maintenance Software [27], for monitoring the state of the six actuators on the motion platform
(e.g., error messages, status messages, etc.) in real-time as the simulation
executes. This software runs on the
On-Board Computer (OBC) and is displayed on an off-board monitor.Figure 5
OBC display screen.


#### 4.3.2. User Controls Simulation with Human Interface Device (HID) (Process b)

The driver controls the simulation while on-board the MP by
way of a USB steering wheel and pedals.  Commands
are captured using DirectX’s DirectInput Application Programmer’s Interface
(API) [28, 29] in real-time. The selected gaming device, shown in Figure 6,
contains numerous components that must be continually tracked, including the
accelerator, brake, clutch pedals, the single-axis motion of the steering
wheel, the paddle shifters, and 6 buttons located on the steering wheel.Figure 6
On-board HID.


#### 4.3.3. Simulation Computer (SC) Receives Inputs, Performs Analysis for Current Time Step (Process c)

The states of the vehicle dynamics
model (described in Section5) are now computed at 60 Hertz and must be
converted into DOF's (i.e., roll, pitch, yaw, heave, surge, and sway)—see Figure 7. This conversion typically involves
scaling, limiting, and tilt coordination [30], collectively known as washout filtering [31].  The work presented here uses a
proprietary filter based upon a classical washout algorithm [32].Figure 7
Motion platform DOF's.


#### 4.3.4. Updated State Delivered to Projection System (PS) and Visualization Screens (VSs) (Process d)

The scene graphics are now updated
and sent to the PS and to the three 48 ft2 VSs. The simulation
graphics have been developed using OpenGL [33], selected primarily to demonstrate that a useful game-based learning
environment can be created without the optimizations of a scene graph toolkit
(e.g., OpenSceneGraph [34] and Delta3D [35]). The virtual world is populated with a sky, ground (grass, pavement),
the driven vehicle and adjacent vehicles, track/roadway obstacles (e.g. cones),
curbs, walls, trees, roadway signs, the vehicle control panel, and various
other details to promote an authentic representation of a test track. Figure 8
presents screen captures of various simulations.(Clockwise, from (a) Experiment 1 (“Skid Pad”), (b) Experiment 2 (“Tri-Radii”),  (c) Future Work (“Networked Karting”), and (d) Experiment 3 (“Millersport Hwy”).
(a)(b)(c)(d)


#### 4.3.5. DOF’s Sent to Motion Platform Interface Computer (MPIC) (Process e)

The updated DOF's are delivered by the SC to the MPIC,
dedicated to the motion process and requires an uninterrupted 60 Hz flow of
data. This is accomplished using Parallel Virtual Machine (PVM) [36], a networking API based upon TCP/IP Socket programming [37] and coded in ANSI C. PVM allows us to easily and reliably send data
packets between computers and handles cross-platform, nonheterogeneity issues
transparently (e.g., endian/byte-order concerns, char-to-float data conversion,
etc.). Once the MPIC receives the updated DOF's, these commands are finally
delivered directly to the MP for motion processing.


#### 4.3.6. MPIC Delivers Computed DOF’s to MP (Process f)

An
“instance” of the MP is declared and assigned an IP address.  The instance is initialized, and then motion
commands are updated and delivered to the MP at a rate of precisely 60 Hz.  When the simulation is complete, the MP
instance is then shutdown, thereby completing the motion process.


#### 4.3.7. Audio States Delivered to Mixer/Amplifier (MA) and 2.1 Sound System (SS) (Process g)

The simulation game uses OpenAL [38] for adding sound events synchronized with the graphical
simulation.  Sound is a vital simulation
cue, and it enhances the impact of visualization and motion cues.  Examples include vehicle ignition, engine
idle, squealing tires, cone strikes, spinout/crash, police siren, and vehicle
shutdown.  Each cue can be made to vary
in pitch/gain in accordance with the present state of the simulation.  The audio states are sent to the mixing
board, are processed, and then are delivered to the amplifier, which delivers the
audio signal to a 2.1 stereo sound system, comprised of front-left
(SS-L), front-right (SS-R), and subwoofer (SS-SW) channels.


#### 4.3.8. Emergency Stop Switch Is Available, at All Times, to Terminate the Simulation (Process h)

Throughout the simulation, both the simulation driver (on
board) and simulation operator (off board) can activate an Emergency Stop
switch (to view the on-board switch, refer to bottom-right corner of Figure6). When
the red button is struck, the motion simulation comes to an immediate,
premature, and smooth conclusion. Such a safety device is required in the case of a
hardware malfunction or in the event of
driver panic/illness, or any other circumstance that might deem premature termination of
the present simulation.The
next section discusses the incorporation of the simulation game described into
a Road Vehicle Dynamics (RVD) engineering course curriculum, using an
experiential learning context.


## 4.1. Motion Simulators

A
motion platform, parallel manipulator [20], or Stewart Platform [21, 22], is a powered, mechanical, self-contained
system for the execution of motion-based simulation and is described by the
number of degrees-of-freedom (DOF) that can be simulated by the hardware. Figure 3 shows the motion platform currently in use in NYSCEDII's simulator facility. The
motion platform has an on-board computer that converts incoming messages into
actuator commands that result in the movement of the top of the platform. Such
systems are commonly used in the automotive and other industries (e.g., flight,
training, and entertainment). NYSCEDII's passenger cabin located atop the
platform is a 1999 Ford Contour, cut in half to accommodate a front seat driver
and passenger.Figure 3
Moog 2000E motion platform and passenger cabin.One of the primary advantages of
motion simulators is that they are able to provide users with immediate
feedback when designing dynamic systems.
When a student drives or rides in the simulator, they experience many of
the same sensory inputs as in a real vehicle.


## 4.2. Methodology

A common complaint of graduating
undergraduate engineers is that while they are well versed in engineering
theory, they are under rehearsed in engineering application.  Many engineering curricula do not spend
enough time assuring that when students complete their studies that they enter
the work force with sufficient practical, experiential, and hands-on knowledge
of their craft. To this end, PITAC [7]
has indeed recommended the development of “technologies for education and
training that use simulation, visualization, and gaming” and “educational
experiences that provide learners with access to world-class facilities and experiences
using either actual or simulated devices.”One option for exposing
engineering students to hands-on driving experiences would be to use an actual
vehicle upon an actual roadway or test track.
While this is undoubtedly the most authentic means of exposing a student
to the practical principals of real world driving, clearly there are
insurmountable safety concerns that deem this option impractical.  As well, the adaptation of existing
high-fidelity driving games (e.g., Gran Turismo [1])
might seem to be a more straightforward option than the full development of a
complete games-based driving simulation.
However, such off-the-shelf games, geared primarily for an entertainment
audience, do not allow for the creation of custom learning scenarios that are
designed to focus a student's attention on the system dynamics rather than the
outcome of a particular race.  By
developing an engaging simulation game that focuses on the differences between
varying simulation models (e.g., vehicle model fidelities, vehicle types, tire
dynamics, etc.), students can develop a better understanding of the information
available as engineers make decisions at different stages of a vehicle design
project.  Data-logging can be performed
directly within the simulation, changes to the vehicle can be made very quickly—even while driving, and weather and climate conditions are operator
controlled.At its core, the experiments
developed can be classified as a serious game as they integrate gaming
elements, simulation elements, and pedagogical elements by including support
from system infrastructure, instructors, and peers [23].
They balance subject matter with game-play, focusing on the skills and
strategies of the driver to retain and apply the subject matter to the real
world. Much of the novelty in the simulator is its integration of computer
programming, computer graphics, linear algebra, numerical methods, and systems
analysis with the vehicle dynamics theory fundamental to the Road Vehicle Dynamics
(RVD) courses for
which the simulation was developed.The methodology employed, adapted
from the general structured process for education gaming and simulation [24],
is as follows.(1)
Define learning outcomes.(2)
Select a set of simulation
activities or gaming tasks to support this required learning outcome with an
appropriate form of assessment.(3)
Consider a strategic ordering
of the game tasks within the gaming session and assessment process.(4)
Implement the session with
proper consideration given to preparation, execution, and reflection.(5)
Assess the impact and
effectiveness of the session.(6)
Redesign the session
according to feedback given. The specific implementation of
this methodology is described in Sections 5 and 6. The underlying hardware and software
framework of the game-based vehicle simulation environment is described in the
next section.


## 4.3. Framework

The simulation game has been
implemented using the C++ programming language [25] and constructed on the Windows platform within Microsoft Visual Studio.  The entire gaming framework is outlined in
the following steps; refer to Figure 4 for a flowchart overview.Figure 4
Driving
simulation game—process flowchart.


### 4.3.1. Establish Network Connection with Motion Platform (MP) (Process a)

The computer integrated into the MP can send/receive motion
commands, in the form of datagrams [26], from another computer on the network.
Figure 5 presents a screen capture of Moog’s Base Maintenance Software [27], for monitoring the state of the six actuators on the motion platform
(e.g., error messages, status messages, etc.) in real-time as the simulation
executes. This software runs on the
On-Board Computer (OBC) and is displayed on an off-board monitor.Figure 5
OBC display screen.


### 4.3.2. User Controls Simulation with Human Interface Device (HID) (Process b)

The driver controls the simulation while on-board the MP by
way of a USB steering wheel and pedals.  Commands
are captured using DirectX’s DirectInput Application Programmer’s Interface
(API) [28, 29] in real-time. The selected gaming device, shown in Figure 6,
contains numerous components that must be continually tracked, including the
accelerator, brake, clutch pedals, the single-axis motion of the steering
wheel, the paddle shifters, and 6 buttons located on the steering wheel.Figure 6
On-board HID.


### 4.3.3. Simulation Computer (SC) Receives Inputs, Performs Analysis for Current Time Step (Process c)

The states of the vehicle dynamics
model (described in Section5) are now computed at 60 Hertz and must be
converted into DOF's (i.e., roll, pitch, yaw, heave, surge, and sway)—see Figure 7. This conversion typically involves
scaling, limiting, and tilt coordination [30], collectively known as washout filtering [31].  The work presented here uses a
proprietary filter based upon a classical washout algorithm [32].Figure 7
Motion platform DOF's.


### 4.3.4. Updated State Delivered to Projection System (PS) and Visualization Screens (VSs) (Process d)

The scene graphics are now updated
and sent to the PS and to the three 48 ft2 VSs. The simulation
graphics have been developed using OpenGL [33], selected primarily to demonstrate that a useful game-based learning
environment can be created without the optimizations of a scene graph toolkit
(e.g., OpenSceneGraph [34] and Delta3D [35]). The virtual world is populated with a sky, ground (grass, pavement),
the driven vehicle and adjacent vehicles, track/roadway obstacles (e.g. cones),
curbs, walls, trees, roadway signs, the vehicle control panel, and various
other details to promote an authentic representation of a test track. Figure 8
presents screen captures of various simulations.(Clockwise, from (a) Experiment 1 (“Skid Pad”), (b) Experiment 2 (“Tri-Radii”),  (c) Future Work (“Networked Karting”), and (d) Experiment 3 (“Millersport Hwy”).
(a)(b)(c)(d)


### 4.3.5. DOF’s Sent to Motion Platform Interface Computer (MPIC) (Process e)

The updated DOF's are delivered by the SC to the MPIC,
dedicated to the motion process and requires an uninterrupted 60 Hz flow of
data. This is accomplished using Parallel Virtual Machine (PVM) [36], a networking API based upon TCP/IP Socket programming [37] and coded in ANSI C. PVM allows us to easily and reliably send data
packets between computers and handles cross-platform, nonheterogeneity issues
transparently (e.g., endian/byte-order concerns, char-to-float data conversion,
etc.). Once the MPIC receives the updated DOF's, these commands are finally
delivered directly to the MP for motion processing.


### 4.3.6. MPIC Delivers Computed DOF’s to MP (Process f)

An
“instance” of the MP is declared and assigned an IP address.  The instance is initialized, and then motion
commands are updated and delivered to the MP at a rate of precisely 60 Hz.  When the simulation is complete, the MP
instance is then shutdown, thereby completing the motion process.


### 4.3.7. Audio States Delivered to Mixer/Amplifier (MA) and 2.1 Sound System (SS) (Process g)

The simulation game uses OpenAL [38] for adding sound events synchronized with the graphical
simulation.  Sound is a vital simulation
cue, and it enhances the impact of visualization and motion cues.  Examples include vehicle ignition, engine
idle, squealing tires, cone strikes, spinout/crash, police siren, and vehicle
shutdown.  Each cue can be made to vary
in pitch/gain in accordance with the present state of the simulation.  The audio states are sent to the mixing
board, are processed, and then are delivered to the amplifier, which delivers the
audio signal to a 2.1 stereo sound system, comprised of front-left
(SS-L), front-right (SS-R), and subwoofer (SS-SW) channels.


### 4.3.8. Emergency Stop Switch Is Available, at All Times, to Terminate the Simulation (Process h)

Throughout the simulation, both the simulation driver (on
board) and simulation operator (off board) can activate an Emergency Stop
switch (to view the on-board switch, refer to bottom-right corner of Figure6). When
the red button is struck, the motion simulation comes to an immediate,
premature, and smooth conclusion. Such a safety device is required in the case of a
hardware malfunction or in the event of
driver panic/illness, or any other circumstance that might deem premature termination of
the present simulation.The
next section discusses the incorporation of the simulation game described into
a Road Vehicle Dynamics (RVD) engineering course curriculum, using an
experiential learning context.


## 4.3.1. Establish Network Connection with Motion Platform (MP) (Process a)

The computer integrated into the MP can send/receive motion
commands, in the form of datagrams [26], from another computer on the network.
Figure 5 presents a screen capture of Moog’s Base Maintenance Software [27], for monitoring the state of the six actuators on the motion platform
(e.g., error messages, status messages, etc.) in real-time as the simulation
executes. This software runs on the
On-Board Computer (OBC) and is displayed on an off-board monitor.Figure 5
OBC display screen.


## 4.3.2. User Controls Simulation with Human Interface Device (HID) (Process b)

The driver controls the simulation while on-board the MP by
way of a USB steering wheel and pedals.  Commands
are captured using DirectX’s DirectInput Application Programmer’s Interface
(API) [28, 29] in real-time. The selected gaming device, shown in Figure 6,
contains numerous components that must be continually tracked, including the
accelerator, brake, clutch pedals, the single-axis motion of the steering
wheel, the paddle shifters, and 6 buttons located on the steering wheel.Figure 6
On-board HID.


## 4.3.3. Simulation Computer (SC) Receives Inputs, Performs Analysis for Current Time Step (Process c)

The states of the vehicle dynamics
model (described in Section5) are now computed at 60 Hertz and must be
converted into DOF's (i.e., roll, pitch, yaw, heave, surge, and sway)—see Figure 7. This conversion typically involves
scaling, limiting, and tilt coordination [30], collectively known as washout filtering [31].  The work presented here uses a
proprietary filter based upon a classical washout algorithm [32].Figure 7
Motion platform DOF's.


## 4.3.4. Updated State Delivered to Projection System (PS) and Visualization Screens (VSs) (Process d)

The scene graphics are now updated
and sent to the PS and to the three 48 ft2 VSs. The simulation
graphics have been developed using OpenGL [33], selected primarily to demonstrate that a useful game-based learning
environment can be created without the optimizations of a scene graph toolkit
(e.g., OpenSceneGraph [34] and Delta3D [35]). The virtual world is populated with a sky, ground (grass, pavement),
the driven vehicle and adjacent vehicles, track/roadway obstacles (e.g. cones),
curbs, walls, trees, roadway signs, the vehicle control panel, and various
other details to promote an authentic representation of a test track. Figure 8
presents screen captures of various simulations.(Clockwise, from (a) Experiment 1 (“Skid Pad”), (b) Experiment 2 (“Tri-Radii”),  (c) Future Work (“Networked Karting”), and (d) Experiment 3 (“Millersport Hwy”).
(a)(b)(c)(d)


## 4.3.5. DOF’s Sent to Motion Platform Interface Computer (MPIC) (Process e)

The updated DOF's are delivered by the SC to the MPIC,
dedicated to the motion process and requires an uninterrupted 60 Hz flow of
data. This is accomplished using Parallel Virtual Machine (PVM) [36], a networking API based upon TCP/IP Socket programming [37] and coded in ANSI C. PVM allows us to easily and reliably send data
packets between computers and handles cross-platform, nonheterogeneity issues
transparently (e.g., endian/byte-order concerns, char-to-float data conversion,
etc.). Once the MPIC receives the updated DOF's, these commands are finally
delivered directly to the MP for motion processing.


## 4.3.6. MPIC Delivers Computed DOF’s to MP (Process f)

An
“instance” of the MP is declared and assigned an IP address.  The instance is initialized, and then motion
commands are updated and delivered to the MP at a rate of precisely 60 Hz.  When the simulation is complete, the MP
instance is then shutdown, thereby completing the motion process.


## 4.3.7. Audio States Delivered to Mixer/Amplifier (MA) and 2.1 Sound System (SS) (Process g)

The simulation game uses OpenAL [38] for adding sound events synchronized with the graphical
simulation.  Sound is a vital simulation
cue, and it enhances the impact of visualization and motion cues.  Examples include vehicle ignition, engine
idle, squealing tires, cone strikes, spinout/crash, police siren, and vehicle
shutdown.  Each cue can be made to vary
in pitch/gain in accordance with the present state of the simulation.  The audio states are sent to the mixing
board, are processed, and then are delivered to the amplifier, which delivers the
audio signal to a 2.1 stereo sound system, comprised of front-left
(SS-L), front-right (SS-R), and subwoofer (SS-SW) channels.


## 4.3.8. Emergency Stop Switch Is Available, at All Times, to Terminate the Simulation (Process h)

Throughout the simulation, both the simulation driver (on
board) and simulation operator (off board) can activate an Emergency Stop
switch (to view the on-board switch, refer to bottom-right corner of Figure6). When
the red button is struck, the motion simulation comes to an immediate,
premature, and smooth conclusion. Such a safety device is required in the case of a
hardware malfunction or in the event of
driver panic/illness, or any other circumstance that might deem premature termination of
the present simulation.The
next section discusses the incorporation of the simulation game described into
a Road Vehicle Dynamics (RVD) engineering course curriculum, using an
experiential learning context.


## 5. Gaming Implementation

The simulation game is
incorporated into a two-course sequence of technical electives on automobile
vehicle dynamics, Road Vehicle Dynamics 1 and 2 (RVD1 and RVD2). These courses are open to senior
undergraduate and graduate students.
Between 60 and 75 students typically enroll in RVD1, with over
two-thirds continuing in RVD2.RVD1 is an introductory course
on the basics of automobile motion, stability, and control.  This includes a review of tire performance
and modeling, exploration of the elementary Bicycle Model of vehicle dynamics,
and the development of a more detailed four wheel model [39].
A secondary goal of the course is to apply engineering skills and techniques
learned in the first three years of the mechanical engineering curriculum to a
specific field, in this case the field of vehicle dynamics. The broad nature of
the material naturally lends itself to such a review, reinforcing the value of
the engineering curriculum, giving students the satisfaction of being able to
apply their engineering skills to a particular topic and mimics the process of
merging these foundational abilities with details of a specific knowledge area
as will be required when entering the workforce. The course traditionally has
consisted of daily lectures, weekly homework assignments, and three exams.RVD2 builds on RVD1, exploring
advanced techniques such as quasistatic vehicle analysis.  Oscillations of the sprung and unsprung
masses are investigated with a focus on ride comfort, and the design and
analysis of suspensions are covered.  The
course has more open-ended material and higher expectations of independent
student learning, with several paper reviews, in-class presentations and projects
throughout the semester.


### 5.1. Experiment 1: Low-Fidelity Simulation, Introductory Gaming Scenario in RVD1

The first experiment is presented
in the context of the overall methodology, as presented in Section4.2.Define Learning Outcomes
The learning objective of the first
experiment is for students to discover through experiential gaming the fundamental
effects that tire properties and vehicle center of gravity location have on
vehicle stability, control, and overall performance.  Prior to the exercise, students complete the
first five weeks of the course focusing mostly on tire behavior and modeling,
including a basic introduction to a standard dynamic model of the automobile,
although this is limited to the structure of the model with no discussion of
its behavior.Simulation Activities
The simulation model used in the
first experiment is the classic Bicycle Model of the automobile [39].  This model makes many simplifying assumptions
that allow it to be easily analyzed and understood, while still providing all
the fundamental characteristics of a real vehicle.  The key elements of the Bicycle Model are as
follows. It has three degrees-of-freedom (forward velocity, lateral velocity,
and yaw rate), plus those used to track the motion of the vehicle in the world
(forward position, lateral position, and heading angle).  Both front tires are treated as a single
tire, as are both rear tires, thus the Bicycle Model name. Figure 9 shows a top
view of the Bicycle Model representation.
The tires are represented as bilinear, as illustrated in Figure 10,
described by (i) a cornering stiffness slope at low slip angles, and (ii) as a
constant lateral force output above a certain “breakaway” slip angle.
Driver inputs from the on-board
steering wheel and pedals are used as inputs to the Bicycle Model which
generates updated model states as outputs through numerical integration during
every time step. One environment in the simulation game replicates a proving
ground skidpad, which is a circular course (of outer radius 500 feet), with
street lines indicating several internal lanes of varying radii. A significant
amount of vehicle testing and tuning has been performed at skidpad facilities [40–42].  This experiment performed for this research
effort involves one of the fundamental tests—driving at ever-increasing speed
while attempting to maintain a given radius.Figure 9
The bicycle model of the automobile.Figure 10
The bilinear tire model, shown
for turning in one direction.Task Ordering:
Tasks 1 and 2
In the
first two driving tasks, the driver is given a vehicle with equal strength (i.e.,
identical cornering stiffness and breakaway slip angle) tires on the front and
rear.  The first task places the center
of gravity (CG) ahead of the vehicle midpoint, while the second task places it
behind the vehicle midpoint. The student drives around the skidpad at slowly
increasing speed, up to (and beyond) the tire saturation points.  Students are asked to describe how the
vehicle felt, how stable it was, and how their steering input changed as speed
increased.  They are also asked to
describe what happened when a tire (front or rear) saturated.  These tasks expose the students to vehicles
with different understeer gradients, stability indices, yaw rate responses, and
limit behaviors.
In Task 1, the CG is ahead of the
vehicle midpoint, resulting in a vehicle with static and dynamic stability. In
short, the vehicle does not spin out while cornering. As vehicle speed
increases, the amount of steering required to stay on the constant radius
circle also increases, indicative of an understeer vehicle. This is also
consistent with passenger car behavior (i.e., passenger cars are designed to
have understeer). Task 1 saturates the front tires first, forcing the driver to
slow down in order to tighten the turn.
In Task 2, the CG is behind the
vehicle midpoint, resulting in a vehicle with static instability but with
dynamic stability up to a certain speed.
Beyond a certain speed the vehicle becomes unstable and spins out.  Unlike the understeer car of Task 1, this
oversteer car requires less and less steering to stay on the circle as speed
increases.  Task 2 saturates the rear
tires first, resulting in a spin-out.
Tasks 1 and 2 allowed the driver
to become familiar with the motion simulator and experience the behavior of two
very different vehicles.  Task 3 built
upon these experiences.Task 3
In the third task
the driver is given a vehicle with the CG at the vehicle midpoint and chooses
tires that have the cornering stiffness distribution biased to either the front
or the rear.  Based on that decision, the
driver is asked to predict if the car will feel like the one driven in either
the first or the second task.  This
prediction is not confirmed by the instructor before the trial run, but instead
the student is asked to indicate as soon as possible while driving which of the
previous cars it felt like.This task enables students to
develop hypotheses about the relative location of the CG and the cornering
stiffness distribution, test the hypothesis, and draw conclusions.  In short, if the CG is forward of the
cornering stiffness distribution the vehicle has understeer, is stable, and
behaves similarly to the vehicle in Task 1.
When the CG is aft of the cornering stiffness distribution the vehicle
has oversteer, is unstable above a certain speed, and behaves similarly to the
vehicle in Task 2.Task 4
The fourth task challenged
the driver to optimize the CG location while driving to achieve the fastest
possible speed around the skidpad. Students used two buttons on the steering
wheel to move the center of gravity and felt the stability and response of the
vehicle change as they moved the CG. The cornering stiffness distribution is
unknown to the students in this case. This task allows students to further
develop and test their hypotheses about the relationship between vehicle
parameters.Session Implementation
A total of 73 students
participated in the experiments, separated into 11 groups.  Each group spent an hour with the motion
simulation system performing the four specific driving tasks.  At the end of the experiment each group
received a copy of the data collected during all the runs for their analysis.Assessment
The assessment phase focused on both how well the students learned the key concepts in each experiment, but how
their gaming experiences impacted their education and learning
opportunities.Learning Objectives
In
Tasks 1 and 2, students were asked while driving if they were steering more or
less as speed increased, and all were able to notice the trends.  Students noted in Task 2 how much more
concentration and steering corrections it took to keep this vehicle on the
circle. Students also noted how the vehicle cornered more “tail out” than the
Task 1 vehicle—this is an
observation that would have been difficult to make without the motion cues on
the motion platform or without the use of a real vehicle.
After the experiment, students
analyzed their results, including a plot of steering wheel angle versus lateral
acceleration. A sample of one of the student plots is shown in Figure11 for the
Task 1 vehicle.  The slope of this curve
is the understeer gradient, and the intercept is the Ackermann steer angle [42]. These values are shown by the linear fit to
the recorded data. The “noise” in the data is a result of the student
correcting the vehicle path by steering while increasing speed—its very
difficult to trace a circle precisely when speed is varying.  The excursion in the right end of the data is
attributed to the start of the run when the vehicle first starts moving.  The excursion at the left end of the data
shows what happens when the front tires saturate.
A similar plot was produced for
the Task 2 vehicle, as shown in Figure12.
Here, the slope of the steering angle versus lateral acceleration curve
is negative, indicating an oversteer vehicle.
Oversteer vehicles are dynamically stable up to a certain critical speed
due to yaw damping, above which they want to spin out. By the time the vehicle
is cornering over −25 ft/sec2 the amount of steering required to
stay on the circle is nearly zero.  At
the far left of the diagram the steering trace shoots upward sharply—this is a result
of the driver trying to catch the vehicle as it spins out.
In Task 3, when the students were
asked to announce as soon as possible which vehicle (Task 1 or Task 2) the car
felt like, they were not able to do so at low speeds.  It was not until speeds increased above
approximately 35 mph that students could definitely tell the difference. There
are two reasons for this. First is the overwhelming presence of stabilizing yaw
damping at low speeds. Since both cars were very stable at low speeds their
response felt similar.  It was not until
speed increased and yaw damping diminished that the differences in behavior
were easily detected, at which point they were obvious. Second, at low speed it
is difficult to accurately trace a large radius circle. Thus, it was difficult
for students to tell if the steering angle required was increasing or decreasing
with speed when speeds were low. These kinds of experiential-based insights
would have been impossible without the gaming experiment.
In Task 4, by the end of the experiment,
every group was able to empirically place the CG within 1-2% of the theoretical
optimum.  Students were asked what
strategy they were going to use to find the optimum as the experiment
proceeded.  By the second and third
drivers, the students realized that they needed to approach the optimum from
the stable side (i.e., CG too far forward) to avoid spin-outs. They would note
the CG location when a spin-out did occur, and they made sure to keep the CG
ahead of that point during further adjustments.Figure 11
Student data for a Task 1 vehicle showing the understeer gradient.Figure 12
Student data for the Task 2 vehicle.Impact on Education
A
survey was administered to the students in the RVD1 class at the beginning (pretest)
and then again at the end (posttest) of the semester.  The survey contained the open-ended question.How could engineering education be
improved? An outside evaluator read
and coded the student responses placing them in the categories shown in Table 1. Student responses commonly fell into categories that suggested the need for
the environment that the RVD1 class was trying to provide as shown in Table 1.  While it would be speculation, it is
interesting to note how the number of responses in items 2 and 3 dropped.  Was that due in part to the experiences the
students had in the RVD1 course?
Similarly, it is interesting to note how the number of responses to item
6 increased.  Again, was that due in part
to the students’ perceived value of RVD1, an elective course? Further investigation is needed to answer
these questions.  Also, specific comments
to the question, when posed at the end of the semester, seem to highlight the
benefit of the incorporation of the simulation game into RVD1.
It is also worthy to note that 43
of the 73 people (59%) enrolled in the Road Vehicle Dynamics 1 course in the
fall 2007 semester enrolled in the Road Vehicle Dynamics 2 course (which
contained the advanced gaming module).
Considering that both are elective courses in degree programs that allow
for just 2-3 electives speaks to the student response to the courses.  In addition, a number of the 30 students who
did not enroll in the sequel class were graduating seniors and therefore were
not eligible to enroll in the course.Table 1
Pre- and posttest open responses.
How could engineering education be improved?PretestPosttest66 students
responded*60 students
responded*(1)
More hands-on experiences17 (26%)18 (30%)(2)
More practical/authentic/realistic experiences13 (20%)8 (13%)(3) More experience with
what is done in industry including the technology and equipment currently
used in industry10 (15%)4 (7%)(4)
Required internships/more internships7 (11%)11 (18%)(5)
More group/interactive experiences4 (6%)2 (3%)(6)
More electives offered earlier2 (3%)7 (12%)(7) More tours of/field
trips to engineering companies and facilities1 (2%)3 (5%)Note* that some students
wrote multipart responses that were classified in more than one category.Experiment Redesign
Based on the feedback of the
participants, instructors, and technical support personnel, a new set of
experiments were designed for the following course, Road Vehicle Dynamics
2.  This included more advanced
simulation models, gaming environments, and instructional tasks.


### 5.2. Experiment 2: High-Fidelity Simulation, Advanced Gaming Setting

The second experiment is also
presented in the context of the overall methodology, as presented in Section4.2.Define Learning Outcomes
The learning objective of the second
experiment is for students to discover through both their first hand experience
and a postprocessing of the data they generated, the fundamental dynamics and
impact of the roll stiffness distribution, roll center heights, friction
ellipse effects, weight transfer, and the dropped-throttle oversteer.  In addition, a secondary objective is for
students to understand g-g diagrams and moment method (CN-AY) diagrams using
the data generated during the experiment.Simulation Activities
There were two major additions to
the vehicle model for Experiment 2.  The
first is a new tire model. Unlike the linear tire model of Experiment 1,
Experiment 2 uses a nonlinear and load sensitive tire model.  This model was based on tire data collected
at the Calspan Tire Research Facility [43]
for the FSAE Tire Test Consortium [44]
and subsequently modified to suit the specific vehicle application. It is in
the form of a Nondimensional Tire Model [42]
and represents tire lateral force as a function of vehicle slip angle and
normal load.
The second addition involved the
transition from a Bicycle Model to a full four-wheel vehicle model with lateral
and longitudinal load transfer based on vehicle lateral and longitudinal
accelerations. This calculates wheel loads at any given vehicle operating
condition so that the load-sensitive tire model can be utilized. A simplified
suspension, represented by a roll stiffness distribution and front/rear roll
center heights [42],
allows additional ways to change vehicle handling characteristics over
Experiment 1. While the resulting model is still very simple compared to
comprehensive vehicle simulations, it is the next step towards such models and makes
for a very useful educational  too.Task Ordering:
Task 1
In the first task,
the student revisits the skidpad of Experiment 1 (Section5.1). Each student
drives a baseline vehicle at ever increasing speeds up to the limit, all the
while being conscious of how much steering is required to stay on the circular
skidpad. Then, one of the parameters (roll stiffness distribution, front roll
center height, or rear roll center height) is set to a new value and the student
is asked to describe the effect of this change. Task 1 also introduced students
to the ability to change the values of the vehicle parameters while
driving.  The steering wheel contained
three thumb-activated buttons on each side of the wheel, one each for roll stiffness
distribution, front roll center height, and rear roll center height.  Once the student had completed the understeer
gradient part of the task, he/she was asked to adjust the parameter to
experience its effects on vehicle handling, and then to determine an optimum
parameter to achieve the highest speed on the skidpad.Tasks 2 and 3
Tasks 2 and
3 presented a new driving world to the students, named the “Tri-Radial
Speedway”. The name arises from the design of the racetrack in the simulation—three corners each with a different radius plus one long straightaway (layout of
the track is shown in Figure13). In Task 2, the students were given a baseline
car and told to drive around the track as fast as possible, which achieved two
purposes. First, it allowed students to learn the track and identify reference
points for braking, turning, and so on. Secondly, it acclimated drivers with the
vehicle performance. Driving at the limit on a skidpad is different from
driving at the limit on a racecourse in that on the skidpad the vehicle is
always at approximately constant speed. On the racetrack the vehicle speed is
changing. Under braking there is load transfer to the front of the vehicle
which is destabilizing and could result in a more uncontrollable vehicle.Figure 13
Student vehicle paths plotted
on the Tri-Radial speedway.In Task 3, students were asked to
vary one of the three vehicle parameters while driving to achieve a better lap
time than they could with the baseline vehicle.
Here students had to consider the tradeoffs in setting-up a
vehicle.  How much steady-state cornering
do you sacrifice for good turn-in stability?
Are the settings that feel good to the driver identical to the
theoretical optimum with a perfect driver?
The answer is “no”, and a compromise needs to be reached.  How much of a compromise depends on how
smooth the driver is.Session Implementation
A total of 41 students
participated in the experiments, separated into 8 groups.  Each group spent an hour with the motion
simulation system performing the three specific driving tasks.  At the end of the experiment each group
received a copy of the data collected during all the runs for their analysis.Assessment
The assessment phase for this
experiment also focused both on how well the students learned the key concepts
in each experiment, and how their gaming experiences impacted their education
and learning opportunities.Learning Objectives
In
the assessment of Task 1, the students prepared plots of understeer gradients
similar to Figures11 and 12 to measure the changes and compare with their
comments.  Students universally commented
on how much more realistic the model from the high-end gaming experiment felt
compared against the bicycle model in the low-end experiment.  The ability to change the values of the
vehicle parameters also proved to be a very instructive tool, as students
quickly learned how a change in a vehicle parameter resulted in a change in
vehicle performance, in what direction and in what magnitude.
In Task 2, students were tasked
to drive around the racetrack shown in Figure13 as fast as possible. The figure
also plots the sum of vehicle paths for approximately 25 drivers, including off
course excursions.  The direction of
travel is counterclockwise.
While the track is not
complicated, the large number of paths off course in the one right-handed
corner indicates that there was definitely a learning curve at work—almost
every driver overshot that corner on their first lap.  Within a handful of laps most students were
driving proficiently, although not necessarily expertly, on the course.  With so many students being adept at video
games the transition to the simulation in this kind of learning environment was
faster than expected.
Many students learned about the
vehicle instabilities at varying speeds abruptly when they entered the first
turn too fast (top of Figure13), braked too hard, and spun themselves out.  They also experienced lateral force rolloff,
the reduction in lateral force with large tractive, or braking forces.  This was evident in the entrance to the
straightaway as drivers who applied too much throttle found the rear of the
vehicle difficult to control.
In Task 3 assessment, students created
their own g-g and CN-AY diagrams [39].
Figure 14 shows a g-g diagram for a smooth driver over four laps on the
course.  This plot of planar vehicle
accelerations illustrates how much time the vehicle spends at the lateral
acceleration limits for this vehicle (approx. 0.9 g). Figure 15 presents a
CN-AY diagram for the same driver.  This
diagram presents yaw acceleration versus lateral acceleration, and a smooth driver
will have very small values on the y-axis.
Figures16 and 17 show similar
figures for a driver who is much less smooth over the course of four laps. This
data includes one spinout.  Compared with
the previous driver there is no distinct pattern in these plots.  The first driver's data is more in line with
what a professional proving ground driver or race driver would produce from
in-car measurements.Figure 14
g-g diagram for a smooth driver over four laps.  Note the repeatability.Figure 15
CN-AY diagram for a smooth driver.  Note small yaw moment values.Figure 16
g-g diagram for a less-smooth driver.Figure 17
CN-AY for a less-smooth driver includes one spinout.Impact on Education
A more comprehensive survey was given at the end of the RVD2 class.  The survey results shown in Table2 reflect
the mean of the responses of the 41 students who completed the course (2
students resigned the course).  The items
in Table 2 capture the responses that most aligned with the objectives of the
gaming laboratory experience.  The
omitted items relate more to teaching methodology employed in the course. The
questions were on a 5-point Likert scale with 1 representing strongly disagree
and 5 representing strongly agree.  An
ANOVA was run for each item, and statistical significance between means, as
denoted in the table, was found for all of the survey items.  These responses provide evidence that
students perceived the RVD2 course (and RVD1 course), including the
laboratory gaming component to be of significant value in their engineering
education.
In addition, the survey also
included open-ended items, one of which asked them why they had enrolled in
RVD2.  All 41 students responded to this
item, some with multiple responses.  They
are categorized and compiled in Figure18.
Note that the fifth most popular response was one that mentioned
specifically the motion simulation gaming environment.  The most popular responses mention their
experiences in RVD1 which could certainly include the gaming experiences that
were part of the class.
An additional item on the survey
asked the RVD2 students if they would recommend either RVD1 or RVD2 to other
students.  All 41 RVD2 students responded
affirmatively.  Figure19 displays the
reasons that were given for this recommendation.  Note that the second most popular response
articulates the hand-on gaming experience in both classes.Table 2
RVD ratings survey results.
RVD1 &
RVD2 have…Other engineering courses have…Exposed me to genuine
engineering problems**4.4393.195Allowed for hands-on
learning experiences**4.5122.902Prepared me for the
workplace**3.9513.097Allowed me to use the
types of technology and facilities that engineers use in today's workforce**3.8782.951Offered me a chance to
identify and formulate engineering problems**4.2203.610Provided engaging learning
opportunities**4.5373.293Made use of problems and
situations similar to those that I expect to face in the workplace**3.8543.049Helped me be more familiar
with what a practicing engineer does**3.8293.073Given me opportunities toperform experiments in engineering**4.2683.244Given me opportunities toanalyze engineering data**4.7323.537Given me opportunities tointerpret engineering data**4.7073.463Presented new ideas and
material in a realistic context**4.4633.341P**<.001.Figure 18
RVD2 responses to the question “Why did you take RVD2?”.Figure 19
Reasons to take RVD1 and RVD2.Experiment Redesign
Section6 addresses some overall insights and
conclusions from the collective set of experiments.  It also presents some ideas toward future
redesigns of the experiments, based on the collected feedback.


## 5.1. Experiment 1: Low-Fidelity Simulation, Introductory Gaming Scenario in RVD1

The first experiment is presented
in the context of the overall methodology, as presented in Section4.2.Define Learning Outcomes
The learning objective of the first
experiment is for students to discover through experiential gaming the fundamental
effects that tire properties and vehicle center of gravity location have on
vehicle stability, control, and overall performance.  Prior to the exercise, students complete the
first five weeks of the course focusing mostly on tire behavior and modeling,
including a basic introduction to a standard dynamic model of the automobile,
although this is limited to the structure of the model with no discussion of
its behavior.Simulation Activities
The simulation model used in the
first experiment is the classic Bicycle Model of the automobile [39].  This model makes many simplifying assumptions
that allow it to be easily analyzed and understood, while still providing all
the fundamental characteristics of a real vehicle.  The key elements of the Bicycle Model are as
follows. It has three degrees-of-freedom (forward velocity, lateral velocity,
and yaw rate), plus those used to track the motion of the vehicle in the world
(forward position, lateral position, and heading angle).  Both front tires are treated as a single
tire, as are both rear tires, thus the Bicycle Model name. Figure 9 shows a top
view of the Bicycle Model representation.
The tires are represented as bilinear, as illustrated in Figure 10,
described by (i) a cornering stiffness slope at low slip angles, and (ii) as a
constant lateral force output above a certain “breakaway” slip angle.
Driver inputs from the on-board
steering wheel and pedals are used as inputs to the Bicycle Model which
generates updated model states as outputs through numerical integration during
every time step. One environment in the simulation game replicates a proving
ground skidpad, which is a circular course (of outer radius 500 feet), with
street lines indicating several internal lanes of varying radii. A significant
amount of vehicle testing and tuning has been performed at skidpad facilities [40–42].  This experiment performed for this research
effort involves one of the fundamental tests—driving at ever-increasing speed
while attempting to maintain a given radius.Figure 9
The bicycle model of the automobile.Figure 10
The bilinear tire model, shown
for turning in one direction.Task Ordering:
Tasks 1 and 2
In the
first two driving tasks, the driver is given a vehicle with equal strength (i.e.,
identical cornering stiffness and breakaway slip angle) tires on the front and
rear.  The first task places the center
of gravity (CG) ahead of the vehicle midpoint, while the second task places it
behind the vehicle midpoint. The student drives around the skidpad at slowly
increasing speed, up to (and beyond) the tire saturation points.  Students are asked to describe how the
vehicle felt, how stable it was, and how their steering input changed as speed
increased.  They are also asked to
describe what happened when a tire (front or rear) saturated.  These tasks expose the students to vehicles
with different understeer gradients, stability indices, yaw rate responses, and
limit behaviors.
In Task 1, the CG is ahead of the
vehicle midpoint, resulting in a vehicle with static and dynamic stability. In
short, the vehicle does not spin out while cornering. As vehicle speed
increases, the amount of steering required to stay on the constant radius
circle also increases, indicative of an understeer vehicle. This is also
consistent with passenger car behavior (i.e., passenger cars are designed to
have understeer). Task 1 saturates the front tires first, forcing the driver to
slow down in order to tighten the turn.
In Task 2, the CG is behind the
vehicle midpoint, resulting in a vehicle with static instability but with
dynamic stability up to a certain speed.
Beyond a certain speed the vehicle becomes unstable and spins out.  Unlike the understeer car of Task 1, this
oversteer car requires less and less steering to stay on the circle as speed
increases.  Task 2 saturates the rear
tires first, resulting in a spin-out.
Tasks 1 and 2 allowed the driver
to become familiar with the motion simulator and experience the behavior of two
very different vehicles.  Task 3 built
upon these experiences.Task 3
In the third task
the driver is given a vehicle with the CG at the vehicle midpoint and chooses
tires that have the cornering stiffness distribution biased to either the front
or the rear.  Based on that decision, the
driver is asked to predict if the car will feel like the one driven in either
the first or the second task.  This
prediction is not confirmed by the instructor before the trial run, but instead
the student is asked to indicate as soon as possible while driving which of the
previous cars it felt like.This task enables students to
develop hypotheses about the relative location of the CG and the cornering
stiffness distribution, test the hypothesis, and draw conclusions.  In short, if the CG is forward of the
cornering stiffness distribution the vehicle has understeer, is stable, and
behaves similarly to the vehicle in Task 1.
When the CG is aft of the cornering stiffness distribution the vehicle
has oversteer, is unstable above a certain speed, and behaves similarly to the
vehicle in Task 2.Task 4
The fourth task challenged
the driver to optimize the CG location while driving to achieve the fastest
possible speed around the skidpad. Students used two buttons on the steering
wheel to move the center of gravity and felt the stability and response of the
vehicle change as they moved the CG. The cornering stiffness distribution is
unknown to the students in this case. This task allows students to further
develop and test their hypotheses about the relationship between vehicle
parameters.Session Implementation
A total of 73 students
participated in the experiments, separated into 11 groups.  Each group spent an hour with the motion
simulation system performing the four specific driving tasks.  At the end of the experiment each group
received a copy of the data collected during all the runs for their analysis.Assessment
The assessment phase focused on both how well the students learned the key concepts in each experiment, but how
their gaming experiences impacted their education and learning
opportunities.Learning Objectives
In
Tasks 1 and 2, students were asked while driving if they were steering more or
less as speed increased, and all were able to notice the trends.  Students noted in Task 2 how much more
concentration and steering corrections it took to keep this vehicle on the
circle. Students also noted how the vehicle cornered more “tail out” than the
Task 1 vehicle—this is an
observation that would have been difficult to make without the motion cues on
the motion platform or without the use of a real vehicle.
After the experiment, students
analyzed their results, including a plot of steering wheel angle versus lateral
acceleration. A sample of one of the student plots is shown in Figure11 for the
Task 1 vehicle.  The slope of this curve
is the understeer gradient, and the intercept is the Ackermann steer angle [42]. These values are shown by the linear fit to
the recorded data. The “noise” in the data is a result of the student
correcting the vehicle path by steering while increasing speed—its very
difficult to trace a circle precisely when speed is varying.  The excursion in the right end of the data is
attributed to the start of the run when the vehicle first starts moving.  The excursion at the left end of the data
shows what happens when the front tires saturate.
A similar plot was produced for
the Task 2 vehicle, as shown in Figure12.
Here, the slope of the steering angle versus lateral acceleration curve
is negative, indicating an oversteer vehicle.
Oversteer vehicles are dynamically stable up to a certain critical speed
due to yaw damping, above which they want to spin out. By the time the vehicle
is cornering over −25 ft/sec2 the amount of steering required to
stay on the circle is nearly zero.  At
the far left of the diagram the steering trace shoots upward sharply—this is a result
of the driver trying to catch the vehicle as it spins out.
In Task 3, when the students were
asked to announce as soon as possible which vehicle (Task 1 or Task 2) the car
felt like, they were not able to do so at low speeds.  It was not until speeds increased above
approximately 35 mph that students could definitely tell the difference. There
are two reasons for this. First is the overwhelming presence of stabilizing yaw
damping at low speeds. Since both cars were very stable at low speeds their
response felt similar.  It was not until
speed increased and yaw damping diminished that the differences in behavior
were easily detected, at which point they were obvious. Second, at low speed it
is difficult to accurately trace a large radius circle. Thus, it was difficult
for students to tell if the steering angle required was increasing or decreasing
with speed when speeds were low. These kinds of experiential-based insights
would have been impossible without the gaming experiment.
In Task 4, by the end of the experiment,
every group was able to empirically place the CG within 1-2% of the theoretical
optimum.  Students were asked what
strategy they were going to use to find the optimum as the experiment
proceeded.  By the second and third
drivers, the students realized that they needed to approach the optimum from
the stable side (i.e., CG too far forward) to avoid spin-outs. They would note
the CG location when a spin-out did occur, and they made sure to keep the CG
ahead of that point during further adjustments.Figure 11
Student data for a Task 1 vehicle showing the understeer gradient.Figure 12
Student data for the Task 2 vehicle.Impact on Education
A
survey was administered to the students in the RVD1 class at the beginning (pretest)
and then again at the end (posttest) of the semester.  The survey contained the open-ended question.How could engineering education be
improved? An outside evaluator read
and coded the student responses placing them in the categories shown in Table 1. Student responses commonly fell into categories that suggested the need for
the environment that the RVD1 class was trying to provide as shown in Table 1.  While it would be speculation, it is
interesting to note how the number of responses in items 2 and 3 dropped.  Was that due in part to the experiences the
students had in the RVD1 course?
Similarly, it is interesting to note how the number of responses to item
6 increased.  Again, was that due in part
to the students’ perceived value of RVD1, an elective course? Further investigation is needed to answer
these questions.  Also, specific comments
to the question, when posed at the end of the semester, seem to highlight the
benefit of the incorporation of the simulation game into RVD1.
It is also worthy to note that 43
of the 73 people (59%) enrolled in the Road Vehicle Dynamics 1 course in the
fall 2007 semester enrolled in the Road Vehicle Dynamics 2 course (which
contained the advanced gaming module).
Considering that both are elective courses in degree programs that allow
for just 2-3 electives speaks to the student response to the courses.  In addition, a number of the 30 students who
did not enroll in the sequel class were graduating seniors and therefore were
not eligible to enroll in the course.Table 1
Pre- and posttest open responses.
How could engineering education be improved?PretestPosttest66 students
responded*60 students
responded*(1)
More hands-on experiences17 (26%)18 (30%)(2)
More practical/authentic/realistic experiences13 (20%)8 (13%)(3) More experience with
what is done in industry including the technology and equipment currently
used in industry10 (15%)4 (7%)(4)
Required internships/more internships7 (11%)11 (18%)(5)
More group/interactive experiences4 (6%)2 (3%)(6)
More electives offered earlier2 (3%)7 (12%)(7) More tours of/field
trips to engineering companies and facilities1 (2%)3 (5%)Note* that some students
wrote multipart responses that were classified in more than one category.Experiment Redesign
Based on the feedback of the
participants, instructors, and technical support personnel, a new set of
experiments were designed for the following course, Road Vehicle Dynamics
2.  This included more advanced
simulation models, gaming environments, and instructional tasks.


## 5.2. Experiment 2: High-Fidelity Simulation, Advanced Gaming Setting

The second experiment is also
presented in the context of the overall methodology, as presented in Section4.2.Define Learning Outcomes
The learning objective of the second
experiment is for students to discover through both their first hand experience
and a postprocessing of the data they generated, the fundamental dynamics and
impact of the roll stiffness distribution, roll center heights, friction
ellipse effects, weight transfer, and the dropped-throttle oversteer.  In addition, a secondary objective is for
students to understand g-g diagrams and moment method (CN-AY) diagrams using
the data generated during the experiment.Simulation Activities
There were two major additions to
the vehicle model for Experiment 2.  The
first is a new tire model. Unlike the linear tire model of Experiment 1,
Experiment 2 uses a nonlinear and load sensitive tire model.  This model was based on tire data collected
at the Calspan Tire Research Facility [43]
for the FSAE Tire Test Consortium [44]
and subsequently modified to suit the specific vehicle application. It is in
the form of a Nondimensional Tire Model [42]
and represents tire lateral force as a function of vehicle slip angle and
normal load.
The second addition involved the
transition from a Bicycle Model to a full four-wheel vehicle model with lateral
and longitudinal load transfer based on vehicle lateral and longitudinal
accelerations. This calculates wheel loads at any given vehicle operating
condition so that the load-sensitive tire model can be utilized. A simplified
suspension, represented by a roll stiffness distribution and front/rear roll
center heights [42],
allows additional ways to change vehicle handling characteristics over
Experiment 1. While the resulting model is still very simple compared to
comprehensive vehicle simulations, it is the next step towards such models and makes
for a very useful educational  too.Task Ordering:
Task 1
In the first task,
the student revisits the skidpad of Experiment 1 (Section5.1). Each student
drives a baseline vehicle at ever increasing speeds up to the limit, all the
while being conscious of how much steering is required to stay on the circular
skidpad. Then, one of the parameters (roll stiffness distribution, front roll
center height, or rear roll center height) is set to a new value and the student
is asked to describe the effect of this change. Task 1 also introduced students
to the ability to change the values of the vehicle parameters while
driving.  The steering wheel contained
three thumb-activated buttons on each side of the wheel, one each for roll stiffness
distribution, front roll center height, and rear roll center height.  Once the student had completed the understeer
gradient part of the task, he/she was asked to adjust the parameter to
experience its effects on vehicle handling, and then to determine an optimum
parameter to achieve the highest speed on the skidpad.Tasks 2 and 3
Tasks 2 and
3 presented a new driving world to the students, named the “Tri-Radial
Speedway”. The name arises from the design of the racetrack in the simulation—three corners each with a different radius plus one long straightaway (layout of
the track is shown in Figure13). In Task 2, the students were given a baseline
car and told to drive around the track as fast as possible, which achieved two
purposes. First, it allowed students to learn the track and identify reference
points for braking, turning, and so on. Secondly, it acclimated drivers with the
vehicle performance. Driving at the limit on a skidpad is different from
driving at the limit on a racecourse in that on the skidpad the vehicle is
always at approximately constant speed. On the racetrack the vehicle speed is
changing. Under braking there is load transfer to the front of the vehicle
which is destabilizing and could result in a more uncontrollable vehicle.Figure 13
Student vehicle paths plotted
on the Tri-Radial speedway.In Task 3, students were asked to
vary one of the three vehicle parameters while driving to achieve a better lap
time than they could with the baseline vehicle.
Here students had to consider the tradeoffs in setting-up a
vehicle.  How much steady-state cornering
do you sacrifice for good turn-in stability?
Are the settings that feel good to the driver identical to the
theoretical optimum with a perfect driver?
The answer is “no”, and a compromise needs to be reached.  How much of a compromise depends on how
smooth the driver is.Session Implementation
A total of 41 students
participated in the experiments, separated into 8 groups.  Each group spent an hour with the motion
simulation system performing the three specific driving tasks.  At the end of the experiment each group
received a copy of the data collected during all the runs for their analysis.Assessment
The assessment phase for this
experiment also focused both on how well the students learned the key concepts
in each experiment, and how their gaming experiences impacted their education
and learning opportunities.Learning Objectives
In
the assessment of Task 1, the students prepared plots of understeer gradients
similar to Figures11 and 12 to measure the changes and compare with their
comments.  Students universally commented
on how much more realistic the model from the high-end gaming experiment felt
compared against the bicycle model in the low-end experiment.  The ability to change the values of the
vehicle parameters also proved to be a very instructive tool, as students
quickly learned how a change in a vehicle parameter resulted in a change in
vehicle performance, in what direction and in what magnitude.
In Task 2, students were tasked
to drive around the racetrack shown in Figure13 as fast as possible. The figure
also plots the sum of vehicle paths for approximately 25 drivers, including off
course excursions.  The direction of
travel is counterclockwise.
While the track is not
complicated, the large number of paths off course in the one right-handed
corner indicates that there was definitely a learning curve at work—almost
every driver overshot that corner on their first lap.  Within a handful of laps most students were
driving proficiently, although not necessarily expertly, on the course.  With so many students being adept at video
games the transition to the simulation in this kind of learning environment was
faster than expected.
Many students learned about the
vehicle instabilities at varying speeds abruptly when they entered the first
turn too fast (top of Figure13), braked too hard, and spun themselves out.  They also experienced lateral force rolloff,
the reduction in lateral force with large tractive, or braking forces.  This was evident in the entrance to the
straightaway as drivers who applied too much throttle found the rear of the
vehicle difficult to control.
In Task 3 assessment, students created
their own g-g and CN-AY diagrams [39].
Figure 14 shows a g-g diagram for a smooth driver over four laps on the
course.  This plot of planar vehicle
accelerations illustrates how much time the vehicle spends at the lateral
acceleration limits for this vehicle (approx. 0.9 g). Figure 15 presents a
CN-AY diagram for the same driver.  This
diagram presents yaw acceleration versus lateral acceleration, and a smooth driver
will have very small values on the y-axis.
Figures16 and 17 show similar
figures for a driver who is much less smooth over the course of four laps. This
data includes one spinout.  Compared with
the previous driver there is no distinct pattern in these plots.  The first driver's data is more in line with
what a professional proving ground driver or race driver would produce from
in-car measurements.Figure 14
g-g diagram for a smooth driver over four laps.  Note the repeatability.Figure 15
CN-AY diagram for a smooth driver.  Note small yaw moment values.Figure 16
g-g diagram for a less-smooth driver.Figure 17
CN-AY for a less-smooth driver includes one spinout.Impact on Education
A more comprehensive survey was given at the end of the RVD2 class.  The survey results shown in Table2 reflect
the mean of the responses of the 41 students who completed the course (2
students resigned the course).  The items
in Table 2 capture the responses that most aligned with the objectives of the
gaming laboratory experience.  The
omitted items relate more to teaching methodology employed in the course. The
questions were on a 5-point Likert scale with 1 representing strongly disagree
and 5 representing strongly agree.  An
ANOVA was run for each item, and statistical significance between means, as
denoted in the table, was found for all of the survey items.  These responses provide evidence that
students perceived the RVD2 course (and RVD1 course), including the
laboratory gaming component to be of significant value in their engineering
education.
In addition, the survey also
included open-ended items, one of which asked them why they had enrolled in
RVD2.  All 41 students responded to this
item, some with multiple responses.  They
are categorized and compiled in Figure18.
Note that the fifth most popular response was one that mentioned
specifically the motion simulation gaming environment.  The most popular responses mention their
experiences in RVD1 which could certainly include the gaming experiences that
were part of the class.
An additional item on the survey
asked the RVD2 students if they would recommend either RVD1 or RVD2 to other
students.  All 41 RVD2 students responded
affirmatively.  Figure19 displays the
reasons that were given for this recommendation.  Note that the second most popular response
articulates the hand-on gaming experience in both classes.Table 2
RVD ratings survey results.
RVD1 &
RVD2 have…Other engineering courses have…Exposed me to genuine
engineering problems**4.4393.195Allowed for hands-on
learning experiences**4.5122.902Prepared me for the
workplace**3.9513.097Allowed me to use the
types of technology and facilities that engineers use in today's workforce**3.8782.951Offered me a chance to
identify and formulate engineering problems**4.2203.610Provided engaging learning
opportunities**4.5373.293Made use of problems and
situations similar to those that I expect to face in the workplace**3.8543.049Helped me be more familiar
with what a practicing engineer does**3.8293.073Given me opportunities toperform experiments in engineering**4.2683.244Given me opportunities toanalyze engineering data**4.7323.537Given me opportunities tointerpret engineering data**4.7073.463Presented new ideas and
material in a realistic context**4.4633.341P**<.001.Figure 18
RVD2 responses to the question “Why did you take RVD2?”.Figure 19
Reasons to take RVD1 and RVD2.Experiment Redesign
Section6 addresses some overall insights and
conclusions from the collective set of experiments.  It also presents some ideas toward future
redesigns of the experiments, based on the collected feedback.


## 6. General Insights and Conclusions

This paper presents a method of
using a simulation game to present students with an authentic vehicle testing scenario.
The driving simulation environment is used to augment two standard road vehicle
dynamics course offerings.  In all, 73
students participated the low-end gaming tasks and 41 of these students
continued with the second course and participated in the high-end gaming
tasks.  Based on the experimental results
from performing these tasks, from in-laboratory observations (before, during,
and after the experiments), and based on the student surveys (before and after
the laboratory experiments), numerous conclusions can be drawn.(i)
The game scenarios were
successful in attaining their primary goal—to serve as a forum for experiential,
inquiry-based learning within an educational setting that had previously been
instructed exclusively by way of traditional, lecture-based classroom
approaches.  Students would first see and
experience the dynamics of a vehicle, hands-on, using the motion simulator, and
this exposure was followed by traditional instruction (i.e., representative
mathematical theory and governing dynamics equations) in the classroom
setting.(ii)
The instructor noted
significant differences between the students who engaged in the gaming
experiments and those from previous nongaming offerings of the courses with
respect to the learning objectives of each experiment.  He noted substantial and identifiable progress
in the students with respect to their comprehension of theoretical concepts and
application of these concepts to practical vehicle design issues.  In previous years a few in-class computer
simulations with plots of output variables were used to illustrate different
vehicle behaviors.  With the use of the motion
simulation experiment the connection between theory and reality was easier to
make since the students had experienced the various vehicle behaviors.  Interestingly, because of the realistic context
that the experiments provided, the instructor also noted a pronounced familiarity
with the technical vocabulary of the course after the experiments were
conducted.(iii)
For each of the laboratory
groups, the laboratory instructors could easily detect the progression of
knowledge and lesson comprehension during the experiments.  With the first driver, there would always be
a considerable amount of apprehension and guesswork, as the student would serve
as the first person to complete the exercise.
Based on that student’s successes and shortcomings, the second, and third
drivers would conquer the exercises much more quickly and confidently, and
generally speaking, improved driver performance in each session reflected this
trend.(iv)
With 114 students in 19
groups across two semesters, almost 200 experimental setups were performed.
Using a physical vehicle and test track or road course, the setup time would
have dominated the experimental process, limiting the students' ability to
explore the impact changes in configuration have on vehicle dynamics.(v)
The results from the course
surveys show that the course has a considerable impact on how students felt
about their engineering education and their perceived experiences in their
educational process. The use of experiential learning in the vehicle dynamics
curriculum increased students' opinions of their opportunity to have hands on
experiences, use modern engineering tools, and solve problems that were similar
to what they expect to see in the workplace. This outcome shows that using
simulation to provide authentic learning environments provides educators with a
means of following the guidance provided by ABET and the National Survey of
Student Engagement, with the ultimate goal of educating engineers that are better
prepared for the workforce.(vi)
Further development and
study will include more experiments aimed at using gaming environments to learn
key technical concepts including fundamental vehicle dynamics, driver-vehicle
interactions, and driver-to-driver interactions in networked simulations.  The networking feature will allow multiple
drivers to interact with one another within the same driving environment using
both TCP and UDP internet protocols.  In
addition, future plans could include developing a computing toolkit to allow
other researchers and educators interested in vehicle and motion simulation
gaming applications to efficiently develop environments and experiments,
similar to the more general serious game design and assessment toolkit in [45].  This toolkit could also be applied to a desktop
version of driving simulation environments, allowing for greater dissemination
and study.



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*Source: 952524-2009-04-29.xml*