Task and Vehicle Dynamics Based Assessment of Motion Cueing Requirements

William W. Chung[^1]

Science Applications International Corporation, Hampton, VA 23666

Judith Bürki-Cohen[^2]

USDOT/Volpe National Transportation Systems Center, Cambridge, MA 02142

and

Tiauw H. Go[^3]

Massachusetts Institute of Technology, Cambridge, MA 02139

One significant difference between real and simulated flight on the
ground are the stimuli or cues provided to the pilot. Due to physical
and/or cost constraints, it is nearly impossible to match all the cues
experienced in the air in ground-based simulators. Motion cues, in
particular, are severely affected by the limits imposed on the ground,
such as the extent of travel and the dynamics bandwidth. Researchers
have been struggling for decades to develop a better understanding on
how pilots\' behavior and performance in the simulator are affected by
these limitations, and to determine the motion-cueing requirements
appropriate to the purposes of the simulation. It has been demonstrated
that motion cues can affect pilot-vehicle performance and pilot behavior
in ground-based simulators. However, whether motion cues affect behavior
and performance appears to be a function of pilot task, vehicle
dynamics, and cueing quality (of motion and other cues). The
experimental design also greatly affects the validity (whether the data
answer the research question) and reliability (whether the results can
be replicated) of a study. This paper is developing a systematic
approach to re-examine past studies in an effort to develop a
comprehensive understanding of the effects of motion in ground-based
flight simulators.

Nomenclature

A~Xp~ = pilot station acceleration, longitudinal, ft/sec^2^

A~Yp~ = pilot station acceleration, lateral, ft/sec^2^

c.g. = center of gravity

DOF = degree of freedom

e^-τs^ = time delay in frequency domain

FOV = field of view

g = gravity acceleration, ft/sec^2^

$\overset{\text{.}}{h}$ = vertical speed, ft/sec

*I* = task input in frequency domain

K~P~ = constant in modeling pilot transfer function

K~V~ = constant in modeling vehicle dynamics

Lp = helicopter roll acceleration due to roll rate, 1/sec

*M* = aircraft states in frequency domain

msec = milliseconds

OTW = out the window

s = first derivative in frequency domain, sec^-1^

s^2^ = second derivative in frequency domain, sec^-2^

*Y~Pe~* = pilot transfer function in a compensatory path

*Y~Pi~* = pilot transfer function in the pursuit path

*Y~V~* = simulated vehicle transfer function

δ~c~ = collective input

δ~Lat~ = lateral stick input

δ~r~ = rudder input

ω~BW~ = first-order system bandwidth, rad/sec

ω~c~ = crossover frequency, rad/sec

ω~TASK~ = task frequency, rad/sec

$\overset{\text{.}}{\psi}$ = turn rate, rad/sec

$\overset{\text{.}\text{.}}{\psi}$ = yaw acceleration, rad/sec^2^

# Introduction

It is well known that human pilots will quite successfully adapt their
behavior to many different control situations, and in the process
optimize the pilot-vehicle performance for the task.^1^ Due to the
artifact of ground-based flight simulations, e.g., visual transport
delay, scene content, and specific force cues, the exact flying
experience in a real cockpit may not be fully reproduced in ground-based
flight simulators. Pilots are expected to change their control strategy
and behavior depending on perceived simulation cues under a given task.
How pilots adapt to simulation cues, and what the requirements are for
different types of simulation cues to fulfill different missions, has
been the focus of many studies. This is particularly true for motion
cues. Due to the travel limits imposed by the motion platform, all
ground-based simulators are restricted to issuing attenuated motion
commands. These commands use frequency-dependent motion-drive
algorithms,^2,3^ which depend on both the dynamics of the task to be
flown and the dynamics of the simulated airplane. The motion cueing
characteristics need to be carefully considered when designing and
analyzing the results of an experiment.^1^

A systematic approach is warranted to analyze and reach practical
conclusions from the vast body of literature available on motion cueing
in ground-based flight simulators. Without identifying all key factors
that directly influence pilots' control behavior in ground-based flight
simulations and considering them in the context of the entire controlled
system, conclusions drawn from individual experiments may only reveal
local effects due to specific simulation cueing elements. This will
limit the lessons that can be learned from these studies.

When considering an experiment, it is also very important to examine
whether the design is valid and reliable. The term \"validity\" relates
to whether the experiment does indeed answer the question posed by the
experimenter, or whether the results are an artifact of a design flaw.
\"Reliability\" refers to the ability to replicate an experiment and
produce the same results, which would show that the results are due to
the intended independent variables chosen by the experimenters instead
of some accidentally introduced variability.

Based on the above factors, this paper proposes an approach to review
past studies related to motion cueing. The paper summarizes the initial
steps of the approach, demonstrates their application to a few selected
studies, and makes recommendations for future elaboration of the
approach. Note that pilots respond to a combination of simulation cues
presented to them during simulated flight. Although the focus of this
paper is simulator motion, future expansions of the approach described
in this paper will also consider the fidelity of cues such as those
provided, e.g., by the control-force and sound systems.

# Task and Vehicle Dynamics

Using a closed-loop control system analogy, McRuer^4^ classifies tasks
into three types, i.e., compensatory, pursuit, and precognitive, as
shown in Fig. 1. In a compensatory task, the pilot reacts to
system-output errors, exercising closed-loop feedback control of the
airplane. In a pursuit task, in addition to responding to
task-performance error, the pilot also uses the information on the state
of the system when applying control inputs, e.g., the state of the lead
aircraft to be followed in a formation flight. Most of the tasks
performed in ground-based simulators are pursuit tasks. In pursuit
tasks, using information from the out-the-window (OTW) visual scene or
displays, pilots combine two control approaches, namely, open-loop
feed-forward control using target-state information to generate lead,
and closed-loop compensatory control using error information to correct
tracking errors. A third type of task, described as precognitive,
emerges when pilots are so familiar with the vehicle characteristics
that they can choose from a set of previously learned responses,
performing the task in an open-loop programmed-control-like fashion.

From Fig. 1, transfer functions between airplane states, m, and task, i,
and between airplane states and error, e, can be written as shown in
Eqs. (1) and (2). In these equations, if the pursuit path is dominant
and pilots can achieve an open-loop response of Y~Pi~Y~V~=1, then
\|M\|/\|I\|→1, and \|E\|/\|I\|→0.^1^ Estimated pilot transfer functions
according to this approach are summarized in Table
1.![](media/image1.wmf){width="2.7527777777777778in"
height="2.1638888888888888in"}

$\frac{M}{I} = \frac{Y_{V}\left( Y_{\text{Pi}} + Y_{\text{Pe}} \right)}{1 + Y_{V}Y_{\text{Pe}}}$
(1)

$\frac{E}{I} = \frac{1 - Y_{V}Y_{\text{Pi}}}{1 + Y_{V}Y_{\text{Pe}}}$
(2)

Table 1. Estimated primary functions of pilots in a pursuit task

+-------------------------+-------------------+------------------------+
| **Vehicle Transfer      | **Pilot Transfer  | **Pilot's Primary      |
| Function, Y~V~**        | Function, Y~Pe~** | Function**             |
+-------------------------+-------------------+------------------------+
| Constant: K~V~          | 1/K~V~            | Gain                   |
+-------------------------+-------------------+------------------------+
| Rate system: K~V~/s     | s/K~V~            | Rate                   |
+-------------------------+-------------------+------------------------+
| Rate system with a time | s(s+a)/K~V~       | Rate plus a lead       |
| constant, 1/a:          |                   |                        |
| K~V~/s(s+a)             |                   | Rate plus a gain, low  |
|                         |                   | frequency              |
|                         |                   | (ω~Task~\<\<a)         |
|                         |                   |                        |
|                         |                   | Acceleration, high     |
|                         |                   | frequency              |
|                         |                   | (ω~Task~\>\>a)         |
+-------------------------+-------------------+------------------------+
| Acceleration system:    | s^2^/K~V~         | Acceleration           |
| K~V~/s^2^               |                   |                        |
+-------------------------+-------------------+------------------------+

If, however, it is assumed that the compensatory path is dominant in
pilots\' control strategy, i.e., Y~Pi~=0, the closed-loop system is
simplified to a single loop as shown in Fig. 2. McRuer and Krendel^1^
modeled the dynamics of a pilot-vehicle feedback control system by
quantitatively describing pilot behavior for this type of task. From
experimental data they distilled the pilot-vehicle crossover model as
shown in Eq. (3), which indicates that pilots adapt their control
strategy so as to preserve such open-loop pilot-vehicle characteristics.
This pilot-vehicle open-loop relationship has been demonstrated in both
fixed-base and motion-base flight
simulators.^5^![](media/image2.wmf){width="3.2006944444444443in"
height="1.0340277777777778in"}

> *Y~Pe~Y~V~* = ω~c~ e^-τs^/s (3)

The approximate pilot transfer functions derived for the control-system
set investigated according to this crossover model are shown in Table 2.
The table shows how pilots adapt their control behavior or primary
function relative to the vehicle dynamics.

Table 2. Estimated primary functions of pilots in a compensatory task
according to the crossover model

+------------------------+------------------+-------------------------+
| **Vehicle Transfer     | **Pilot Transfer | **Pilot's Primary       |
| Function, Y~V~**       | Function,        | Function**              |
|                        | Y~Pe~**          |                         |
+------------------------+------------------+-------------------------+
| Constant: K~V~         | Κ~P~ e^-τs^/s    | Integrator              |
+------------------------+------------------+-------------------------+
| Rate system: K~V~/s    | Κ~P~ e^-τs^      | Gain                    |
+------------------------+------------------+-------------------------+
| Rate system with a     | Κ~P~(s+a)e^-τs^  | Lead                    |
| time constant, 1/a:    |                  |                         |
| K~V~/s(s+a)            |                  | Gain, low frequency     |
|                        |                  | (ω~Task~\<\<a)          |
|                        |                  |                         |
|                        |                  | Rate, high              |
|                        |                  | frequency(ω~Task~ \>\>  |
|                        |                  | a)                      |
+------------------------+------------------+-------------------------+
| Acceleration system:   | Κ~P~se^-τs^      | Angular rate or         |
| K~V~/s^2^              |                  | translational velocity  |
+------------------------+------------------+-------------------------+

# Visual Cues 

In ground-based flight simulators, pilots use the aircraft-state and
performance-error information acquired from simulation cues, i.e.,
visual, motion, inceptor cues, and/or aural cues, to generate
appropriate control commands to the simulated aircraft. Among them,
pilots rely most heavily on visual cues. They provide pilots with
spatial orientation and position relative to the tasks. Visual cueing
delay due to image generation and transport delay, which used to reduce
cue effectiveness, has been improved to an acceptable level. Another
technical advance is the availability of wide field-of-view (FOV)
display systems that provide peripheral vision. Peripheral vision has
been shown to play an important role in the emergence of vection, or the
illusion of motion.^6^

# Motion Drive Algorithms and Platform Dynamics

There are three major components in motion drive algorithms. They are
high-pass washout filter, tilt compensation, and coordinated crossover
logic due to angular motion.^3^ All three components are frequency
dependent, and their cueing effects are dependent on the
simulated-airplane dynamics and the flying tasks. High-pass washout
filters provide good motion phase responses at high frequency. However,
for frequencies below the washout-filter frequency, a
frequency-dependent phase lead relative to the visual cues is
inevitable. Tilt provides a tradeoff for simulating low frequency
translational motion cues, which typically demand large travel. Low
frequency sway and surge cues are produced by tilting the cockpit and
using the gravity to produce the desired specific force. By doing so,
simulator travel demand is preserved, but has to be constrained by tilt
rate and phase delay stemming from the use of a first-order low-pass
filter. Coordinated translational motion to minimize unintended gravity
force due to angular motion has been found to improve the motion
fidelity in rotorcraft flight simulations.^7,8^ Because low-frequency
angular motion has already been attenuated by the application of
high-pass filtering, tasks within a frequency range above the tilting
frequency are subject to cross feed.

# Experimental Validity and Reliability

In addition to the task and simulator characteristics, the outcome of an
experiment is also affected by the experiment design. An experiment is
considered \"valid\" if the design does indeed answer the question it is
supposed to answer. For example, data from questionnaires that pilots
completed after flying a simulator will not reveal the effect of
simulator characteristics on pilot-vehicle behavior and task
performance, but only on pilots' conscious perceptions. In fact, if
pilots knew which simulator characteristics were manipulated as part of
the experiment, their answers may reflect preconceived opinions rather
than their actual perceptions during the experiment. Even pilot-vehicle
performance data collected from the simulator may be misleading if
pilots were able to stabilize vehicle performance by adopting a
different strategy to compensate for cue deficiencies, e.g. To fully
understand the effect of the characteristics compared, it is important
to look at pilots' control inputs as well. Finally, for a valid answer
to the question of whether a particular simulator characteristic affects
the training of pilots to fly the real airplane, it is important to look
at how the simulator characteristics affect transfer of training to the
airplane rather than just the performance/behavior in the simulator.
Vice versa, when the simulator configuration is examined as a tool for
pilot proficiency checks, then transfer from the airplane to the
simulator needs to be tested. Because measurement of transfer between
the simulator and the airplane may not be possible for a variety of
reasons, researchers more and more resort to quasi-transfer between the
simulator configuration to be tested and a configuration representing a
higher degree of fidelity.^9,10^

Reliability refers to the ability of an experiment to be replicated.
Reliable data will be obtained in an experiment free of nuisance
variables such as drift in simulator performance or
measurement-instrument precision and idiosyncratic variability between
pilots. The first two can be avoided by careful calibration. Sampling
pilots from the same populations may reduce pilot variability (e.g., all
captains qualified for the same airplane with a similar background).
Counterbalancing across experimental groups may control variability that
can not be controlled by the above measures, so that, e.g., the
background of the pilots across the different experimental groups
averages out. Any residual variability must be washed out by increasing
the number of pilots tested. Statistical power analyses will indicate
what size of an effect could have been detected given the number of
pilots and their idiosyncratic variability. Power analyses are
especially important when the experimental results confirm the null
hypothesis, i.e., that the experimental manipulation had no effect.
Without determining what size of an effect could have been found if
there had been one, it is impossible to be sure that no operationally
relevant effects have been masked by the variability between pilots.^11^

# Description of Approach

Based on understanding the interactions between task and vehicle
dynamics and pilot behavior, the effects of visual cues, the fundamental
principles of motion-drive algorithms, and the importance of
experimental design, the following approach is proposed to review past
studies regarding the effect of motion cues.

First, the experimental design is examined to establish the validity and
reliability of the results. Then, the nature of the tasks performed by
the pilots participating in the investigation is determined. This
provides information on the control strategies pilots need to adopt to
complete the task, i.e., to achieve an open-loop pilot-vehicle response
of Y~Pi~Y~V~ =1 for pursuit tasks, or of *Y~Pe~Y~V~* = ω~c~ e^-τs^/s
(Eq. (3)) for compensatory tasks. The estimated pilot functions for
different vehicle dynamics are listed in Table 1 for pursuit tasks and
Table 2 for compensatory tasks. Next, the quality of the simulation cues
used in the investigation is considered. For visual cues, transport
delay and FOV are two key cueing-quality parameters. Others are display
resolution, brightness, and scene content. The quality of the motion
cues depends mainly on the dynamics of the motion platform, the type of
motion drive algorithms, and the duration of the motion transport delays
and motion/visual cue lags.

To determine how the different types of simulator cues affected
pilot-vehicle performance and/or behavior in past studies, an attempt is
made to categorize the type of cues based on pilots\' primary function.
A cue that is assumed to directly help pilots conform to the estimated
primary pilot function is called a principal cue. For example, if
pilot's primary function is a rate system, the principal cue is angular
rate or velocity. Supplemental cues are cues pilots may use to derive or
supplement the principal cue. Based on their role in supporting the
flying task, visual cues and motion cues are categorized according to
the estimated pilot functions shown in Table 3. In the reviews of past
investigations, we will first examine the effect of the principal cues,
and then the effect of the supplementary cues.

Table 3. Available simulation cues according to pilot primary function
in a task

+------------+------------+------------+----------+-------------------+
|            | *          |            | **Supp   |                   |
|            | *Principal |            | lemental |                   |
|            | Cues**     |            | Cues**   |                   |
+------------+------------+------------+----------+-------------------+
| **Pilot    | **Visual** | **Mo       | **       | **Motion\*\***    |
| Primary**  |            | tion\*\*** | Visual** |                   |
|            |            |            |          |                   |
| **         |            |            |          |                   |
| Function** |            |            |          |                   |
+------------+------------+------------+----------+-------------------+
| Gain       | Attitude   | None       | Rate or  | Rate or velocity  |
|            | or         |            | velocity |                   |
|            | position   |            |          | Acceleration      |
+------------+------------+------------+----------+-------------------+
| Rate       | Rate or    | Rate or    | Acce     | Acceleration      |
|            | velocity   | velocity   | leration |                   |
|            |            |            | \*       |                   |
+------------+------------+------------+----------+-------------------+
| Lead       | Rate or    | Rate or    | Accele   | Acceleration      |
|            | velocity   | velocity   | ration\* |                   |
+------------+------------+------------+----------+-------------------+
| Ac         | Acce       | Ac         | None     | Rate of change of |
| celeration | leration\* | celeration |          | acceleration      |
+------------+------------+------------+----------+-------------------+

\* Limited by spatial frequency

\*\* Limited by motion platform characteristics and travels

# Review of Past Studies

## Review Study #1: Schroeder's yaw experiment^12^

### Validity and Reliability

The study provides valid answers to the question of what are the effects
of different types and levels of rotational and translational motion on
pilot-vehicle performance, pilot workload, and pilots\' subjective
motion-fidelity and handling-quality perception in a helicopter
simulator, by collecting data on each of these dependent variables. The
paper does not explicitly state whether precautions were taken to
exclude pilot bias. Each pilot served as his or her own control
experiencing all simulator motion configurations. This kept the
between-group variability low and indeed, a number of significant
effects were found performing a 2x2 (translational vs. rotational
motion, each present or absent) repeated measures analysis of variance
despite the small number of pilots (6 for the first and 5 for the second
task). No power analyses are presented, so the effect size that could
have been found in the cases where none was found remains unknown. The
order of presentation of the different configurations, which may affect
the results due to pilot adaptation to simulator characteristics in a
within-subject experiment, was randomized across pilots to control for
adaptation effects. Pilot stimulation by the simulator is carefully
described and was presumably stable during the experiment. In general,
it is expected that these results are reliable and could be replicated.

### Effect of Motion Cues

1)  Task: A 15° yaw offset and a 180° hover turn of a rotorcraft. Both
    tasks are compensatory since the offset error is clearly presented
    by OTW visual cues (Fig. 3).

2)  Vehicle dynamics: The rotorcraft's yaw response is a first-order
    rate-command system, as shown in Eq. (4), which means that pilots\'
    yaw command will result in a lagged yaw-rate response. According to
    Table 2, this is a rate system with a time constant. Therefore, the
    estimated pilot transfer function is of Κ~P~(s+a)e^-τs^ type and the
    primary function of the pilot depends on the task frequency. There
    is a sway and surge coupling due to yaw acceleration and yaw rate,
    as given in Eqs. (5) and (6), respectively.

> $\overset{}{\psi} = \frac{0\text{.}\text{494}\delta_{r}}{s + 0\text{.}\text{27}}$
> (4)
>
> $A_{\text{Yp}} = 4\text{.}5\overset{\cdot}{\psi}$ (5)
>
> $A_{\text{Xp}} = - 4\text{.}5{\overset{}{\psi}}^{2}$ (6)

3)  Visual cues: The experiment had ±40° FOV with a clearly marked
    target pole and background to provide heading error, and turn rate
    information as shown in Fig. 3. Visual cueing had a transport delay
    of 60 msec.

4)  Motion cues: Both full and no motion cues were applied in the yaw
    and translational degrees of freedom (DOF). Equivalent time delays
    for each motion DOF due to the motion system were 130 msec for yaw
    and sway, and 170 msec for surge.

5)  Conclusions from the study: The study found full yaw motion cues (no
    attenuation) did not have an effect to pilot-vehicle performance
    (except for a marginal improvement in one of many measures). The
    results show that lateral motion cues significantly reduced yaw
    overshoot and pedal workload (root mean square pedal rate).
    Subjective motion fidelity ratings were significantly improved when
    translational motion cues were presented.

6)  Lessons learned: Table 2 shows that the primary function of the
    pilot depends on the task frequency. The time histories for both
    tasks indicate that, for heading stabilization, pilots had a
    pedal-input frequency of around 0.4 Hz (2.5 rad/sec) or higher. This
    is much higher than the first-order yaw-rate system, which has a
    bandwidth of 0.27 rad/sec, and makes the vehicle dynamics more of an
    acceleration system than a rate system with a lag. Therefore, the
    pilot's primary function would be closer to providing rate than
    lead. Table 3 indicates that the principal simulation cue for
    providing rate is turn rate. The OTW scene would provide heading and
    turn-rate information, which would be useful for pilots to close the
    outer heading loop with yaw-rate feedback to augment damping. Motion
    cues from the yaw DOF would also provide the turn-rate information
    to the pilot. In addition, since pilots were sitting 4.5 ft in front
    of the rotational center of the simulated rotorcraft, pilots were
    also subject to lateral acceleration cues. The study results
    indicate that yaw rate from platform motion did not help to improve
    pilot-vehicle performance. Instead, when the lateral-acceleration
    motion cues were added, heading stabilization showed an improved
    damping response. This suggests that lateral-acceleration cues are
    more useful for the task. In order to achieve the desired damping
    for heading stabilization, the pilot combines these cues with the
    turn rate cues from the OTW scene to obtain appropriate yaw-rate
    feedback.![](media/image3.wmf){width="2.4993055555555554in"
    height="1.648611111111111in"}

## Review Study #2: Schroeder's vertical experiment^12^

### Validity and Reliability

The study provides valid data on the effect of varying motion-filter
gain and natural frequency on the performance and subjective
motion-fidelity perceptions of three experienced pilots flying a
helicopter simulation. Each pilot experienced all 10 configurations at
least once, in random order to exclude adaptation and learning effects
(personal communication from author, August 2004). Due to time
constraints, only some of the configurations were repeated, which
introduced the possibility of experimenter bias. Pilot bias, however,
was controlled for by preventing pilots from knowing which configuration
they were flying. No statistical or power analyses were performed on the
data, so the possibility that some of the results occurred by chance or
that the variability within and between pilots has masked some effects
cannot be excluded. The author's criterion for accepting a result as
reliable was congruence between objective and subjective data.

### Effect of Motion Cues

1)  Task: A 10 ft bob-up maneuver of a rotorcraft followed by
    stabilization in front of a hover target. The task started out as a
    precognitive task, followed by a compensatory stabilization.

```{=html}
<!-- -->
```
7)  Vehicle dynamics: The vertical dynamics off the rotorcraft is a
    first-order rate command system (Eq. (7)). Therefore, from Table 2,
    the pilot transfer function for the compensatory part of task is of
    Κ~P~(s+a)e^-τs^ type.

$\overset{}{h} = \frac{9\delta_{c}}{s + 0\text{.}3}$ (7)
![](media/image4.wmf){width="2.7895833333333333in"
height="1.945138888888889in"}

8)  Visual cues: The cockpit FOV in the elevation was 10° up and 15°
    down, as shown in Fig. 4. A chin window at the lower right extended
    the vertical FOV to --48°. At the initial hover position, the hover
    target, which was 10 ft above, was in sight. The OTW view contained
    a hover target for the stabilization task with limited background
    scene content. The visual cueing transport delay was 60 msec.

9)  Motion cues: The vertical motion-cueing fidelity investigated ranged
    from full-motion to no-motion with different levels of motion
    attenuation (Fig. 5).^12^ This was achieved by varying the motion
    command gains and washout filter frequencies. The vertical motion
    equivalent time delay was 140
    msec.![](media/image5.wmf){width="2.698611111111111in"
    height="2.21875in"}

10) Conclusions from the study: First, the study found that vertical
    motion cues appeared to affect pilot-vehicle performance. Second,
    the study indicates that whether this effect results in improvement
    or deterioration of pilot performance and behavior is a function of
    the fidelity or quality of the motion cues.

11) Lessons learned: Table 2 indicates that pilots\' primary function
    depends on the task frequency. The collective time history of the
    altitude stabilization part of the task after the capture of the
    desired hover-position showed significant frequency content up to
    0.4 Hz or 2.5 rad/sec. This is much higher than the vertical-rate
    command-system bandwidth of the vehicle, which is 0.3 rad/sec.
    According to Table 2, the pilot's primary function would therefore
    be to provide rate. According to Table 3, the principal simulation
    cue for the task would then be vertical velocity. The OTW would have
    provided the vertical-velocity cues, and the motion system would
    have provided the acceleration cues. When motion was absent in the
    study, the altitude response exhibited an under-damped
    characteristic. As motion cueing fidelity increased, an improved
    altitude-damping response was observed, although the motion
    platform's equivalent time delay lagged the visual transport delay
    by 80 msec (60 msec vs. 140 msec). This indicates that pilots were
    obtaining the vertical-velocity feedback by integrating cues
    perceived via vestibular or proprioceptive mechanisms to improve
    damping for the attitude stabilization.^13^

## Review Study #3: Schroeder, Chung and Laforce\'s sidestep experiment^7^

### Validity and Reliability

The study provides valid data on the effects of false coordinated
roll-lateral motion cues on pilot-helicopter performance and pilot
control inputs (workload). Subjective motion-fidelity ratings and
handling-quality ratings were also collected. Each of the three pilots
experienced all configurations in random order, which reduced
within-group variability and adaptation effects. No statistics or power
analyses are provided.

### Effect of Motion Cues

1)  Task: A 20-ft lateral offset maneuver of a rotorcraft. The
    stabilization at the station-keeping point is a compensatory task.

```{=html}
<!-- -->
```
12) Vehicle dynamics: Roll and lateral vehicle dynamics were a combined
    fourth-order single-loop multi-DOF system as shown in Fig. 6 and
    described by Eq. (8), where L~P~=-4.5 rad/sec.

> $\frac{y}{\delta_{\text{Lat}}} = \frac{K_{\text{PV}}}{s^{3}\left( s + L_{P} \right)}$
> (8)![](media/image6.wmf){width="2.6506944444444445in"
> height="2.3229166666666665in"}

13) Visual cues: The simulator cockpit had a horizontal FOV of ±78° and
    vertical FOV of 10° up and 15° down, with clearly marked ground
    reference (black and white stripes on the ground) during
    translation. Visual-cue delay was 60 msec.

14) Motion cues: Motion commands were scaled from 0 (fixed-base, no
    motion) to 1.0 (full motion, one-to-one scale) for both roll and
    lateral motion axes to test roll-motion fidelity and
    lateral-coordination requirements as shown in Fig. 7. No washout
    filter was used.

15) Conclusions from the study: This study found roll and lateral
    motion-gain variation to be important for pilots' subjective
    perception of handling qualities and simulation fidelity, but not
    for their positioning performance. Lateral cues appeared important
    for this latter task.

16) Lessons learned: Following the crossover model, the pilot transfer
    function for the given task is of the type Y~Pe~=Κ~P~s^2^(s+a)
    e^-τs^. The pilot's primary function is acceleration with a lead.
    From Table 3, the information from visual cues would be limited and
    the principal motion cues would be accelerations. Indeed, the time
    history data suggest that pilots used the motion cues to generate
    lead or to integrate them to obtain lateral velocity information for
    improved lateral hover position control.

## Review #4: V1 cut takeoff from Go et al*.*^10^

### Validity and Reliability

The data presented are part of a study examining the effect of hexapod
motion on recurrent airline-pilot evaluation and training, using a
quasi-transfer paradigm. The training phase of the study (where the
pilots were told which maneuver they would experience and were given the
chance to repeat it three times) is singled out to demonstrate the
application of the proposed approach. The data provide a valid answer to
the question of whether motion affects pilot-vehicle performance and
pilot behavior in the simulator. Both objective and subjective data were
collected on these dependent variables. For the no-motion configuration,
the simulator was initialized as for motion and then the motion washed
out without knowledge of the pilots to prevent pilot bias. The equipment
was carefully calibrated. Pilots experienced only one of the two motion
conditions to prevent order and learning effects. They were
counterbalanced across groups based on their experience. Power analyses
showed that even in the cases where no effect of motion was found, the
resolution of the experiment was such that any operationally relevant
effect could have been found.^10^ This required that 40 airline captains
and first officers qualified for the simulated airplane participated in
the experiment.

### Effect of Motion Cues 

1)  Task: Stabilize airplane (B747-400) heading after engine failure at
    V~1~. This is a compensatory task.

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17) Vehicle dynamics: Directional motion was dominant for a few seconds
    after the engine failure. The yaw dynamics during this time frame
    can be approximated by a first-order rate-command system.

18) Visual cues: The visual system of the simulator had 180° horizontal
    and 40° vertical FOV with high brightness and high resolution.
    Visual cueing transport delay of the simulator was about 130 msec.

19) Motion cues: The motion system consisted of a 6-DOF synergistic
    hexapod platform with a 48 inch stroke and a 9 Hz bandwidth. Yaw
    motion was traded off for improved lateral acceleration cues based
    on Sinacori-type criteria.^12,14^ Cockpit side force was simulated
    by combining tilt and a high-pass washout filter of medium fidelity.
    Transport delay of the motion cues was about 90 msec.

20) Conclusions from the study: The results showed that motion cues
    affect pedal-reaction time. No effect on performance due to this
    reaction-time difference was found, however, probably due to the
    relatively sluggish dynamics of the airplane.

21) Lessons learned: Because the airplane's dynamic-bandwidth was
    relatively low, the heading control system can be considered an
    acceleration system. Therefore, according to Table 2, the pilot's
    primary function is to provide rate. According to Table 3, the
    motion cues would be important to provide the principal cue for the
    task, i.e., yaw rate, especially with minimal runway visual range.
    This principal cue, however, was not available due to the
    motion-system adjustment tradeoff mentioned above. Because the pilot
    position in the airplane is far in front of the c.g., the yaw motion
    induced lateral-acceleration at the pilot station. Pilots apparently
    used these lateral acceleration cues to reduce their reaction time
    to the engine failure.

# Discussion of Approach

The approach uses the open-loop pilot-vehicle characteristics to clarify
complex interactions between pilots' control function and simulation
cues based on type of task. Based on this approach, the concept of
principal cues is constructed as a starting point to determine the cues
pilots are using for a given task. In the limited number of reviews
conducted, this approach provides physics-based insights into the
relationship between the results of the study and how pilots use visual
and motion cues. Contrary to expectation, the approach reveals that
there is no linear relationship between principal cues and pilot-vehicle
behavior. This appears to result from the fact that pilots use not only
principal cues, which are defined as direct cues that do not require
extensive processing, but also combine and process (e.g., integrate or
differentiate) supplemental cues to extract the information necessary to
successfully complete their tasks. This finding will be used to refine
the procedure further.

It should be noted that this approach is not an attempt to solve how
motion cues affect pilot-vehicle behavior and performance issues that
past studies tried to address. Rather, it offers a systematic process to
review past studies to extract additional lessons and guidance for
future experiments. No attempt was made to address aural and
control-inceptor cues, which may also affect pilot-vehicle behavior.
This will be left to future elaborations of the approach.

The approach requires that a study documents experiment design, vehicle
dynamics and visual and motion cueing characteristics sufficiently to
allow this kind of in-depth review. Even in the absence of this
documentation, however, the approach may help develop a better
understanding of the interaction between pilot-vehicle performance and
behavior and simulation cues.

# Summary of Lessons Learned

The lessons learned from the four studies reviewed are summarized in
Table 4.

Table 4. Summary of lessons learned from four studies reviewed by the
approach

<table>
<colgroup>
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 9%" />
<col style="width: 9%" />
<col style="width: 7%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 9%" />
<col style="width: 10%" />
<col style="width: 11%" />
</colgroup>
<thead>
<tr class="header">
<th>Author</th>
<th>Task</th>
<th>Simulated vehicle type</th>
<th>Vehicle dynamics</th>
<th>DOF</th>
<th>Estimated pilot function</th>
<th>Visual cues</th>
<th>Motion cues</th>
<th>Results</th>
<th>Remarks</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Schroeder<sup>12</sup></td>
<td>Heading stabilization</td>
<td>Rotorcraft</td>
<td><p>Yaw rate,</p>
<p>ω<sub>BW</sub>=0.27 rad/sec</p></td>
<td>Yaw and lateral</td>
<td>Κ<sub>P</sub> se<sup>-τs</sup></td>
<td><p>FOV: ±40° horizontal,</p>
<p>+10°/-15° vertical (front window); 60 msec transport delay</p></td>
<td><p>Full-motion, no washout filter;</p>
<p>130 msec delay for yaw and sway</p></td>
<td>Yaw motion has no effect. Lateral motion effect is significant.</td>
<td>Yaw motion has no effect. Pilots integrate lateral motion cues to
improve yaw rate feedback</td>
</tr>
<tr class="even">
<td>Schroeder<sup>12</sup></td>
<td>Vertical stabilization</td>
<td>Rotorcraft</td>
<td><p>Vertical speed,</p>
<p>ω<sub>BW</sub>=0.3 rad/sec</p></td>
<td>Vertical</td>
<td>Κ<sub>P</sub> se<sup>-τs</sup></td>
<td><p>FOV: ±78° horizontal, +10°/-15° vertical (front window);</p>
<p>60 msec transport delay</p></td>
<td><p>Varied washout filter gain and frequency;</p>
<p>140 msec delay for vertical</p></td>
<td>Vertical motion has effects on pilot performance and subjective
motion fidelity</td>
<td>Pilots integrate vertical acceleration to improve vertical velocity
feedback</td>
</tr>
<tr class="odd">
<td>Schroeder and Chung<sup>7</sup></td>
<td>Sidestep</td>
<td>Rotorcraft</td>
<td><p>Roll rate,</p>
<p>ω<sub>BW</sub>=4.5 rad/sec</p></td>
<td>Roll and lateral</td>
<td>Κ<sub>P</sub>s<sup>2</sup>(s+a) e<sup>-τs</sup></td>
<td><p>FOV: ±78° horizontal, +10°/-15° vertical (front window);</p>
<p>60 msec transport delay</p></td>
<td><p>Varied roll and lateral gains, no washout filter;</p>
<p>60 msec delay for roll and lateral</p></td>
<td>Both roll and lateral motion affect pilots’ perception and control
activities, but not performance</td>
<td>Better understanding for multi-axis maneuver needs to be
developed</td>
</tr>
<tr class="even">
<td>Go et al<em>.</em><sup>10</sup></td>
<td>V1 cut</td>
<td>Transport</td>
<td>Yaw rate</td>
<td>Yaw and lateral</td>
<td>Κ<sub>P</sub> se<sup>-τs</sup></td>
<td><p>FOV: 180° horizontal, 40° vertical;</p>
<p>130 msec transport delay</p></td>
<td><p>Medium fidelity for lateral, no yaw motion;</p>
<p>90 msec delay</p></td>
<td>Motion cues had an effect on pedal reaction time</td>
<td>Pilots use lateral acceleration cue for faster pedal response to
engine failure</td>
</tr>
</tbody>
</table>

# Concluding Remarks

1)  An approach based on pilot-vehicle closed-loop control theory is
    developed to aid the interpretation of past studies investigating
    motion cueing effects.

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22) The review process helps to develop a systematic understanding of
    factors that may determine the effectiveness of motion cues in a
    ground-based flight simulation, but needs further adjustment to
    include consideration of other simulation cues such as control
    inceptor cues and aural cues.

23) The process helps to define the relationship between pilot-vehicle
    behavior and simulation cues.

24) Detailed documentation of vehicle dynamics and visual and motion
    cueing characteristics are recommended for future studies
    investigating the effect of simulation cues.

Acknowledgments

The authors thank Dr. Eleana Edens from the FAA Human Factors Division,
AAR-100, and Dr. Thomas Longridge from the FAA Flight Standards Service,
AFS-230, for helpful comments and supports of this work. We also thank
Dr. Jeffery Schroeder who has volunteered his time to review the paper
and provided valuable suggestions.

References

^1^McRuer, D.T., and Krendel, E.S., "Mathematical Models of Human Pilot
Behavior," AGARD-AG-188, 1974.

^2^Parrish, R.V., Dieudonne, J.E., Bowles, R.L., and Martin, Jr., D.J.,
"Coordinated Adaptive Washout for Motion Simulators," *Journal of
Aircraft*, Vol. 12, No. 1, Jan., 1975, pp. 44-50.

^3^Grant, P.R., and Reid, L.D., "Motion Washout Filter Tuning: Rules and
Requirements," AIAA-95-3408, August 1995.

^4^McRuer, D.T. , "Pilot Modeling," AGARD-LS-157, 1988.

^5^Stapleford R. L., Peters, R. A., and Alex, F. R., "Experiments and a
Model for Pilot Dynamics with Visual and Motion Inputs," NASA CR-1325,
May 1969.

^6^Young, LR., "Visually Induced Motion In Flight Simulation,"
AGARD-CP-249, Piloted Aircraft Environment Simulation Techniques,
Brussels, Belgium, April 1978.

^7^Schroeder, J. A., Chung, W. W., and LaForce, S., "Effects of Roll and
Lateral Flight Simulation Motion Gains on a Sidestep Task," American
Helicopter Society 53rd Annual Forum, Virginia Beach, Virginia, May
1997.

^8^Mikula, J., Chung, W. W., and Tran, D., "Motion Fidelity Criteria for
Roll-Lateral Translational Tasks," AIAA 99-4329, 1999.

^9^Taylor, H. L., Lintern, G., and Koonce, J. M., "Quasi-Transfer as a
Predictor of Transfer from Simulator to Airplane," *The Journal of
General Psychology*, 120(3), 1993, pp. 257-276.

^10^Go, T. H., Bürki-Cohen, J., Chung, W. W., Schroeder, J. A.,
Saillant, G., Jacobs, S., and Longridge, T., "The Effects of Enhanced
Hexapod Motion on Airline Pilot Recurrent Training and Evaluation,"
AIAA-2003-5678, 2003.

^11^Cohen, J., *Statistical Power Analysis for the Behavioral Sciences*,
2^nd^ ed., Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1988.

^12^Schroeder, J. A.: \"Helicopter Flight Simulation Motion Platform
Requirements, \" NASA TP-1999-208766, July 1999.

^13^Hess, R. A., and Siwakosit, W., "Assessment of Flight Simulator
Fidelity Including Visual Cue Quality," *Journal of Aircraft*, Vol. 38,
No. 4, 2000, pp. 607-614.

^14^Sinacori, J. B., "The Determination of Some Requirements for a
Helicopter Flight Research Simulation Facility," NASA CR-152066, 1977.

[^1]: Aerospace Engineer. Spatial Technologies & Concept Division, 130
    Research Drive. Senior Member AIAA.

[^2]: Engineering Psychologist, Operator Performance and Safety Analysis
    Division, DTS-79, 55 Broadway. Member AIAA.

[^3]: Aerospace Research Engineer, Center for Transportation and
    Logistics, 37-219, 77 Massachusetts Avenue. Member AIAA.