Human Factors of UAVs 1

Human Factors Implications of UAVs in the National Airspace

Jason S. McCarley

Christopher D. Wickens

Institute of Aviation

Aviation Human Factors Division

University of Illinois at Urbana-Champaign
Human Factors of UAVs 2

INTRODUCTION

Unmanned aerial vehicles (UAVs) are quickly becoming a part of the national

airspace system (NAS) as they transition from primarily military and hobbyist

applications to mainstream flight applications such as security monitoring, satellite

transport, and cargo hauling. Before the full potential of UAV flight in the NAS can be

realized, however, FAA standards and regulations for UAV operations must be

established. Given the experience of the U.S. military that mishap rates for UAVs are

several times higher than for manned aircraft (Williams, 2004)—over thirty times higher,

in some cases (Department of Defense, 2001)—the importance of carefully designed 

standards and regulations is clear.

Issues related to human factors are likely to be of particular concern in

establishing guidelines for UAV flight. As noted by Gawron (1998), UAV flight presents

human factors challenges different from and in some ways greater than those of manned 

flight. These arise primarily from the fact that operator and aircraft are not co-located. As

discussed in more detail below, the separation of operator and vehicle imposes a number

of barriers to optimum human performance, including loss of sensory cues valuable for

flight control, delays in control and communications loops, and difficulty in scanning the

visual environment surrounding the vehicle. Unmanned flight also allows the possibility 

that a single operator might control multiple vehicles simultaneously, a task likely to

impose unique and heavy workload demands.

The goal of the current work was to examine the existing research literature on the

human factors of unmanned flight, and to delineate issues for future research to address.

The topics discussed below are divided into the categories Automation; Perceptual and 

Cognitive Aspects of Pilot Interface; Air Traffic Management Procedures; and Crew

Qualifications. As will be clear, however, the issues covered within the various

categories are highly interrelated. Answers to questions about crew complement, for

example, will be contingent on the nature and reliability of automation provided to

support UAV operators. Likewise, decisions about interface design will depend on the

extent to which flight control is automated, with manual flight mode demanding 

traditional stick-and-rudder controls and automated flight mode allowing for point-andclick menu-based control or other forms of non-traditional interface.

It is also important to note that unmanned aircraft will likely serve a range of

purposes in civilian airspace, and that the demands placed on human operators will vary 

with characteristics of the flight mission. Proposed uses for UAVs include agricultural,

geological, and meteorological data collection; border surveillance; long distance

transport; search and rescue; disaster monitoring; traffic monitoring; and

telecommunications relay. Furthermore, military UAVs will increasingly be required to

transition through civilian airspace en route to their missions. In some of these cases, the

vehicle is likely to operate solely within line-of-sight communications range and only 

over relatively short periods of time (i.e., on the time scale of several hours or less). In 

other cases, the vehicle will operate at distances demanding over-the-horizon 

communications, and will potentially remain airborne for many days on end. These

mission characteristics will modulate concerns about communications delays between 

ground control station and vehicle, and about the need for transfer of vehicle control

between crews. For some applications, additionally, operators will likely be required to
Human Factors of UAVs 3

make frequent control inputs, adjusting flight parameters or selecting new waypoints

“online” in response to changing task demands or conditions. For other applications,

flight path will be predetermined and less susceptible to modification, reducing the

immediacy and frequency with which operators are required to intervene in flight control

and allowing for a heavier reliance on automated vehicle guidance.

TECHNICAL APPROACH

Our technical approach involved three parallel efforts. (1) We acquired a large body of

literature, both in published sources and in technical reports, that addressed any aspects

of human factors in UAVs. This literature is documented in an annotated bibliography in 

Appendix A. (2) We identified laboratories where UAV human factors work is in

progress. These laboratories, and points of contacts, are listed in Appendix C. (3) We

became acquainted with UAV human factors issues in civilian airspace by familiarizing

ourselves with Access 5 documents. (4) We applied our own subject-matter expertise of

both aviation human factors in general, and UAV operations in particular, to identify 18 

key human factors research topics, that we believed were relatively unique to UAV

operations. This uniqueness constraint is critical. There are for example numerous human 

factors issues that should be applied equally to manned as well as to unmanned aircraft,

relating to topics such as display legibility, CRM and communications, checklist design,

etc. We did not include these in our effort, but note their enduring importance for UAV

certification. Research topics are described in the text below, and in Appendix B are

cross-indexed with relevant sources from the research literature described in Appendix A.

Having identified issues, and examined written documents that described human 

factors research, our final product was to map research needs against existing research 

documents, where such documents contained empirically valid findings. This material,

contained in table 1 of the report below, provides an identification of the key research 

areas that we believe should be funded, in order to proceed on the path toward safe

certification of UAVs in civilian airspace. We have not explicitly prioritized these areas

in terms of their importance.

AUTOMATION ISSUES

1. To what extent should en route flight control be automated?

Current UAV systems vary in the degree to which en route flight control is

automated. In some cases the aircraft is guided manually using stick and rudder controls,

with the operator receiving visual imagery from a forward looking camera mounted on

the vehicle. In other cases control is partially automated, such that the operator selects the

desired parameters or behaviors through a computer menu or rotary dial interface in the

ground control station. In other cases still control is fully automated, such that an

autopilot maintains flight control using preprogrammed fly-to coordinates. At least one

system (Pioneer), finally, allows the operator to switch between full manual, hybrid, and 

full automation control modes.

These various modes of flight control each present benefits and drawbacks

(Mouloua, et al, 2003). Full manual control would seem to impose the highest and most

continuous level of cognitive workload on UAV operators. Moreover, manual control

will be degraded by communication delays between UAV and GCS (see #8, #13).

Conversely, fully automated control can prevent an operator from rapidly intervening 
Human Factors of UAVs 4

when necessary, (e.g., upon loss of communications) and by leaving the operator largely

“out of the loop” (Wickens & Holland, 2000), can produce degraded situation awareness

(e.g., noticing a change of handling qualities due to icing). Flight planning can also be

excessively time consuming for fully automated systems, sometimes requiring many 

weeks (Williams, 2004).

For reasons like those described above, Mouloua, et al (2003) recommended 

hybrid manual/automated control systems for military UAVs. A blanket

recommendation, however, may not be appropriate for UAV flight in civilian airspace.

Rather, the optimal flight control system seems likely to vary with the characteristics of

the flight operation, either within or across flights. UAV operations that entail primarily

long-endurance station-keeping (ACCESS 5, 2003), for example, are not likely to impose

especially high demands on operator situation awareness. Fully automated control might

therefore be more appropriate for such operations that either hybrid or manual

automation. The optimal level of automation may also depend on the number of UAVs

that a single operator is required to control, the communication delays between operator

and UAV, and the quality of visual imagery and other sensory information provided to 

the operator from the UAV.

A number of questions related to the method of UAV flight control thus remain to

be addressed. Research is recommended to:

• Determine the circumstances under which various modes of UAV flight control—

fully automated, partially automated, manual—are appropriate.

• Determine whether or not the level of automated flight control should be

reconfigurable, such that the operator can alternate between levels of control

when he/she deems appropriate.

• Determine whether the reconfiguration of flight control should itself be adaptively 

automated, such that the UAV system adjusts the level of automated flight control

to match the current circumstances (e.g., the current communications delay 

between UAV and GCS).

• Determine how and when the UAV operator will be allowed to override the

automated flight control system.

The output of this work would be a set of rules advising what level of automation should

be available/required, during what phases of flight and types of operations.

2. What are the consequences of degraded reliability of automated UAV functions

for performance of the automated task and of concurrent tasks?

As the discussion in #1 above makes clear, UAV operations are likely to be

highly automated. It is widely acknowledged, however, that often the effect of

automation is not to reduce the human operator’s task demands but rather to change

them, imposing new forms of cognitive workload and modifying the operator’s

performance strategies (Parasuraman, 2000). Such changes, and occasional increases in 

cognitive workload, often result in circumstances when automation is imperfect. This

imperfection does not refer to issues such as software reliability (e.g., “10 –5

requirements”), but rather, to circumstances where correctly functioning automation is

incapable of perfectly carrying out the functions asked of it. Examples include on-board 

conditions (e.g., icing) for which stability control cannot fully compensate, diagnostic

systems based on imperfect cues, or conflict detection/avoidance algorithms based upon 
Human Factors of UAVs 5

future trajectory estimates in a probabilistic environment (Xu, et al, 2002; Kuchar, 2001).

Past work has indicated that imperfect automation at a reliability level greater than 

around 0.80 can continue to support performance on the automated task as well as

concurrent tasks (Dixon and Wickens, 2004; Wickens & Dixon, 2005), although this

“threshold” estimate remains far from an absolute value, and other factors, such as the

nature and priority of the automated task, appear to modulate pilot tolerance for

imperfection. To allow the optimal design of automated support systems for UAV

operators, research is thus recommended to:

• Determine the minimum acceptable reliability levels for automated functions that

relatively unique to UAV operations.

• Anticipate potential forms of system failure, and delineate their likely

consequences.

• Estimate the means and standard deviations of operators’ response times to 

various failures.

Techniques such as Failure Modes Effects Criticality Analysis can be used in these

endeavors.

3. How will see and avoid requirements be addressed in UAV flight? Can automated

detect, see, and avoid (DSA) technology allow a UAV operator to maintain

acceptable levels of separation? What are the consequences of imperfectly reliable

DSA automation on conflict detection and on performance of concurrent tasks?

The ability to maintain adequate separation between aircraft is a prerequisite for

the safe integration of unmanned vehicles into the NAS. While safe separation from other

aircraft can generally be assured through standard ATC operations in operations under

IFR and IMC (but see issue #13 below), there will be times in which UAVs may be

flying under VFR (or a corresponding designation) in which detect, see and avoid (DSA)

capabilities are essential. In such circumstances, separation may often be maintained

through emerging CNS (communications, navigation, surveillance) technology supported 

by GPS navigation and ADS-B communications. However, these conditions do not

accommodate unequipped (non-cooperating) air vehicles that are unable to accurately 

transmit (or transmit at all) their position and trajectory through the 3D airspace, and 

which may be uncooperative or non-responsive in negotiating conflict avoidance

maneuvers. It is for this reason that automated DSA functions are required. The need for

such functions raise two critical human factors concerns.

First, operators will be asked to interact with error prone systems. It is likely that

automatic target recognition capabilities will be fallible, particularly if they are asked to 

generate early alerts (i.e., at sufficient distance that avoidance maneuvers are possible).

As a consequence, this form of automation will be imperfect (see # 2 above; Thomas,

Wickens, & Rantanen, 2003), leading to either misses (late alerts) or false alerts. Given

the high costs of misses, and low base-rate of events (Parasuraman, Hancock, and 

Obofinbaba, 1996), the false alarm rate will be potentially quite high (Krois, 1999). The

effects of such automation errors will have to be considered in designing DSA systems.

Second, operators will be required to interact with the imperfectly reliable DSA system

while also maintaining responsibility for airframe and payload control. These concurrent

responsibilities will determine the degree to which the operator can be expected to 
Human Factors of UAVs 6

oversee the DSA, monitoring the raw data of the UAV sensor images of the 3D airspace

upon which the DSA algorithms are based. In light of these concerns, research is

recommended to:

• Determine how operators will respond to alert imperfections in DSA.

• Delineate the conflict geometries and visibility conditions that are likely to

degrade the reliability of DSA automation.

• Establish procedures by which the output of human perception and automated

target analysis can be combined to maximize the sensitivity of the two 

component (pilot and algorithm) system given the pilot’s concurrent

responsibility for flight control.

4. To what extent should takeoff and landing be automated?

Current UAV systems differ in their manner of takeoff and landing. Some (e.g.,

the Hunter and Pioneer) are controlled by an on-site external pilot. Others (e.g., the

Predator) are controlled by an air vehicle operator within the GCS. For others still (e.g.,

the Global Hawk) takeoff and landing are fully automated. These differences appear to be

consequential; takeoff and landing errors constitute a higher proportion of human factorsrelated accidents for the Hunter (67%) and Pioneer (78%) systems, both of which are

controlled by an external pilot, than for other systems (Williams, 2004). Research is

therefore recommended to:

• Determine what method of UAV control during takeoff and landing is appropriate

for aircraft in civilian airspace.

• Delineate the responsibilities that the human operator can and will be expected to

assume in the case of automation failure.

• Establish guidelines to for how and how will the human operator will be allowed

to override automated control systems.

PILOT INTERFACE: PERCEPTUAL AND COGNITIVE ISSUES

5. Through what form of control interface should internal and external pilots

manipulate a UAV?

As noted above, UAV systems will vary in the degree to which airframe control is

automated either en route or during takeoff and landing. For any system that is not fully

automated—including systems that allow for a human operator to intervene in vehicle

control by overriding automation—it will be necessary to provide operators with a

control interface through which to manipulate the vehicle. The form of this interface will

differ for internal pilots, those who interact with the vehicle through an interface of

sensor displays and controls inside a ground control station, and external pilots, those

who interact with the vehicle while in visual contact with it at the site of takeoff or

landing. In the case of full manual control by an internal pilot, the seemingly obvious

choice of control design is a stick and rudder interface like that used for control of

manned aircraft. In cases of partially automated flight control, or of fully automated flight

control where the pilot is provided authority to override the automation when deemed 

necessary, the optimal design of control interface is less clear. Current UAV systems vary 

in control design, with some systems allowing interaction through knobs or position 

switches and others through mouse-driven point-and-click computer menus (Williams,
Human Factors of UAVs 7

2004). Alternative designs may be possible, however, and should be explored.

Additionally, it is important to ensure that any interface be tailored according to

established human factors guidelines; data suggest that some existing UAV system

interfaces are poorly designed for human interaction.

Similarly, research is necessary to assess and improve the design of controls for

external pilots. Currently, an external pilot manipulates the UAV using joystick controls

similar to those used by radio-controlled aircraft hobbyists (Williams, 2004). These

designs are problematic, however, in that the mapping of vehicle movement to control

input varies depending on the heading of the vehicle relative to that of the EP. When the

heading of the vehicle and pilot are the same, a rightward input to the joystick control

produces rightward motion from the aircraft relative to the pilot. When the heading of the

aircraft and pilot deviate, however, this is no longer true. In the most extreme case, where

the heading of the UAV and pilot differ by 180°, a rightward input on the joystick 

produces leftward motion of the vehicle relative to the pilot. Joystick controls for external

pilots are thus not designed to conform consistently to the well-established human factors

principle of motion compatibility (Wickens & Holland, 2000; Wickens, Vincow, & Yeh,

2005). Not surprisingly, this violation appears to be contributing factor in a high number

of UAV mishaps (Williams, 2004). Quigley and colleagues (Quigley, Goodrich, & Beard,

2004) have designed and tested a variety of control interfaces to address this problem.

Further is now necessary to:

• Explore and optimize the design of control interfaces for internal and external

pilots’ control of UAVs.

• Delineate the performance benefits and drawbacks of various forms of UAV

control interface so as to determine which design should be adopted.

6. What compromises should be adopted between spatial resolution, temporal

resolution, time delay, and field-of-view (FOV) in the display of visual imagery for

flight control and/or conflict detection?

A UAV operator generally relies on imagery from onboard sensors for manual

control of vehicle and payload and for visual target detection. The quality of this visual

information, however, may be degraded due to datalink bandwidth limits and 

transmission delays. Specific degradations include poor spatial resolution, limited FOV,

low update rates, and delayed image updating (Van Erp, 2000). These conditions will

impair both vehicle control and the visual detection of air traffic. For example, low image

update rates will degrade perception of motion information that is useful for drawing 

attention to air traffic in the visual field. Low update rates and long communication 

delays, likewise, will produce discontinuous and slow visual feedback in response to 

operator control inputs, leading to instability of manual UAV control or camera image

control and encouraging operators to adopt a “go-and-wait strategy” in manual control

(Van Erp & Van Breda, 1999). Poor spatial resolution, obviously, will impair detection of

objects that occupy only a small visual angle within an image, reducing stand-off distance

in detection of potential traffic conflicts (see 4 above). A small field of view (FOV) will

not only eliminate ambient visual information useful for assessing ego-motion necessary 

at low level flight (Gibson, 1979; Wickens & Holland, 2000), but will also impose a

demand for greater amounts of camera scanning for successful traffic detection. A welldesigned system for display of sensory imagery will be required to balance the benefits
Human Factors of UAVs 8

and costs of temporal resolution, spatial resolution, and FOV. To guide the design of

visual information displays, research is recommended to:

• Determine what information is “task-critical” in manual airframe control, payload 

sensor control, and visual traffic detection (Van Breda, 2000).

• Establish the optimal compromise between spatial resolution, temporal resolution,

and FOV in the display of visual imagery.

As part of this work, it will be important to establish a catalogue of mission payload 

requirements that may compromise the quality of visual information for flight, and

establish the minimum necessary information (time delay and image quality) for manual

control. For different functions, sensitivity curves should be established to show

performance quality or function degradation as a function of spatial and temporal

resolution.

7. Can augmented reality displays or synthetic vision systems successfully 

compensate for the degraded visual imagery provided by onboard sensors?

As noted above (#6), low temporal resolution and delayed updating of visual

imagery received from onboard sensors will degrade manual control of airframe and 

payload tasks. The judicious choice of spatial and temporal image parameters may

attenuate these effects, but is unlikely to mitigate them in full. An alternative approach to 

improving visual information display may be through the use of “augmented reality”

(Milgram & Colquhoun, 1999) or “synthetic vision” (Draper, et al, 2004), in which the

real-world imagery provided by a sensor is embedded within a display of computergenerated landmarks or objects representing the same scene. The virtue of augmented 

reality in the context of UAV flight is that the computer-generated component of a

display can be updated immediately in response to control inputs from a UAV operator,

providing rapid feedback to improve manual tracking. For example, Van Erp & Van 

Breda (1999) provided subjects in a simulated payload sensor control task camera

imagery overlaid by a computer-generated grid of perpendicular lines, oriented so as to

conform to the imaged scene. The synthetic grid shifted in real time following input from

the operator, giving visual feedback as to the direction and magnitude of camera

movement. As compared to a control condition with no virtual grid, augmented displays

significantly improved target tracking at low camera update rates (i.e, long sensory 

delays). A study by Veltman and Oving (2002) produced similar benefits by embedding 

current and predicted camera footprints within a larger map (either 2D or 3D) of the

terrain to be scanned. A still more sophisticated form of display is a fully virtual synthetic

vision system, in which terrain information is stored in databases and rendered based on 

GPS position. An important issue here concerns the degree of realism with which

synthetic imagery should be presented, whether minimalist (e.g., the grid used by Van

Erp and Van Breda), or highly realistic, such as employed in current SVS systems

(Prinzel et al, 2004). The danger of the latter is that pilots may place undue trust in the

imagery, leading to cognitive tunneling and neglect of information not available within 

such a high imagery display (e.g., a “transponder off” aircraft; Thomas and Wickens,

2004). Augmented displays thus present a promising method of enabling good UAV

operator performance, but are not without potential costs. Research is thus recommended 

to:
Human Factors of UAVs 9

• Further develop and test predictive augmented displays to improve airframe and 

payload sensor control.

• Determine the effects of display format/fidelity on the UAV operator’s level of

trust in the automated system.

8. Can multimodal display technology be used to compensate for the dearth of

sensory information available to a UAV operator?

One of the primary consequences of the separation between aircraft and operator

is that the operator is deprived of a range of sensory cues available to the pilot of a

manned vehicle. Rather than receiving direct sensory input from the environment in

which his vehicle is embedded, the UAV pilot receives only that information provided by

onboard sensors via datalink. As noted above, this consists primarily of potentially 

degraded visual imagery covering a relatively small FOV. Sensory cues that are lost thus

include ambient visual input, kinesthetic/vestibular information, and sound, all of which 

are valuable in maintaining operator awareness of the environmental and system

conditions (e.g., turbulence, icing). As compared to the operator of a manned aircraft,

therefore, a UAV operator can be said to function in relative “sensory isolation” from the

aircraft under his control. It is critical in light of this for UAV system developers to

design displays and alarms to keep operators well-informed of system status and aware of

potential system failures.

Visual displays provide one method of presenting a UAV operator with sensor

information beyond that conveyed by imagery from a vehicle-mounted camera. Data

suggest, however, that UAV operators may not optimally modify their visual scanning 

strategies to compensate for the absence of multisensory cues (Tvaryanas, 2004).

Moreover, the task of creating an “ecological”, intuitively-interpreted visual

representation for such information is often difficult. An alternative way to compensate

UAV operators for the lack of direct sensory input from the vehicle’s environment could 

be through the use of multimodal (e.g., tactile or auditory) information displays. For

example, fly by wire controls have long been equipped with augmented force feedback 

mimicking the forces experienced on the air surfaces of manned aircraft, and roughly

capturing the changes in handling quality. Ruff, et al (2000) examined the value of haptic

displays for alerting UAV operators to the onset of turbulence. Their data revealed that

haptic alerts, conveyed via the UAV operator’s joystick, could indeed improve self-rated 

situation awareness during turbulent conditions in a simulated UAV approach and 

landing task. Interestingly, this was true despite the fact that the haptic signals were not

designed to closely simulate or mimic the veridical haptic information experienced by the

pilot of a manned vehicle. The benefits of haptic displays, however, were obtained only 

under limited circumstances (specifically, only when turbulence occurred far from the

runway), and were offset by an increase in the subjective difficulty of landing. These

results suggest some value of multi-modal displays as a method of compensating for

sensory cues typically denied to a UAV operator, but also indicate that such displays may 

not be universally valuable and may carry costs as well as benefits.

A related point is that multimodal displays may be useful not simply as a means

to compensate for the UAV operator’s impoverished sensory environment, but more

generally to reduce cognitive-perceptual workload levels. Studies by Calhoun, et al
Human Factors of UAVs 10

(2002), Sklaar and Sarter (1999), and Wickens and Dixon (2002; Dixon & Wickens,

2003; Wickens, Dixon, & Chang, 2003), for example, have found that auditory and tactile

displays can improve aspects of flight control and system monitoring.

In sum, research is necessary to:

• Further develop techniques for multimodal information display.

• Assess the value of multimodal displays in countering UAV operators’ sensory 

isolation.

• Assess the more general value of multimodal displays in distributing workload 

optimally across cognitive-perceptual channels.

9. To what extent can displays and controls be standardized across UAV systems? 

What level of standardization should be mandated? (Basic T instrument panel? 

HUD overlay?)

We anticipate a tendency for vendors to produce novel designs for the interface,

particularly, given the diversity of specialized payload missions for which UAVs may be

designed. It is essential to establish certain commonalities across all interfaces. Exactly

what these should be remains a question for research. Questions to be considered include,

but are not limited to:

• Should the “basic T” always be maintained?

• Is an inside-out attitude display necessary, given that the pilot is no longer inside

the vehicle?

• Should certain information always be visible (never hidden to be retrieved by

menu navigation)?

• Should all aspects of the payload display be kept spatially separated from the

primary flight display, or are HUD overlays advisable?

• Should certain controls (e.g., a joystick) be mandatory for certain functions, and

should others be prohibited (e.g., mouse for flight control)?

Identification of these issues recognizes that no single display layout or control

assignment is optimal for all tasks, but also recognizes that certain cases of inconsistency 

can lead to negative transfer and pilot error, as pilots transfer from one interface to

another. Similar issues have been addressed in assigning common type ratings and 

differences training in commercial manned aircraft.

10. What are the consequences for system safety of pilot judgment when the pilot no 

longer has a “shared fate” with the vehicle? Will there be subtle shifts in risk taking 

that might affect overall airspace safety?

UAV pilots will not be at risk for injury or death if their aircraft crashes, in 

contrast to the circumstances of manned aircraft pilots, who “share fate” with their

aircraft. This difference could, in theory, impose a substantial difference in risk taking 

tendencies, in such areas as the decision to carry out a flight into bad weather (Goh &

Wiegmann, 2001; Weigmann, Goh, & O’Hare, 2002). Such differences may be further

amplified by the sensory isolation described previously. Research is thus recommended 

to:

• Determine how the UAV operator’s risk perception and risk taking behavior are

affected by absence of shared fate with his/her vehicle.
Human Factors of UAVs 11

• Determine how the UAV operator’s risk perception and risk taking behavior are

affected by the absence of sensory/perceptual cues.

AIR TRAFFIC MANAGEMENT PROCEDURAL ISSUES

11. How will hand-offs between crews be accomplished during long-endurance

flights?

Long-distance and/or long-endurance UAV flight will require the frequent

transfer of control between operators, generally taking one of three forms (Kevin 

Williams, personal communication). First, control may be passed from one ground

control station to another. Second, control may be passed from one crew of operators to 

another within the same ground control station. Finally, control may be passed from one

operator to another within a crew. The transfer of control will likely constitute a critical

and high-workload phase of UAV operations. Indeed, a number of military UAV

accidents have occurred during transfer of control from one team of operators to another,

generally because the station receiving control was not properly configured (Williams,

2004). Research is necessary to establish procedures for the safe handoff of control

between UAV crews. More specifically, this work should:

• Develop and test formal procedures for handoff of UAV control between teams of

operators.

• Develop and test displays, automation, and procedures to ensure that the operators

receiving UAV control are adequately informed of system status and are alerted to

discrepancies in system configuration between control stations relinquishing and

assuming vehicle control.

12. What are the effects of variable total loop time delays on response to ATC

instructions?

Datalink delays may be expected to add as many as several seconds to the

communications loop between UAV operators and ATC. The magnitude of these delays,

however, will be variable, and may not always be predictable to human operators. Thus,

controllers may have greater difficulty in compensating for these delays than they do in 

compensating for the fixed response characteristics of a given class of aircraft. Potential

compensatory responses to communication delays are changes in the timing with which 

ATC commands are issued and acted upon, and changes in the communications flow

between ATC and UAV operators (Kiekel, Gorman, & Cooke, 2004; Rantanen McCarley 

& Xu, 2004). To anticipate and accommodate the effects of communications delays, it

will be necessary to understand and take account of these compensatory behaviors and 

their consequences for system performance. Research should thus be conducted to:

• Determine what compensatory behaviors, if any, air traffic controllers’ and UAV

operators’ adopt in response to communication delays.

• Determine the effects of communications delays on the flow of air traffic.

Computer simulation models of communications may be particularly effective tools here,

so long as such models are based on empirically validated estimates of human response

time, variability, and reliability (probability of communications error).
Human Factors of UAVs 12

13. What form of predictable autonomous behavior should a UAV adopt following a

loss of ground-to-air communications? How should the UAV operator be alerted to 

a loss of ground-to-air communications?

One particularly disruptive scenario of UAV automation failure is a total severing of

the GC-UAV control loop. It is important that the vehicle behave predictably under such 

circumstances. This is a human factors issue because such default rules are of critical

importance to the ATC/ATM who must manage traffic based on the knowledge of these

rules (Shively, 2004). It is also important, clearly, that the UAV operator become aware

of the communications loss as rapidly as possible. Research is therefore necessary to:

• Determine what behavior UAV be programmed to adopt by default in case of a

total communications loss with ground control station (Continue to fly on a

straight path? Descend? Fly toward the nearest equipped airfield?).

• Develop displays, automation, and/or procedures by which the UAV operator can

be made aware of a communications loss, and be provided estimates of its

potential causes and consequences.

CREW QUALIFICATIONS

14. How many members will each crew comprise, and what will be each

crewmember’s responsibilities? Can an operator supervise multiple UAVs

simultaneously while maintaining an acceptable level of performance?

Military UAV crews for reconnaissance missions typically include both an air

vehicle operator and a mission payload operator (Draper, et al, 2000; Mouloua et al,

2003). Such crew structure is reasonable in light of findings that the assignment of

airframe and payload control to the same operator can substantially degrade performance

(Van Breda, 1995, cited in Van Erp & Van Breda, 1999). For UAV flight in civilian 

airspace, however, the size of the crew complement necessary for each vehicle is likely to

be contingent on the nature and goals of the flight task (e.g., surveillance vs. longdistance transport vs. station keeping for telecommunications). Although some research 

has demonstrated possibility of single pilot-multiple UAV (1-to-many) control

(Cummings and Guerlain, 2004; Galster, et al, 2001; Wickens, et al, 2003), these

successes pre-suppose three circumstances: (1) closely coordinated and correlated

activities among the multiple UAVs (Cummings & Guerlain, 2004), (2) operation in a

disturbance free (closed) environment, such as very high altitudes, (3) high levels of

reliable automation (Dixon and Wickens, 2004). When any of these three characteristics

are not in force, and, in particular, when one UAV enters a failure mode, the ability of the

pilot to monitor others in a 1-to-many configuration is severely compromised.

Furthermore, even in a 1-to-1 configuration, performance of concurrent tasks is

dramatically degraded when heavy demands are imposed on the single operator by 

complex payload operations (e.g., manipulating camera imagery) (Dixon and Wickens,

2004). In light of this, research is necessary to:

• Delineate circumstances under which multiple responsibilities (e.g., flight control,

conflict detection, payload control) be safely assigned to a single UAV operator,

and circumstances under which such responsibilities should be distributed across

two or more crew members.

And, by extension:
Human Factors of UAVs 13

• Delineate circumstances under which a single operator can safe hold

responsibility for multiple UAVs simultaneously.

It is crucial that such research consider circumstances under which automation is

imperfect (#2), and that it address the potential cost of communications and teamwork 

between multiple operators (Kiekel, et al, 2004).

15. What are the core knowledge, skills, and abilities (KSAs) that should be

required for UAV pilot certification? What KSAs should be required for

certification to fly particular UAV systems or classes of systems?

Research is necessary to:

• Determine the general KSAs that will be required of all UAV operators.

• Determine KSAs required for certification to operate specified classes or systems

of UAV.

16. How should UAV operators be trained? What constitutes an appropriate

regimen of ground school, simulator, and flight experience for UAV flight

certification?

Safe flight of unmanned vehicles in the national airspace will demand effective

procedures for UAV pilot training. Ryder, et al (2001) note that because the task demands

of operating a UAV from a ground control console are similar during simulated and real

flight, simulator experience is likely to constitute a greater portion of training for pilots of

unmanned vehicles than for pilots of manned aircraft. As noted below (#17), furthermore,

experience piloting manned aircraft appears to produce positive but imperfect positive

transfer to UAV flight (Schreiber, et al, 2002). Research is needed to:

• Optimize simulation systems for UAV pilot training and test their adequacy

• Establish requirements for flight training outside the simulator.

• Determine to what extent manned pilot experience should offset training

requirements for UAV certification.

17. Should experience piloting a manned aircraft be prerequisite for UAV pilot

certification?

Past research has come to conflicting conclusions as to whether UAV operators

will benefit from experience piloting a manned aircraft. Schreiber, et al (2002) examined 

the effects of prior flight experience on novice operators’ skill acquisition and transfer in 

a Predator UAV simulation. In general, flight experience reduced the number of training

trials required for operators to reach a criterion level of performance on a set of basic

maneuvering and landing tasks, and improved operator performance on a subsequent

reconnaissance task. Other findings, however, have suggested that UAV operators need 

not be rated aviators. Using the Army’s Job Assessment Software System (JASS),

Barnes, et al (2000) elicited Hunter UAV operators ratings of the relative importance of

various cognitive skills in UAV air vehicle operators. Ratings indicated that outside of

communication skills, raters did not consider flight-related skills of great importance to 

UAV operations, leading the authors to conclude that selection of rated aviators as air

vehicle operators would be of little value.
Human Factors of UAVs 14

The apparent discrepancy in the conclusions reached by Schreiber, et al (2002)

and Barnes, et al (2000) may be due, at least in part, to differences in the operation of the

UAV systems under consideration; while the Predator is piloted manually via a stick and 

rudder interface similar to that of a manned aircraft, the Hunter is guided by automation

that allows the operator to select flight parameters using knobs on the GCS console. The

value of prior flight experience to a UAV operator, that is, may depend in part on 

similarity between the manned and unmanned systems. Research is necessary to:

• Determine whether and how much experience piloting a manned system should 

be required for UAV pilot certification.

• Determine whether prerequisite levels of flight experience, if any, should vary 

across UAV platforms.

18. What medical qualifications should a UAV operator be required to meet?

Although issues of high altitude physiology and medication induced vestibular

disruption are not relevant to UAV pilots, some forms medical qualifications are likely to

remain necessary. Research is necessary to:

• Determine whether medical standards for UAV operators should be in any ways

less or more strict than for pilots of manned aircraft.

• Establish special duty limits for long duration missions.
Human Factors of UAVs 15

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Human Factors of UAVs 19

Appendix A

Bibliography

1) Ball, J.T., Gluck, K.A., Krusmark, M.A., & Rodgers, S.M. (2001).

Comparing three variants of a computational process model of basic aircraft

maneuvering. Proceedings of the 12th Conference on Behavior Representation in

Modeling and Simulation.

The paper uses an ACT-R model to examine expert/novice differences and effects of

control strategy on Predator UAV flight. Three models were developed. Model P

(performance only) lacked the knowledge of control instrument settings that is

characteristic of expert pilots, and therefore could only rely on performance indicators in

maneuvering the aircraft. Model CP (Control + Performance) had knowledge of control

and performance settings needed to achieve aircraft behavior, and therefore could rely on

a control and performance strategy. However, the model did not remain focused on 

control indicator after making adjustment to it, but continued with normal crosscheck and 

checked to see if manipulation had its intended when attention eventually returned to the

indicator. Model CFP (Control Focus & Performance) was similar to model CP, except

that it remained focus on control instrument until it was properly set. This was in addition

to normal crosscheck.

To examine expert/novice differences, the authors compared Model P to Model CP. To

examine strategic effects, they compared Model CP to Model CFP.

Results

Performance was better for Model P than for Model CP on 6 of 7 maneuvers. Model P

was better on the most complex (three-axis) maneuver, though its not clear why.

Performance was better for Model CFP than for Model CP on 5 of 7 maneuvers.

Performance on other two maneuvers was similar. Overall, Model CFP performed the

most like human subject matter experts.

2) Barnes, M.J., Knapp, B.G., Tillman, B.W., Walters, B.A., Velicki, D. (2000).

Crew systems analysis of unmanned aerial vehicle (UAV) future job and tasking 

environments (Technical Report ARL-TR-2081). Aberdeen Proving Ground, MD:

Army Research Laboratory.

Experiment 1 

Assessed the importance of using rated aviators for air vehicle operator (AVO) and

external pilot (EP) positions in the Hunter UAV. The AVO tasks are to design mission 

plans in collaboration with commander, fly the UAV after take-off, and set course to 

waypoints. The AVO must be able to read the instruments, understand flight status,

coordinate with the mission payload operator (MPO) when reaching target area, and 
Human Factors of UAVs 20

respond to emergencies and make course changes when necessary. However, the AVO

does not fly the vehicle using stick-and-rudder controls, in the manner of a typical pilot.

The EP is responsible for take-off and landing, using a controller similar to that for model

airplanes. Most flight safety problems occur during the times that the EP is in control,

primarily because take-off and landing are inherently difficult.

The study used the Army's Job Assessment Software System (JASS) to determine what

cognitive skills are important for the AVO and EP positions. JASS collects ratings about

the degree to which various skills and abilities are necessary to perform a given task. The

skills/abilities rated by JASS fall into six categories: communication, speed-loaded,

reasoning, visual, auditory, and psychomotor. JASS data were supplemented with

enhanced computer-aided testing (ECAT) data from an earlier study. The ECAT data

were one- and two-handed tracking scores, which were correlated with failure rates for

EP training.

21 subjects were AVO & MPO designated; 11 gave ratings of AVO task structure, 10 

gave ratings of MPO task structure. 9 subjects were certified EPs, and gave ratings of EP

task structure. 16 fixed- or rotary-wing Army aviators also rated EP skills. ECAT data

came from a sample of 28 students in Pioneer and Hunter UAV EP training courses. Six

of these failed the course.

Results

AVO raters did not rate flight-related tasks as overly demanding on any of the six skill

sets except communication. The EP task was rated as more demanding than AVO task on 

all skill sets. EP subjects were broken into 4 groups: EP low experience, EP high 

experience, fixed-wing aviators, and rotary-wing aviators. Aviators gave slightly higher

ratings to reasoning skills than did EPs. The EP low experience group gave especially 

high ratings to vision, audition, and psychomotor skills. Experienced EPs reported using 

mostly conceptual skills during emergency situations. Inexperienced EPs reported relying

on visual & psychomotor skills.

ECAT tracking data were correlated with EP course success rates; 5 of the 6 students

who failed had tracking data near bottom of sample distribution. These findings are

consistent with the finding (noted above) that inexperienced EPs find visual and 

psychomotor skills to be particularly important.

Experiment 2

Examined the potential value of imagery and intelligence analysts as components of the

UAV. The method used was to measure overlap between JASS ratings for imagery

analysts, intelligence analysts, and UAV crew task duties. Imagery analyst skill rankings

were significantly correlated with those for 2 out of 16 UAV crew duties, intelligence

analyst skill rankings were correlated with those for 14 out 16 UAV crew duties. Results

suggest that imagery analysts would complement UAV crew skill sets.

Experiment 3
Human Factors of UAVs 21

Used a computational model of human cognition (Micro Saint) to investigate workload

throughout the course of a simulated Outrider UAV flight mission. Also considered 

remarks from subject matter experts (SMEs). Results suggest that candidate tasks for

automation included pre- and post-flight procedures & checks, verification of system

settings, and computer checks of mission plans. SMEs reported that they did not want full

automation, but preferred instead to retain decision making authority themselves. To

reduce workload, they suggested making the computer interface faster and letting the

automation provide backup check for safety problems.

3) Bell, B., & Clark, J.G. (2002). Bringing command and control of unmanned

air vehicles down to earth. Proceedings of the 21st Digital Avionics Systems

Conference (DASC), Irvine, CA.

Describes an automated system to assist in UAV search area planning. System is called 

the Automated Search Area Planning System (ASAPS), and is meant to reduce search 

area by modeling terrain and target mobility then helping operator to plan a search route

focusing on areas where target is most likely to be found.

4) Calhoun, G.L., Draper, M.H., Ruff, H.A., & Fontejon, J.V. (2002). Utility of a

tactile display for cueing faults. Proceedings of the Human Factors and Ergonomics

Society 46th Annual Meeting, 2144-2148.

Subjects performed a compensatory tracking task in conjunction with a monitoring task.

Study compared the value of visual, tactile, and combined visual/tactile alerts for

identifying which of four scales exceeded normal range in the monitoring task. In the

visual condition, subject was required to monitor the scales. In the tactile condition,

subject received pulse train alert of fault, with location and frequency of train indicating 

which scale was beyond normal range of values.

Results

Response time to faults was faster and RMS tracking error was reduced with tactile cues

compared to visual cues. Subjective ratings also strongly preferred the tactile cues.

5) Calhoun, G.L., Fontejon, J.V., Draper, M.H., Ruff, H.A., & Guilfoos, B.J.

(2004). Tactile versus aural redundant alert cues for UAV control applications.

Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting,

137-141.

Experiment 1

Examined the value of aural and tactile alerts, presented redundantly with visual cues, to 

signal warnings during a simulated UAV control task. Subjects performed a continuous
Human Factors of UAVs 22

UAV control task. While doing this, they were required to respond to occasional (3-4 per

minute) data entry tasks. Two kinds of data entry task were used:

1) Warning response task Subject determined whether warning level was caution (20-24 

per trial) or critical (3 per trial), then responded by entering an appropriate sequence of

keys.

2) Radio frequency task Subjects heard call signs, followed by a combination of color &

number. On events with call sign Eagle, subject was required to select the appropriate

color/number coordinate on the HDD using a mouse. On low auditory load trials, only the

call sign Eagle events were used. On high auditory load trials, distractors events with

different call signs were interposed.

3) Data query task Simultaneous visual/voice commands specified data for subject to

retrieve from HDD and enter via keypad.

Primary manipulation of interest was in the warning response task. In baseline condition,

caution signals were specified by yellow visual cue and redundant Type I aural cue,

critical signals were specified by red visual cue and the Type I aural cue. In +2nd aural

condition, caution signals were specified by yellow visual cue and redundant Type I aural

cue, critical signals were specified by red visual cue and the Type 2 aural cue. In +Tactile

condition, caution signals were specified by yellow visual cue and redundant Type I aural

cue, critical signals were specified by red visual cue and tactile cue.

Results

+2nd aural and +Tactile conditions produced shorter RTs than baseline condition (p < .05

and p < .10). Baseline was also subjectively rated as less salient than the other two

conditions. +2nd aural and +Tactile were not significantly different. +2nd aural and 

+Tactile improved performance in the radio frequency task under high auditory load 

conditions. Flight performance was not affected by alert condition.

Experiment 2

The second experiment was conducted to examine the interaction of cue format and

auditory load more closely. Method was similar to that the Experiment 1, except that 1)

no auditory cue was used in the baseline condition, 2) only critical cues (no caution cues)

were used, 3) high auditory load was more difficulty, 4) aural and tactile cues were

matched for salience by a pilot study, 5) a visual IFF status probe task was added.

Results

+Aural and +Tactile conditions produced shorter RTs than baseline condition, and were

rated as more salient. No differences obtained between +Aural and +Baseline conditions.

Baseline condition also produced higher subjective workload. Low auditory load in

general produced better self-rated SA and task performance and lower workload, but did

not interact with cue format, contrary to findings of Experiment 1.
Human Factors of UAVs 23

6) Cooke, N.J., & Shope, S.M. (2004). Synthetic task environments for teams:

CERTT's UAV-STE. Handbook on human factors and ergonomics methods. Taylor

Francis.

Details steps involved in creating a synthetic task environment, and illustrates the process

by describing the development of CERTT's Predator UAV STE.

7) Cummings, M.L. (2004). Human supervisory control of swarming networks.

Paper presented at the Second Annual Autonomous Intelligent Networked Systems

Conference.

Discusses issues related to supervisory control of swarming UAVs, i.e., groups of UAVs

with some level of autonomous inter-vehicle collaboration. Collaboration between UAVs

introduces another layer of automation into UAV control task. At the minimum level of

of network autonomy, there is no collaboration between UAVs. At the maximum level,

vehicles are in full collaboration and there is no need for human intervention in emergent

situations.

Increasing inter-vehicle collaboration does not necessarily increase automation level for

the system as a whole. At the lowest level of inter-vehicle collaboration, automation can 

range from SV levels 1-10. At highest levels, it can range from levels 7-10. The effects of

automation of full system and of inter-vehicle communication must be considered in

system design.

DOD recognizes the need for a standard UAV interface that provides critical SA and 

location data to support airspace integration. Swarming UAVs will be tasked to optimize

multi-objective cost functions, and central issue in maintaining SA will be to provide

visualization tools that communicate cost function info to UAV operator. It will also be

necessary to provide interactive sensitivity analysis tools to determine how human 

adjustments of variables could change overall cost function.

8) Cummings, M.L., & Guerlain, S. (2004). Developing operator capacity estimates

for supervisory control of autonomous vehicles. Manuscript under review.

An experiment assessed operators' ability to control multiple autonomous aircraft.

Subjects performed a supervisory task that required them to control and occasionally retarget multiple Tomahawk missiles. Commands and occasional queries were presented in 

an onscreen chat box. Chat box responses served as a secondary task measure of

workload. Retargeting was done with a decision matrix (looks like a spreadsheet) that

allowed subjects to view information on all retargetable missiles, including how long it

would take missiles to get to target and the time remaining for the operator to retarget the

missile. Available missiles were listed in rows, potential targets were listed in columns.

Cell at the intersection of a given row and column gave info about that missile/target
Human Factors of UAVs 24

pairing. Retargeting commands arrived at two tempos, low (one event every 4 minutes)

and high (one event every 2 minutes). Difficulty of task scenarios was easy, medium, or

hard.

Dependent variables were decision time for retargeting; Figure of Merit (FOM), a

weighted measure of overall performance; utilization, an objective workload measure

given by % busy time; and NASA-TLX ratings. Participants were 42 active and retired 

duty Navy personnel.

Results

Decision time, FOM, and utilization scores were similar with 8 and 12 missiles, but were

degraded with 16 missiles. The effect of number of missiles on decision time was larger

as the scenario became more difficult. Subjective workload scores were not affected by 

number of missiles. Results suggest that operators can manage up to 12 missiles with no 

degradation. See papers by Galster, et al (2001) and Hilburn, et al (1997) for similar

conclusions from ATC domain.

9) de Vries, S. C. (2001). Head-slaved control versus joystick control of a remote

camera (TNO-report TM-01-B008). Soesterberg, The Netherlands: TNO Human

Factors Research Institute.

Experiment compared benefits of head-slaved HMD control of UAV camera vs. joystick 

control. Camera joystick was either passive, active (force feedback), or combined with 

UAV control joystick. Dynamics of the joystick were either position or velocity control.

In some conditions, reference marks were included to aid perception of camera

orientation.

Results

Analysis of joystick manipulations indicated that best performance came from a passive

joystick providing position control without vehicle references. Performance on almost all

measures was superior with joystick control relative to head-slaved control.

10) de Vries, S.C., & Jansen, C. (2002). Situational awareness of UAV operators

onboard moving platforms. Proceedings HCI-Aero 2002.

An experiment examined spatial awareness of operators controlling a UAV from onboard

a moving helicopter. Displays presented a 2-D electronic map of terrain including the

UAV, helicopter, football stadium, and a column of tanks. In some conditions, a 3-D map 

was presented to provide self-motion info from perspective of operator inside the

helicopter. 2-D maps varied in their center (heli-centered vs. UAV centered) and 

orientation (north up vs. helicopter heading up vs. UAV heading up). The subject's task 

was to monitor displays through a 40-60 s automated flight period then answer questions
Human Factors of UAVs 25

about locations of various items. Questions could ask about either absolute (worldcentered) orientation or relative positions of the four items onscreen.

Results

North-up displays were better for absolute orientation questions, as assessed by angular

judgment errors and by RTs. In general, absolute judgments were slower than relative

judgments, except in case where map is north-up and there was no 3-D self-motion. 3-D

self motion increased errors in most conditions (perhaps producing an SAT in some

cases) but improved judgments of relative direction from helicopter, and had no effect on 

judgments relative to the UAV.

11) Wickens, C.D., & Dixon, S. (2003). Imperfect automation in unmanned aerial

vehicle flight control (Technical report AHFD-03-17/MAD-03-2). Savoy, IL:

University of Illinois, Institute of Aviation, Aviation Human Factors Division.

Employed the single-UAV task of Wickens & Dixon (2002) to examine the effects of

imperfect automated aids for detecting system failures and controlling UAV flight path.

Subjects flew simulated UAV missions to a command target (CT) locations while

concurrently searching for targets of opportunity (TOOs) and monitoring a set of gauges

for system failures (SFs). In a baseline manual condition, subjects flew without

automated aids. Three groups of subjects were provided an aid to signal system failures.

For one group, aid was perfectly reliable. For another group, aid was 67% reliable and 

prone to committing false alarms. For the third group, aid was 67% reliable and prone to 

committing misses. Two additional groups were provided an autopilot to control UAV

flight path. For one group, autopilot was perfectly reliable. For the other group, autopilot

was 67% reliable (i.e., prone to going off-course). A final group was provided both forms

of automation, with both being perfectly reliable.

Results

Data indicated that perfectly reliable aids improved performance relative to baseline, and 

that even the imperfect autopilot was beneficial. Furthermore, automated flight control

improved performance on the concurrent TOO search task. In contrast, imperfectly

reliable aids for SF detection produced no gains relative to baseline, and even perfectly 

reliable SF detection failed to improve TOO detection. Results suggest that the benefits

of later stage automation (i.e., automation of task execution) may be greater and more

robust than the benefits of early stage automation.

12) Dixon, S.R., & Wickens, C.D. (2004). Reliability in automated aids for

unmanned aerial vehicle flight control: Evaluating a model of automation dependence

in high workload (Technical report AHFD-04-05/MAAD-04-1). Savoy, IL: University

of Illinois, Institute of Aviation, Aviation Human Factors Division.
Human Factors of UAVs 26

Employed the single-UAV task of Wickens & Dixon (2002) to examine the effects of an 

imperfect automated aid for detection of system failures. Subjects flew simulated UAV

missions to a command target (CT) locations while concurrently searching for targets of

opportunity (TOOs) and monitoring a set of gauges for system failures (SFs). In A80 

condition, automated aid was 80% reliable and was equally likely to commit a miss or a

false alarm. In A60f condition, aid was 60% reliable and was 3x more likely to commit a

false alarm than a miss. This should have encouraged high reliance/low compliance. In

A60m condition, aid was 60% reliable and was 3x more likely to commit a miss than a

false alarm. This should have encouraged low reliance/high compliance. In a baseline

condition, subjects performed with no automated aid.

Results

Tracking error was unaffected by automation condition.

The number of instruction refreshes (presented visually) was higher in the A60m (M =

8.5) condition than in the baseline (M = 3). The number of refreshes for A80 (M = 5.57)

and A60f (M = 5.25) conditions were marginally lower than in A60m condition, and

were non-significantly higher than in baseline condition.

TOO detection rate was higher in the A80 condition than in baseline. No other

differences in detection rate between groups were significant. TOO detection times were

higher in the A60f and A60m conditions than in baseline. Data showed a non-significant

trend toward larger decrement in A60f condition, suggesting that a high false alarm rate

induced subjects to invest more visual resources in inspecting gauges in response to an 

alarm than a high miss rate did.

CT detection times were significantly and substantially (2 seconds) longer in A60f and

A60m conditions than in the baseline and A80 conditions.

SF detection rates were higher when workload was high (i.e., during loitering/inspection),

but this did not interact with automation condition. SF detection times were also higher

when workload was high, and showed an interaction with condition, reflecting the fact

that load increased detection times in A60f condition more than in any other condition.

Effects of load were similar on all other conditions. Comparison of A60f and A60m

conditions showed that in both cases, detection times were increased when automation 

missed the SF. Detection times when the automation detected the failure were longer in

the A60f condition than in the A60m, reflecting greater compliance with alarms in the

later condition.

A computational model accounted for the data well. Results of the modeling suggest that

compliance and reliance are linearly related to the automation's FAR and HR,

respectively, and are largely independent of one another.
Human Factors of UAVs 27

13) Dowell, S.R., Shively, R.J., & Casey, D.P. (2003). UAV ground control station

payload symbology evaluation. Paper presented at the Annual AUVSI Conference,

July 15-17, Baltimore, MD.

Compared the effects of floating compass rose and heading tape symbology on mission 

payload operators' ability to respond to change commands and SA queries. Symbology

formats also differed in their representation of sensor pitch: compass rose displays gave

pitch as a digital readout, heading tape displays depicted it with a wedge representation 

indicating the angle of declination. Commanded changes could be to sensor heading,

sensor pitch, sensor heading relative to air vehicle (sensor bearing angle), or to AV

heading. MPO did not perform AV heading changes, but called them out to confederate

pilot. Subjective measures of workload (NASA-TLX) and SA (SART) were also 

collected.

Results

Changes to sensor heading and sensor bearing angle were more accurate with heading 

tape than with compass rose symbology, with no SAT. Unexpectedly, changes to sensor

pitch were more accurate with compass rose symbology. Post-experiment interviews with

subjects suggest this might be due to size and gradient of marked increments on heading 

tape symbology. Control reversals were more frequent with compass rose than with

heading tape. SA probes didn't show much, and no differences were found in subjective

ratings.

14) Draper, M., Calhoun, G., Ruff, H., Williamson, D., & Barry, T. (2003).

Manual versus speech input for unmanned aerial vehicle control station operations.

Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting,

109-113.

Employed a UAV simulation to examine benefits of manual (keyboard) and speech input

modalities for intermitted data entry tasks during a continuous flight/navigation control

task. A manual command comprised a series of button presses. A voice command 

comprised a single word or short phrase. Subjects received intermittent signals to perform

data entry tasks during flight task. A response to each alert was required within 10

seconds, and the task was required to be completed within an allotted amount of time

thereafter. A failure to acknowledge alert was considered a miss, and a failure to

complete the task was considered a time-out.

Results

Task completion times were on average 40% shorter with voice commands. Benefits

ranged in magnitude from 3.14 to 21.43 seconds. Number of time-outs was almost 10

times higher with manual entry (M = .95 vs. M = .1), and the number of tasks completed 

incorrectly was approximately 23 times greater. Response time to alerts was faster for

manual entry mode, but difference was less than 1 second. RMS airspeed error, path 

error, and altitude error were all smaller in speech entry conditions. Subjective data also 

favored speech entry.
Human Factors of UAVs 28

As study was devised, speech entry mode generally required fewer commands than 

manual entry. Data do not indicate if speech entry would still be superior if modalities

were equated for number of steps required to execute commands.

15) Draper, M.H., Geiselman, E.E., Lu, L.G., Roe, M.M., & Haas, M.W. (2000).

Display concepts supporting crew communications of target location in unmanned

air vehicles. Proceedings of the IEA 2000/ HFES 2000 Congress, 3.85 - 3.88.

UAVs for intelligence, reconnaissance, and surveillance (ISR) usually have two 

operators, a sensor operator (SO) and an air vehicle operator (AVO). The AVO controls

the airframe, monitors subsystems, and communicates with the ground control station 

(GCS). The SO searches for targets using a UAV-mounted camera.

The AVO generally views scene with a larger FOV than the SO, and can therefore assist

in target detection by directing the SO's attention to targets outside the SO's current FOV.

Usually, the AVO attempts to communicate the target location verbally. The goal of

paper was to assess a pair of display concepts meant to facilitate AVO/SO

communication about target location. Two kinds of advanced displays were tested:

• Compass rose overlay on the SO's camera display Allows AVO to give direction 

in world-centered references (N, S, E, W), and should make translation to screencentered references easier for the SO

• Telestrator Allows AVO to designate target location on his display using a mouse,

then presents a locator line on the SO's display indicating the direction and 

distance in which SO should shift camera to find target

Four conditions were tested: baseline (control), compass rose, telestrator, compass rose +

telestrator

Results

Telestrator reduced the time necessary to designate the target, improved camera path

efficiency improved, and reduced workload. Compass rose was of little benefit.

16) Draper, M.H., Nelson, W.T., Abernathy, M.F., Calhoun, G.L. (2004).

Synthetic vision overlay for improving UAV operations.

The authors discuss potential benefits of synthetic vision systems (SVSs) for UAVs.

These include:

• SVS could improve SA by highlighting items of interest in camera image.

• SVS could allow operator to maintain SA if visual datalink is lost.

• SVS could facilitate communications between users who are not co-located.
Human Factors of UAVs 29

17) Draper, M.H., Ruff, H.A., Fontejon, J.V., & Napier, S. (2002). The effects of

head-coupled control and a head-mounted display (HMD) on large-area search

tasks. Proceedings of the Human Factors and Ergonomics Society 46th Annual

Meeting, 2139-2143.

Compared effects of various head-coupled and manually-controlled camera/display 

configurations on ability to locate targets in a 360-degree search task in a simulated 

UAV. Target acquisition was better with manual joystick/stationary CRT combination 

than with head-coupled HMD configurations. Workload ratings, SA ratings, and 

simulator sickness data also generally favored the non-HMD configurations.

18) Draper, M.H., Ruff, H.A., & LaFleur, T. (2001). The effects of camera 

control and display configuration on teleoperated target search tasks. Proceedings of

the Human Factors and Ergonomics Society 45th Annual Meeting, 1872-1876.

Subjects performed forward-field and rear-field search tasks in a UAV simulation using 

either A) joystick controlled camera with stationary CRT display, B) 1.0x gain head 

coupled camera with HMD, C) 1.5x gain head coupled camera with HMD, or D) 1.0x 

gain head coupled camera with HMD in conjunction with combined with manual joystick 

control.

Results

Configuration A produced best performance for forward-field search. Data showed no 

significant differences between configurations for rear-field search.

19) Fong, T., & Thorpe, C. (2001). Vehicle teleoperation interfaces. Autonomous

Robots, 11, 9-18.

Paper discusses various forms of interfaces for vehicle teleoperation. These include:

• Direct Operator manually controls aircraft, typically using controls that are

similar to those of a manned vehicle. This form of control/interface is appropriate

when 1) real-time human control or intervention is required, and 2) the control

station and vehicle are connected by a high-bandwidth, low-delay 

communications link.

• Multimodal/multisensor Multimodal interfaces "provide the operator with a

variety of control modes (individual actuator, coordinated motion, etc.) and 

displays (text, visual, etc.)...Multisensor displays combine information from

several sensors or data sources to present a single integrated view."

• Supervisory control Operator specifies subtasks which the vehicle then performs

on its own. This is appropriate when datalink bandwidth is low or

communications are delayed.

• Novel These include psychophysiologically-driven control, gesture-based control,

web-based interfaces, PDA-based interfaces.
Human Factors of UAVs 30

20) Fontejon, J., Calhoun, G., Ruff, H., Draper, M. & Murphy, K. (2004). Tactile

alerts for monitoring tasks in complex systems.

An earlier study (Calhoun, et al, 2002) found that tactile alerts could speed detection of

system faults in a multi-task environment. In that experiment, subjects were required to

detect & identify system faults while also performing a manual tracking task. Two tactors

were used to signal four possible system faults: combination of tactor location and 

vibration frequency signaled which of four system parameters was in fault. Performance

was best (RT shortest) when one tactor was located on each arm. When both were on a

single arm, performance was better with the right than the left arm.

In the study described above, all participants were right-handed. Additionally, manual

tracking was performed with the right hand. The current study was conducted to

determine if similar results would obtain for A) left-handed subjects, and B) when subject

performed the tracking task using the left hand.

Results

RTs were shortest when tactors were located on different arms. When they were on the

same arm, there was no significant difference in RTs for left & right arms. Hand used for

tracking did not have any affect on RT to faults.

21) Gawron, V.J. (1998). Human factors issues in the development, evaluation,

and operation of uninhabited aerial vehicles. AUVSI '98: Proceedings of the

Association for Unmanned Vehicle Systems International, 431-438.

The author discusses a number of unique human factors concerns unique to UAV flight.

These include:

• Data link drop outs may be difficult for operator to notice.

• UAV mission times may exceed human vigilance capability.

• Humans can attend to/inspect only one stream of images at a time, while some

UAVs may provide multiple image streams.

• Operators are sometimes given with unprioritized lists of multiple of targets to 

search for. This may be especially problematic when the operator is asked to 

control multiple UAVs simultaneously.

• Crew coordination depends on appropriate communications flow between crew

members, which can be difficult when crew is large or when crew members are

not co-located.

• Visual imagery is difficult to obtain during rocket launching or UAV, and during 

net or cable arrest. Workload is also high during launch and recovery. Finally,

small sensor FOV can reduce SA and make navigation, target acquisition, and 

traffic detection difficult.

• Manual control of vehicles with time delays is difficult.

• Control interface on some systems is poorly designed.

• Software is not standardized, even between instances of the same UAV system.
Human Factors of UAVs 31

Proposed military uses for UAVs include special operations; point reconnaissance, cued 

surveillance, and target acquisition. Non-military uses are possible in the fields such as

law enforcement, fire fighting, agriculture, construction, archaeology, geology, and postal

delivery.

22) Gluck, K.A., Ball, J.T., Krusmark, M.A., Rodgers, S.M., & Purtee, M.D.

(2003). A computational process model of basic aircraft maneuvering. Proceedings

of the Fifth International Conference on Cognitive Modeling, 117-122.

Paper presents an ACT-R model of Predator UAV flight. The model is based on 

simulation used to train Air Force Predator operators. The simulation involves three

tasks: basic maneuvering, landing, and reconnaissance. The modeling effort presented in 

this paper focuses on basic maneuvering. Pilot is required to make constant-rate changes

in airspeed, altitude, and heading. A total of seven maneuvers are involved. The first

three require pilot to change one flight parameter and hold the other two constant. The

second three maneuvers require pilot to change two flight parameters and hold third

constant. The seventh maneuver requires the pilot to change all three flight parameters

simultaneously.

The model uses a flight strategy called "Control and Performance Concept". First, the

operator establishes appropriate control settings for desired performance. Next, the

operator cross checks instruments to determine if the desired performance is being 

achieved. The rationale is that control instruments have a first-order effect on aircraft

behavior, which shows up only as a second-order effect in performance instruments.

Results

RMSD for airspeed, altitude, and heading were normalized and summed to provide an 

overall measure of performance. Grand mean performance on this measure over 20 runs

of the model was almost identical to grand mean performance of 7 subjects. Across

maneuvers, r-squared for predicting human performance from model was .64. The model

was also sensitive to maneuver complexity in the same way that human subjects were,

showing better performance for one-axis maneuvers than for two axis-maneuvers and 

better performance for two-axis maneuvers than for three-axis maneuvers.

23) Gorman, J.C., Foltz, P.W., Kiekel, P.A., Martin, M. J., & Cooke, N. J. (2003).

Evaluation of latent-semantic analysis-based measures of team communications.

Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting,

424-428.

The authors used Latent Semantic Analysis to develop methods of assessing 

communications content between team members in a Predator UAV simulation.

Measures used were communications density (CD), the average task relevance of the

team's communications, and lag coherence (LC), a measure of task-relevant topicshifting. Data came from two experiments in which teams of three-operators (air vehicle

operator, payload operator, and navigator) flew simulated Predator UAV reconnaissance
Human Factors of UAVs 32

missions. In the second experiment, workload levels were manipulated (low vs. high) and

some teams of operators were distributed rather than colocated.

Results

Communications density

Team performance in Experiment 1 was related to CD by a quadratic function, indicating 

that beyond some point performance declined with additional communication. Similar

results were found for co-located and low-workload teams in Experiment 2. Under highworkload conditions, performance continued to increase as CD increased, showing no 

evidence of a quadratic trend or an optimal CD level. Data from distributed team

conditions was too noisy to interpret clearly.

Lag coherence

Coherence was positively correlated with team performance, indicating that lowperforming teams tend to shift topics more within a short window than high-performing

teams.

24) Gugerty, L. & Brooks, J. (2004). Seeing where you are heading: Integrating 

environmental and egocentric references frames in cardinal direction judgments.

Journal of Experimental Psychology: Applied, 7, 251-266.

Navigational tasks often require operators to make cardinal direction judgments, which 

data suggest are difficult. The goal of the experiments reported here was to examine the

strategies by which people make cardinal direction judgments.

Experiment 1

Subjects performed a static judgment task. Stimuli each trial were A) a north-up map 

showing their aircraft and a footprint of a forward-facing vehicle-mounted camera, and 

B) a 3-D view of the terrain as seen through the vehicle-mounted camera. The view

presented each trial contained a building with a parking lot on each side (N, S, E, W).

One of the parking lots contained vehicles while the others were empty. The subjects' job 

was to indicate the cardinal direction of the occupied parking lot, relative to the building.

Results

Three noteworthy effects were evident in both the error rate and the RT data. The first

was a misalignment effect, whereby performance declined as camera heading deviated 

from north-up. The second was a south-advantage effect, whereby performance was

substantially better when camera was oriented south than when it was at nearby

orientations. The third was cardinal-direction advantage effect, whereby judgments were

more slightly accurate when the camera was oriented east or west than when it was at

nearby orientations.

Experiment 2

The goal of experiment 2 was to determine if dynamic spatial information, such as that

provided by controlling a vehicle, improves cardinal direction judgments. The dynamic
Human Factors of UAVs 33

task used was a simulated UAV mission. Subjects were provided three visual channels,

A) a north-up map similar to that used in Experiment 1, B) a 3-D view of terrain from a

rotatable vehicle-mounted camera, similar to that used in Experiment 1, and C) a standard 

flight display. The subjects' task was to pilot the UAV to a 10 target objects and answer

questions about each one. Three of the 10 questions required cardinal direction 

judgments.

Subjects also performed a static judgment task identical to that of Experiment 1.

Results

Static judgment task replicated the results of the first experiment. Cardinal-direction 

judgments in the dynamic task showed effects similar to those of the static task, though

the cardinal direction advantage was weaker.

Experiment 3

Subjects performed the cardinal direction task of Experiment 1 and 2 while providing

verbal protocols.

Results

Protocols gave evidence for final strategies. The first was a mental rotation strategy,

whereby subjects mentally transformed images to be in alignment with one another and 

north-up. The second was a heading referencing strategy, whereby subjects assigned the

current heading to the forward view in the camera, then making judgments relative to that

heading ("If forward is northeast, then this is north [pointing to the upper left lot], and 

this is east [pointing to the upper right lot]." The third was a south-reversal strategy,

whereby subjects noted that camera heading was south and then reversed the answers

they would have given for a northward heading (this was possible only when camera was

oriented toward the south, obviously). The final strategy was a north-heading strategy,

whereby subjects noted that camera was oriented to the north and then simply made

judgments within a canonical north-up frame.

25) Gunn, D.V., Nelson, W.T., Bolia, R.S., Warm, J.S., Schumsky, D.A., &

Corcoran, K.J. (2002). Target acquisition with UAVs: Vigilance displays and

advanced cueing interfaces. Proceedings of the Human Factors and Ergonomics

Society 46th Annual Meeting, 1541-1545.

The authors note that UAV operators will probably spend much of their time in

supervisory control mode, but will be required to switch to manual control suddenly in

response to system malfunctions, target acquisition, enemy actions, and other intermittent

events. As such, UAV operation will be a form of vigilance task. The goals of study were

A) to examine value of display type (cognitive or sensory) on performance in a vigilance

task and in subsequent manual control mode, B) to compare the effects of visual,

auditory, and haptic cueing of target location in a 360 degree target acquisition task.
Human Factors of UAVs 34

Subjects flew simulated UAV missions. In supervisory control mode, they were required 

to monitor a stream of digit pairs for a threat warning indicating the presence of an enemy

aircraft. In the sensory task, the threat warning was signaled by a size difference between 

the two digits. In the cognitive task, the threat warning was signaled by an even-odd digit

pairing. After detecting a threat warning, the subject was required to target the hostile

aircraft with a joystick-controlled crosshair. In the visual cueing condition, a locator line

on the bottom right of screen indicated the direction of the target. In the auditory cueing 

condition, broadband noise pulses were presented from the target location. In the haptic

cueing condition, force feedback on control stick guided the subject toward the target. In

the control condition, no cueing was provided.

Results

Hit rates for warnings showed no effect of signal rate, but a significant benefit of sensory 

display format relative to the cognitive format. False alarms were lower for cognitive

than for sensory displays. Target acquisition times were shorter for sensory displays than 

for cognitive. Visual, auditory, & haptic cue conditions produced similar benefits in 

target acquisition times, all of which were shorter than in control condition. Subjective

workload was higher with cognitive than with sensory displays.

26) Hansman, R.J., & Weibel, R.E. (2004). Panel 1: UAV classification thoughts.

Paper presented at NRC Workshop on UAVs.

Proposes a classification scheme for UAVs in NAS.

High altitude, long endurance

• Above FL 500, above majority of commercial air traffic

• Potential applications include long-dwell missions such as communications relay,

precision mapping/imaging, and atmospheric research

Medium altitude endurance

• FL 180 - FL 500, Class A airspace

• Potential applications include meteorology, disaster monitoring, border patrol,

and regional mapping

Tactical

• 1000 to FL 180/10,000 ft., mixed airspace

• Potential applications include law enforcement surveillance, pipeline/rail

monitoring, search and rescue, agriculture

Mini

• Below 1200/700 ft. AGL

• --Potential applications include law enforcement, local imagery, and

cinematography

Micro

• Below 1200/700 ft. AGL
Human Factors of UAVs 35

• Potential applications include recreation, and local imagery

Rotorcraft

• Up to 2000 ft. AGL

• Potential applications include search & rescue, law enforcement, traffic

monitoring, cinematography, and agriculture

27) Hansman, R.J., & Weibel, R.E. (2004). Panel 2: Operating and flight rules.

Paper presented at NRC Workshop on UAVs.

Presents safety analyses of UAV, deriving acceptable failure rates (mean numbers of

hours until failures) for varying classes of UAVs. Note a number of safety issues for

UAVs operating under instrument and visual flight rules.

IFR

• Control latency

• Communication paths

• Controller workload and representation

• Separation standards

• Traffic load

• Flight plan filing

• Cost recovery

VFR

• See and be seen equivalence

• Rules of the road

• Operation at controlled and uncontrolled airfields

28) Kiekel, P.A., Gorman, J.C., & Cooke, N.J. (2004). Measuring speech flow of

co-located and distributed command and control teams during a communication

channel glitch. Proceedings of the Human Factors and Ergonomics Society 48th

Annual Meeting, 683-687.

An experiment used communication flow measures developed by the authors in earlier

papers to examine effects of co-location and communication channel disruptions on team

communications in a simulated UAV recon task. Teams of three members flew a

simulated UAV, taking pictures of target items. Each team comprised three members: the

data exploitation, mission planning and control (DEMPC) member planned the route, air

vehicle operator (AVO) flew the aircraft, and the payload operator (PLO) controlled the

camera and took pictures. Manipulations were A) teams colocated vs. teams distributed,

B) workload high vs. low (workload effect not further discussed in this paper), and C)

communications normal or disrupted by glitch in channel from DEMPC to AVO.

Three types of analysis were conducted. The first used Pathfinder algorithm to identify

common sequences of communications events: XLoop (person X begins and ends a

communication, then begins another), XYcycle (person X produces a complete
Human Factors of UAVs 36

communication, then person Y does), and XiY (person X interrupts person Y). CHUMS

analysis measured the stability of communications, as reflected in the relative proportion 

of speech produced by each member in a one-minute window. Analysis of dominance

measured the influence that each team member's communications had over other

member's.

Expectation was that occurrence of glitch would modify communication pattern, that

DEMPC should have high dominance score, and that dominance of DEMPC should drop

in distributed teams and when glitch occurs.

Results

Pathfind analysis found that colocated teams produced more utterances in general than 

distributed teams. Glitch causes decrease in DAcycles (communications between 

DEMPC and AVO), increase in DPcycles, increase in PAcycles, and decrease in

PDcycles. This is generally what would be expected if communications that normally 

would have gone from DEMPC to AVO were re-routed through the PLO following the

glitch.

CHUMS analysis produced more models for distributed teams, suggesting less stable

communications patterns. The communications glitch also reduced stability.

Analysis of dominance found that in co-located teams under normal conditions, the

DEMPC is moderately dominant and the AVO is reactive. In distributed teams under

normal conditions, AVO is distributed and DEMPC is reactive. During the

communications glitch, co-located teams become AVO dominant and PLO reactive.

29) LaFleur, T., Draper, M.H., & Ruff, H.A. (2001). Evaluation of eyedominance effects on target-acquisition tasks using a head-coupled monocular

HMD. Proceedings of the Human Factors and Ergonomics Society 45th Annual

Meeting, 1433-1437.

Subjects performed a target acquisition task in a UAV simulation. A large FOV display 

was presented with a monocular HMD. Image was provided by the UAV's gimbaled 

camera, with camera control by operator's head movements. After spotting a potential

target in the HMD, the operator was required to ID and designate it on a high-resolution,

small FOV CRT display with camera view controlled by joysticks. Several dependent

variables recorded. Of primary interest were effects of dominance of HMD viewing eye

on performance.

Results

Eye-dominance had no effect.

30) Miller, C.A., Funk, H.B., Goldman, R.P., & Wu, P. (2004). A "playbook" for

variable autonomy control of multiple heterogeneous unmanned air vehicles.
Human Factors of UAVs 37

Proceedings of the Second Human Performance, Situation Awareness, and 

Automation Conference (HPSAA II), Daytona Beach, FL, March 22-25.

Discusses "delegation" as a technique for control of multiple UAVs. Characteristics of

delegation are that supervisor sets agenda for subordinates, but subordinates are given 

authority to decide exactly how to carry out commands. The authors note five

manners/components of delegation that can be employed in varying combinations:

1. Stipulation of goal

2. Stipulation of a plan to perform

3. Stipulation of constraints (via specification of actions or states that should be avoided)

4. Stipulation of actions or states to be achieved (i.e., subgoals)

5. Stipulation of an objective function that will allow the subordinate to assess the

desirability of various states and actions

The authors describe their work on developing a "playbook" architecture for delegating to

UAVs. Playbook would involve assigning a name to complex behavior patterns, then

allowing UAVs to autonomously implement a play when it is called. The Playbook

system would assess the feasibility of a commanded behavior before attempting to 

perform it. When given a high-level command, Playbook would assess various methods

of achieving goal, then would issues specific commands to vehicles under its control.

When given more highly-specified lower-level commands, Playbook would report to the

human operator if the commands were infeasible, or would issues the commands to the

vehicles if they were feasible, filling in additional details as necessary.

31) Morphew, M.E., Shively, J.R., & Casey, D. (2004). Helmet mounted displays

for unmanned aerial vehicle control. Paper presented at the International Society for

Optical Engineering (SPIE) conference, April 12-16, Orlando, FL.

Compared performance on a target search & ID task when subjects used a conventional

CRT display & joystick control versus when they used a head-slaved HMD. UAV flight

was automated. Subjects' task was to search for items in the virtual world display and ID

them as target, non-target, or distractor. After spotting a target or non-target, subject was

to center crosshairs on the item and press a button on control box to classify it.

Independent variables were method of display/control (CRT/joystick vs. HMD/headslaved), virtual world search width (2500 vs. 5000 ft.), and mission duration (3 vs. 9 

minutes). Dependent variables were target detection accuracy (HR, CRR), cursor distance

(distance of crosshairs from center of object when object was classified), slant range

(distance from aircraft at which subjects were able to classify target),

Results

Accuracy was high (>98%) for both forms of display/control. However, cursor distance

was smaller and slant range was larger (i.e., performance was better in both cases) for the

CRT/joystick configuration. HMD configuration also produced higher levels of nausea,

eyestrain, disorientation, and over simulator sickness rating than the CRT configuration.

Wide search width produced smaller cursor distance than did narrow width, but effect did 

not interact with any others.
Human Factors of UAVs 38

32) Mouloua, M., Gilson, R., Daskarolis-Kring, E., Kring, J., & Hancock, P.

(2001). Ergonomics of UAV/UCAV mission success: Considerations for data link,

control, and display issues. Proceedings of the Human Factors and Ergonomics

Society 45th Annual Meeting, 144-148.

Lists a number of considerations and recommendations for optimal design of

UAV/UCAV system interface and data transfer.

Data-link timing

If satellite-UAV or UAV-UAV relays are used, variable time delays of 1 second or

longer are possible. This eliminates real-time feedback to controls inputs. One way of

circumventing this problem is to task operator with supervisory monitoring of on-board

automation using pre-programming flight parameters such as speed, altitude, direction.

Predictive graphics displays may also be useful.

Controls

Neither full automated control nor full manual control is practical. Full automation

prevents the operator from intervening in flight control when necessary, while full

manual control can produce excessive workload and make control susceptible to 

communications delays. The authors recommend hybrid control in which human operator

supervises automation by calling subroutines of pre-programmed software.

Display/control interfaces should be based on a standardized group of core functions

described with common terminology. Keyboards, touchscreens, pointing devices, and 

joysticks/pedals are appropriate controls, but must be designed to resist dirt, damage, etc.,

especially for field operations. Keyboard inputs should be replaced with menu- or

speech- inputs for on-line vehicle control.

Assuming semi-automatic flight, flight-management systems and terminology should 

emulate that of ARINC and DOD. Since commands are anticipatory, this approach allows

for preview and escape actions. If manual flight control is used, a GCS with full-time

joystick/pedal/power controls will be necessary, and real-time communication with

UAV/UCAV will be required.

If menus are used for in-flight supervisory control, it will be necessary to determine

optimal number of menus and menu items. Five seems like reasonable starting point,

based on Miller's magic number.

Displays

Displays need to reduce and format data for easy interpretation. Other principles to 

follow include minimizing scene movement & unnecessary changes in viewpoint; using 

high-quality displays to help ID areas of interest; employing alerts/alarms for system

faults; providing mechanisms of selection, comparisons, parsing, scaling of displayed 

info.
Human Factors of UAVs 39

In addition to high-quality sensor-image displays, content should include:

• system conditions and communications status

• flight data

• threat advisories

• weapons status

33) Mouloua, M., Gilson, R., Kring, J., & Hancock, P. (2001). Workload,

situation awareness, and teaming issues for UAV/UCAV operations. Proceedings of

the Human Factors and Ergonomics Society 45th Annual Meeting, 162-165.

The authors discuss UAV design considerations relevant to workload, SA, and teaming.

Some of these issues pertain specifically to combat UAVs.

Physical & cognitive workload

Assuming that the UAV control will be highly automated, then the operator's task will

consist primarily of supervisory monitoring and making small course adjustments. This is

likely to be tedious, producing vigilance failures. UAV systems must therefore be

designed not just to avoid overload, but underload as well. This might be done by 

combining manual and automated control. Operator would be responsible for higherorder tasks (target recognition, munitions deployment) and automation would be

responsible for lower-order tasks (flight control, obstacle avoidance). On-board 

automation should require operator action only as needed.

Situation awareness

Poor SA is likely to contribute to UAV mishaps. One way to help maintain SA is to

provide displays that keep the operator aware of the processes being controlled by the

automation, with the goal "to make the deep relational structure of the system

environment visible to operators and help to identify options for action and indicate the

boundaries for successful performance." UAVs may also be able have and advantage

relative to manned systems in providing good SA since large numbers of on-board and 

off-board sensor data streams are available.

Teaming

Research is needed to determine the appropriate crew size and structure for UAV control,

and to ensure effective communications. One particular source of miscommunication is

the large amount data provided to the various UAV operators. This demands that

important information be shared appropriately among operators. Ways to do this include

"creating a mechanism for communicating understanding the real-time situation at a

higher level across several connected teams or individuals", creating teams of specialists

for target detection/authentication and for emergency operations.

34) Nelson, W. T., Anderson, T.R., McMillan, G.R. (2003). Alternative control

technology for uninhabited aerial vehicles: Human factors considerations. Book

chapter.
Human Factors of UAVs 40

Discusses potential alternative control technologies for UAVs. These include position 

and orientation tracking, eye-position tracking, speech recognition, gesture recognition,

and electrophysiological measures. The authors advocate increasingly immersive

environments for UAV pilots, with eventual possibility that alternative control

technologies will replace traditional controls. Possible impediments to these goals include

time delays in display updating, simulator sickness.

35) Nelson, J.T., Lefebvre, A.T., & Andre, T.S. (2004). Managing multiple

uninhabited aerial vehicles: Changes in number of vehicles and type of target

symbology. Interservice/Industry Training, Simulation, and Education Conference

(I/ITSEC).

The authors describe an experiment conducted with two goals. The first was to examine

changes in performance that result from increasing the number of UAVs under an 

operator's supervision. Contrary to expectations, past research (Draper, Calhoun, & Ruff,

2003) found limited performance consequences when the number of UAVs under a single

operator's supervision increased from 2 to 4. The present experiment compared 

performance using 3 and 5 UAVs, in an effort to increase the workload demands of the

higher-load condition. The second was to compare performance with a novel set of

stylized icons to performance with a set of standardized icons (MIL-STD_2525B). The

authors hypothesized that the stylized icons, designed to have a physical resemblance to 

the objects they represented, would produce better performance.

After training, subjects flew two missions in the Multi-modal Immersive Intelligent

Interface for Remote Operation (MIIIRO). One mission involved control of 3 UAVs, the

other involved control of 5 UAVs (order of missions counterbalanced). Flight control was

automated. Subjects were responsible for several additional tasks: identifying unknown

aircraft, approving replans of UAV routes, identifying and selecting targets in the

imagery from UAV sensors, completing tasks on a mission mode indicator, and counting 

symbols on the Tactical Situation Display (top-down map of terrain with UAVs and 

routes depicted) for later recall. Subjective workload measures were also collected.

Results

Data showed no difference between the 3 UAV and 5 UAV conditions in the number of

enemy targets identified and selected. Other dependent variables showed significant

effects favoring the 3 UAV condition. Specifically, the time to respond to unidentified 

aircraft was shorter, the number of Mission Mode Indicator tasks completed was higher,

the time to check and approve UAV replan routes was shorter, and all subjective

performance measures (ratings of situation awareness, perceived task difficulty,

perceived performance, and perceived workload level) were better.

Recall of symbols on the TSD was better with the standardized icons than with the

stylized set. The authors speculate that this might have happened because the

standardized icons were easier to perceptually segregate from the background.
Human Factors of UAVs 41

36) Purtee, M.D., Gluck, K.A., Krusmark, M.A., Koffe, S.A., & Lefebvre, A.T.

(2003). Verbal protocol analysis for validation of UAV operator model. Proceedings

of the 25th Interservice/Industry Training, Simulation, and Education Conference,

1741-1750.

Used concurrent and retrospective verbal reports from subject matter experts piloting a

Predator UAV simulation to determine how accurately the lab's ACT-R model of

Predator pilot performance represents the cognition/information processing of actual

pilots.

Results

Overall, attention to performance instruments was verbalized more often than attention to

control instruments during concurrent reports. However, retrospective reports suggested 

that SMEs were using the Control and Performance concept implemented by the model.

Results also demonstrate that distribution of operator attention, as reflected in concurrent

verbal reports, is influenced by goals/demands of maneuver being implemented. Ideas for

improving the cognitive model include incorporating use of trim and a metacognitive

awareness of passage-of-time to improve use of timing checkpoints for monitoring

progress toward goal.

37) Quigley, M., Goodrich, M.A., & Beard, R.W. (2004). Semi-autonomous humanUAV interfaces for fixed-wing mini-UAVs. Proceedings of IROS 2004, Sep 28 – Oct

2, Sendai, Japan.

The paper describes work prototyping and testing several forms of interface for control of

small (32" wingspan, in this case) semi-autonomous UAVs.

Control techniques

Numeric parameter-based interfaces provide text boxes in which operator types desired 

flight parameters.

PDA direct manipulation interface presents fixed-horizon wing-view representation from

the viewpoint of an observer behind the UAV. Display also includes a compass and 

speedometer alongside the wing-view display. The user controls the UAV through dragand-drop manipulation of the UAV icon or the compass/speedometer. Color differences

(blue vs. red) are used to distinguish current state of UAV from desired state specified by

user manipulation. This makes the future state of the UAV easy to predict. This interface

was also tested with a laptop using trackpad and mouse.

Voice controller is allows UAV control using a grammar of twenty words (e.g., "climb",

"go north"). Voice synthesizer provides immediate feedback in present progressive tense

(e.g., "climbing", "going north").
Human Factors of UAVs 42

Attitude joystick controller mimics a fly-by-wire controller by mapping deflection in

joystick to deflection in aircraft attitude. This form of interaction is especially good for

novices, non-experience pilots.

Trackpoint controller uses a trackpoint pointing device from ThinkPad laptop, with 

horizontal inputs mapped to UAV roll and vertical inputs mapped to UAV pitch.

The physical icon interface is a hand-held model of UAV. Orientation of model in 3-D

space is tracked, and converted into pitch / roll commands for UAV. Should provide good 

SA since the operator is holding a representation of UAV in its actual orientation. An 

accompanying (optional) display helps the user distinguish actual and desired UAV states

by presenting the current state in one color and the desired state (i.e., state of physical

icon control) in another.

Assessments

The attitude joystick, physical icon, and trackpoint controller produce the fastest operator

response times for UAV manipulations. However, these re problematic when UAV is

traveling toward the operator, since they require reversal of control inputs.

Direct manipulation interfaces are useful because they don't required sustained attention.

After the user has specified the desired UAV state, no further interaction is necessary 

until another change of state is desired. This can make these interfaces especially 

appropriate for control of multiple UAVs. The only difficulty is that subject must shift

gaze between interface and UAV. This creates demand for frequent accommodation 

changes.

Voice control allows operator to maintain gaze on UAV, and, assuming commands can 

be issued in world-centered coordinates (e.g., "go north"), does not require mental

transformation of commands depending on heading of UAV.

Parameter based interfaces are difficult because they require operator to type, and also 

demand mental math.

38) Ruff, H.A., Calhoun, C.L., Draper, M.H., Fontejon, J.V., Guilfoos, B.J.

(2004). Exploring automation issues in supervisory control of multiple UAVs.

Proceedings of the Second Human Performance, Situation Awareness, and 

Automation Conference (HPSAA II), Daytona Beach, FL, March 22-25.

Experiment 1

Examined the effects of automation on task completion time and subjective workload 

levels in control of 2 or 4 UAVs. Two levels of automation (LOA) were compared,

management by consent (MBC) and management by exception (MBE). Automation was

provided for two tasks, image prosecution and route re-plans. Time limits for overriding 
Human Factors of UAVs 43

automation in the MBE condition were 40 s for image prosecution and 15 s for route replan. Automation reliability was either 75% or 98% for image prosecution, 75% or 100%

for route re-plan.

Overall task involved four sub-tasks

1. Responding to unidentified aircraft

2. Route re-planning

3. Image prosecution

4. Monitoring Mission Mode Indicator (MMI)

Results

An increase in number of UAVs increased completion times for image prosecution, route

re-planning, and MMI monitoring, and also increased the amount of time was spent in

threat zones. Subjects were more likely to respond before MBE automation acted (i.e.,

subjects were more likely to carry out the automation's recommendation than to simply 

let the automation carry out the action itself) when there were 2 UAVs than when there

were 4. Subjective workload ratings were higher for 4 UAVs than for 2.

Fewer images were prosecuted and more errors committed when automation was less

reliable. Subjective trust ratings were also lower.

No consistent differences obtained between MBC and MBE . This might be because the

time limit for overriding MBE automation was too long. As noted above, subjects often 

pre-empted the automation under MBE by carrying out the recommended action 

themselves. Expt 2 therefore reduced time limits for MBE.

Experiment 2

Automation reliability and LOAs were same as in Expt 1, but the time limit for

overriding MBE was now included added as a dependent variable. Time limits for image

prosecution were 15 and 40 seconds. Time limits for route re-plan were 10 and 15 

seconds. Execution of route re-plan task was also made more difficult. Only the 4 UAV

condition was used.

Results

Only one measure, the percentage of images correctly prosecuted, showed an effect of

automation reliability.

With MBC, subjective ratings were similar across time limits. With MBE, shorter time

limits produced higher workload ratings and poorer performance on both tasks. Subjects

were faster to complete tasks in MBE/short time limit condition than in any other

combination of automation/time limit, suggesting that they felt time pressure. This may 

have contributed to higher subjective workload. MBE automation was more likely to 

execute action in short time limit condition.
Human Factors of UAVs 44

39) Ruff, H.A., Draper, M.H., Lu, L.G., Poole, M.R., & Repperger, D.W. (2000).

Haptic feedback as a supplemental method of alerting UAV operators to the onset of

turbulence. Proceedings of the IEA 2000/ HFES 2000 Congress, 3.41 - 3.44.

UAV operators are denied many of the sensory cues available to the pilot of a manned 

aircraft. One instance in which this might be consequential is in detecting turbulence.

During flight of a manned aircraft, the onset of turbulence typically produces

kinesthetic/haptic feedback. During UAV flight, turbulence is signaled to the operator

only by perturbations of camera image.

Current experiment measured value of haptic alert (via control stick) for detection of

turbulence onset. Participants flew simulated UAV landings. When turbulence occurred,

subjects rated their level of SA. After each trial, participants rated the difficult of

difficulty, assessed their performance, and judged the strength (mild or severe) and axis

of perturbation (horizontal or vertical) of the turbulence. Note that the multimodal display 

did not mimic the haptic signals experienced in real flight, but was simply meant as an

alerting signal.

Results

Haptic feedback improved SA ratings, but if when turbulence occurred when UAV was

far from the runway. When UAV was near runway, no benefit of feedback. Authors

suggest that heightened alertness near runway might facilitate turbulence detection,

mitigating the effects of haptic feedback. RTs for turbulence detection would have

provided useful data to test this hypothesis.

Subjective ratings of landing performance were unaffected by haptic alert, but ratings of

landing difficulty increased when haptic alert was provided. Perceived turbulence

strength and judgments of turbulence direction were unaffected by haptic feedback.

40) Ruff, H.A., Narayanan, S., & Draper, M.H. (2002). Human interaction with

levels of automation and decision-aid fidelity in the supervisory control of multiple

simulated unmanned air vehicles. Presence, 11, 335-351

Subjects flew simulated UAV missions, with task of acquire four targets at unknown

locations (3 enemy and 1 friendly) while avoiding enemy fire. Number of UAVs was 1,

2, or 4. Flight path was preprogrammed. Subjects were required to respond to/manage

events as they occurred through course of scenario. Automation was provided to some

subjects to help manage events. Two forms of automation were used, in addition to the

no-automation baseline: management by consent, and management by exception. Two 

levels of automation reliability were used: 100% and 95%.

Results

Management by consent produced the highest level of mission efficiency (number of

enemy targets destroyed divided by number of missiles fired). Management-by-exception 
Human Factors of UAVs 45

and manual monitoring produced similar efficiency scores. Decision aid false alarms in

the 95% reliability automation condition were more likely to be detected under

management-by-consent than management-by-exception.

In manual condition, event management became poorer as the number of UAVs

increased. Subjective workload estimated by NASA_TLX also increased with number of

UAVs in the manual and management by consent conditions. SWORD ratings of

workload were higher for manual control than for either form of automation, and were

higher for management-by-consent than for management-by-exception when reliability 

was less than perfect. Sword ratings also increased as the number of UAVs increased.

Management-by-consent produced higher levels of self-rated situation awareness than did 

manual control or management-by-exception. Management-by-exception produced 

especially low ratings of SA when automation reliability was only 95%. SA ratings also 

decreased as the number of UAVs increased.

Trust in automation decreased as the number of UAVs increased.

41) Ryder, J.M, Scolaro, J.A., Stokes, J.M. (2001). An instructional agent for

UAV controller training. UAVs-Sixteenth International Conference, 3.1-3.11. Bristol,

UK: University of Bristol.

Describes development of an automated agent, EAGLE, to train pilots on a simulated 

Predator UAV landing task. The authors note that because there are minimal differences

between operating a console during real missions and simulations, simulations may be

ideal for UAV operator training. The current instructional agent was developed using 

CHI Systems' COGNET framework.

42) Schreiber, B.T., Lyon, D.R., Martin, E. L., & Confer, H.A. (2002). Impact of

prior flight experience on learning Predator UAV operator skills (AFRL-HE-AZ-TR2002-0026). Mesa, AZ: Air Force Research Laboratory, Warfighter Training

Research Division.

Examined subjects ability to learn & perform maneuvers on a Predator UAV. Compared 

several groups of subjects including experienced Predator pilots; experienced USAF

pilots selected to fly the Predator; students who had recently completed USAF T-38 

training; students who had recently completed USAF T-1 training; students who recently

completed single-engine instrument training at Embry-Riddle; students who recently

completed requirements for private pilot's license; Embry-Riddle ROTC students who 

planned to join USAF but had no flight training or experience.
Human Factors of UAVs 46

Subjects flew basic maneuvers and landings until reaching a criterion level of

performance, then flew 30 reconnaissance missions. Of interest was the number of trials

necessary to reach criterion performance on the training task, and the transfer of training

performance to the reconnaissance task.

Results

Training

As expected, predator pilots required the fewest trials to reach criterion performance, and

nonpilot ROTC students required the most. This comparison demonstrates the validity of

the simulation and task. Predator selectees and civilian instrument pilots required fewer

trials than T-1 grads, required fewer trials. T-38 grads and private pilots were not

significantly worse than Predator selectees/civilian instrument pilots, nor were they

significantly better than T-1 grads. Results demonstrate that prior flight experience can 

reduce the number of trials to become proficient at maneuvering & landing the Predator

simulation.

Transfer

Dependent variable was mean amount of time that sensor was focused on target during

each trial. Predator pilots, Predator selectees, and T-38 grads had more time on target

than other groups. ROTC nonpilots had less time on target than Predator pilots, selectees,

T-38 grads, and T-1 grads. Results show that even after subjects achieved matched levels

of performance on the training task, prior flight experience improved performance in the

recon task. Authors suggest that good performance of T-38 grads as compared to T-1 

grads may reflect the degree to which performance characteristics of the T-38 and T-1 are

similar to those of the Predator.

43) Tvaryanas, A.P. (2004). Visual scan patterns during simulated control of an

uninhabited aerial vehicle (UAV). Aviation, Space, and Environmental Medicine, 75,

531-538.

An experiment examined pilots' eye movements during a simulated Predator UAV flight

task. Goals were to determine A) how efficiently operators process the moving textbox

instrument displays used in the Predator HUD, B) whether workload (as determined by

windiness during flight and by the difficulty of flight maneuvers) affected scan patterns,

and C) whether the absence of auditory and haptic cues caused UAV pilots to increase

their dwell frequency on the engine instrument (RPM) relative to pilots in a manned 

aircraft. Of particular interest was whether or not the moving textbox instruments would 

be processed as digital/quantitative displays or as analog/qualitative displays. Past data

has suggested that quantitative displays are processed less efficiently (i.e., require longer

dwell times) than qualitative displays.
Human Factors of UAVs 47

Subjects were 5 instrument rated pilots. Subjects flew an eight-leg flight plan twice, once

in no-wind conditions and once in windy conditions (order randomized). Different

segments of the flight profile included changes in heading, altitude, and airspeed,

sometimes singly and sometimes in combination. Changes involving multiple parameters

were presumed to impose higher workload, as were windy flight conditions.

Results

Dependent variables were dwell times and dwell frequencies. Independent variables were

flight conditions (no wind vs. windy), flight segment (one, two, or three parameters

changed), and instrument. Both dwell times and dwell frequencies showed a significant

main effect of instrument, and neither showed any other main effects or interactions.

Dwell frequency was highest for the ADI, followed by the VSI, AS, HI, ALT, RPM, and

AOA.

To determine whether moving textboxes were processed as qualitative or quantitative

displays, dwell times for these instruments were compared to dwell time for the heading 

indicator, an instrument that is clearly quantitative. If moving textboxes are processed as

qualitative displays, they should have dwell times shorter than those for the HI. Only the

AOA and RPM had significantly shorter dwell times, suggesting that other moving 

textboxes were processed as quantitative displays.

The author also compares the current data to the results of earlier studies of instrument

scanning in manned aircraft. In the present study, ADI was the most frequently fixated 

instrument, but still accounted for only 30% of all dwells. In contrast, previous research 

has found that the ADI can account for over 50% of all dwells in manned flight.

Conversely, VSI was fixated more often in the present study (16% of all dwells) than in 

studies of manned flight (22%). The author suggests two possible reasons for these

differences. First, ADI in the Predator HUD was very simple, showing only a horizontal

line without any pitch or bank scale. VSI scanning might therefore be necessary to 

acquire or verify climb and descent rates. Second, pilots might rely on the VSI more

heavily in UAV given the inherent system delays. In other words, delays in the system

responses to control inputs might require operators to rely more heavily on the predictive

VSI instrument than in manned flight.

Data also suggest that the engine instrument was not scanned more heavily in the current

task than in previous studies of scanning in manned flight. This suggests that operators

did not use the RPM to compensate for the absence of auditory and haptic information,

and may indicate a sub-optimal performance strategy.

The author speculates that the high skill level of the participants in the current experiment

might explain the null effect of workload on scanning behavior.
Human Factors of UAVs 48

44) Van Erp, J.B.F. (2000). Controlling unmanned vehicles: The human factors

solution. RTO Meeting Proceedings 44 (RTO-MP-44), B8.1-B8.12.

The author notes that bandwidth constraints on the datalink between a ground control

station and UAV will limit the quality of sensor information displayed to UAV operators.

Two remedies to this problem are possible. The first is to reduce bandwidth needs by 

identifying task-critical sensor information and ensuring that only this is transmitted. The

second is to design advanced interfaces that assist the operator in compensating 

degradations or limits in sensor imagery.

Several specific ways in which the information provided to a UAV operator is degraded 

are described. First, this information typically includes only imagery from an on-board

camera. Input from other sensory modalities (audition, kinesthesia) is lost. Second, the

sensor imagery provided to the operator is often of low resolution, achromatic, and 

limited to a small FOV. Third, sensor imagery is often of low temporal resolution.

Fourth, the control devices used to manipulate sensor cameras do not provide

proprioceptive/kinesthetic feedback similar to that obtained in using the scanning through 

head and eye movements.

The author next delineates a variety of sensor image characteristics that contribute to

vehicle control: field size, magnification factor, chromaticity, temporal resolution, spatial

resolution, monoscopic vs. stereoscopic presentation, fixed vs. variable viewing direction,

and placement/aiming. To optimize performance, operator can be given the capability to 

manually adjust the temporal and spatial display resolution, reduce the image field size,

and toggle between color/grayscale and between stereoscopic/monoscopic display modes.

The UAV operator is also confronted with difficulties in payload sensor control. First,

controls do not provide feedback on camera responses to user inputs. Second, the

operator does not receive vestibular feedback to specify vehicle attitude. Third, the

operator has no proprioceptive feedback to indicate viewing direction. Fourth, control

inputs do not produce immediate changes in sensor imagery. Fifth, spatial information

within the sensor imagery is low in resolution. Sixth, the sensor FOV is often small,

imposing the need for additional control inputs to scan a scene and degrading the

operator's ability to integrate sensor images into a coherent and veridical mental

representation. Seventh, camera imagery may be zoomed-in, disturbing the normal

relationship between camera translation and image motion. Finally, image update rates

may be low, degrading the temporal resolution necessary for dynamic tracking tasks. The

author discusses a number of advanced display designs to address the difficulties in 

camera control produced by these degradations. Two of these involve computersynthesized imagery superimposed upon or embedding the camera imagery. The value of

such "augmented reality" displays is that they computer-generated components can be

updated immediately in response to user inputs, even if the sensor imagery itself is not

updated until after a delay. The computer-generated components thus can provide realtime feedback to assist in guiding the sensor footprint. The third novel display discussed 

is a radar image that includes actual and predictive sensory footprints. Thus, motion of

the computer-generated predictive sensor footprint again provides operator with

immediate feedback to aid camera targeting, despite delays in camera update rate. Head-
Human Factors of UAVs 49

coupled camera control, the author notes, does not improve camera control in a search 

task (effect of the head-coupled control is a speed-accuracy tradeoff), and may degrade

performance because of mismatches in proprioceptive and visual information produced 

by sensor delays.

45) Van Erp, J.B.F., & Van Breda, L. (1999). Human factors issues and advanced 

interface design in maritime unmanned aerial vehicles: A project overview. TNOreport TM-99-A004. Soesterberg, The Netherlands: TNO Human Factors Research

Institute.

Report presents a summary of human factors issues in UAV control, and an overview of

relevant research conducted at TNO.

Human factors concerns

The authors assume that vehicle control will generally be highly automated, and so focus

their discussion of on manual control of payload camera. The studies reported assume

that the most important source of information for camera control will be the imagery 

from the on-board camera.

The authors note that the perceptual information the operator receives from the remote

environment is likely to be degraded in several ways:

--no proprioceptive feedback from controls

--no vestibular input based on attitude

--no proprioceptive feedback based on viewing direction

--limited spatial orientation

--no direct feedback (i.e., feedback delayed) in response to control inputs

--no auditory input

--limited resolution of camera images

--limited geometrical field of view

--zoomed-in camera image

--few points of reference at sea

--limited image update rate

Possible consequences for human performance include poor tracking; difficulty in 

judging camera, platform, and target motion; confusion about direction of platform flight;

confusion about viewing direction of camera; disorientation; degraded situation 

awareness.

Experiments

Experiment 1

Examined the benefits of synthetic visual motion in guiding payload camera. A

computer-generated grid of perpendicular lines was overlaid on camera image, and
Human Factors of UAVs 50

moved in response to camera inputs. In the first experiment, subjects had to track a

moving ship with a simulated UAV sensor camera. Performance was improved by

synthetic image augmentation, and benefits were largest when the update frequency of

the camera was low. In a second experiment, subjects saw a target ship, then had to point

camera at after a 15 s delay that included several translations and rotations of the MUAV.

Again, performance was improved by the synthetic image overlay.

Experiment 2

Asked whether a computer-synthesized world embedding the camera image (called an

ecological display, based on the notion that visual cues provided by embedding world are

directly perceived) can aid in guiding camera. Subjects had to search for target ships with

camera. Performance with ecological display was compared to performance with

heading/pitch indicators adjacent to camera image. Such indicators require cognitive

inference, in contrast to ecological display. Ecological display reduced search time and

total number of camera motions. Indicators did not significantly improve performance

relative to baseline.

Experiment 3

Asked whether an ecological display can allow an operator to control UAV airframe and 

camera simultaneously. The task required the subject to track a target ship while circling

it. Four display types were used: two without augmentation (north up & track up), and 

two with augmentation (2D synthetic world and a 3D synthetic world). Data showed that

augmented displays aided airframe control without degrading tracking. Augmented

displays also allowed effective manual control with high airframe speeds.

Experiment 4

Experiment examined manual control of sensor under conditions of low update rates and 

delayed visual feedback, and measured the benefits of a predictive camera footprint. Data

showed that update rates below 2 Hz and delays longer than 2 seconds degraded tracking 

performance. Predictor display eliminated costs of slow update rate and time delay except

at the most extreme values.

Experiment 5

Examined the effects of head-slaved camera control, time delays, and advanced interface

design on situational awareness. The authors speculate that proprioceptive feedback from

head-slaved control may aid SA. However, helmet-mounted displays might be

uncomfortable, and transmission delays could make the perception of spatial information

difficult. In the experiment, HMD was compared to head-slaved dome projection. To

overcome the problems of delayed image transmission, a method of compensation called 

delay handling was introduced. Delay handling preserves spatial relationships between 

input images by presenting them in the viewing direction of the camera at the moment

image was recorded, rather than the moment at which image transmission is received.

Data indicate that delay handling improves SA. No benefit was found for dome

projection relative to helmet-mounted display.

Experiment 6
Human Factors of UAVs 51

Compared head-coupled control/helmet-mounted displays to manual control of camera.

Subjects had to locate five target ships as quickly as possible. In manual control

condition, imagery was projected on a dome, so that proprioceptive info was available in

both conditions. Head-slaved camera control increased search speed but enlarged the

search path as compared to manual control.

46) Veltman, J.A., & Oving, A.B. (2002). Augmenting camera images for

operators of unmanned aerial vehicles. RTO Meeting Proceedings (RTO-MO-088).

UAVs flight path is often pre-programmed, but camera must still be steered manually.

This can be difficult because of low camera update rates and communication time delays

between GCS and vehicle. One method of addressing these difficulties is to provide

current and predictive camera view footprints on a 2D map. This provides motion 

feedback when camera moves, along with information preview of where the camera will

be shifting. The authors note that a 2D map provides primarily exocentric (authors use

the term "local") spatial information, while a 3D map provides egocentric ("global") info.

Authors speculate that providing a predictive camera footprint within a 3D map might

therefore improve camera steering performance beyond that observed with a 2D map.

The goal of the experiment was to test this speculation.

Subjects flew a simulated UAV recon mission which required them to search for military

vehicles along roads and edges of woods. Two side-by-side displays were used. On the

left was a 2D map which presented waypoints and route plan; flight direction; and actual

and predicted camera footprints. On the right (in some conditions) was a 3D map which 

presented an immersed view from vantage point of UAV camera, along with actual and 

predicted footprints. In experimental conditions, subjects were provided the 3D display in

addition to the 2D map. In control conditions, only the 2D map was provided. The

camera image was presented in lower right display. Camera image quality had three

levels: normal, 3 Hz update rate, 1 second delay. In some conditions, subjects also 

performed a secondary monitoring/memory task.

Results

When camera quality was normal, 3D camera produced a small increase in the percentage

of roads and wood edges that were inspected (~35% vs. ~40%). When camera image

quality was degraded, the benefits of the 3D map were larger (~20% vs. ~30%).

Secondary task performance was better with 3D map, suggesting that map produced 

spare mental capacity, and subjective workload ratings were lower with 3D camera than 

without. EOGs indicated that subjects inspected 3D map frequently, especially when 

camera quality was low.
Human Factors of UAVs 52

47) Walters, B.A., Huber, S., French, J., & Barnes, M.J. (2002). Using simulation

models to analyze the effects of crew size and crew fatigue on the control of tactical

unmanned aerial vehicles (TUAVs) (ARL-CR-0483). Aberdeen Proving Ground, MD:

Army Research Laboratory.

A study used simulation modeling to determine how fatigue, crew size, and rotation

schedule affect operator workload and performance on a TUAV control task. Simulations

were conducted using MicroSaint modeling architecture, from Micro Analysis and 

Design. 18 subject matter experts (SMEs) provided A) a list of tasks that are involved in 

controlling a TUAV during normal operations and during emergencies, B) the order in

which the tasks are performed, C) the visual, auditory, cognitive, and psychomotor

workload imposed by tasks, D) the types of emergencies that can occur during missions,

and E) the probabilities of mishaps occurring during emergencies when soldiers are

fatigued.

The fatigue algorithm used by simulation predicts human response capability for tasks

over an extended period of sleep deprivation. The focus of algorithm is the interaction of

sleep deprivation with circadian rhythms.

The model used simulates the tactical operations center (TOC) and launch/recover station 

(LRS) (including mission commander [MC], aerial vehicle operator [AVO], and mission 

payload operator [MPO] duties), and several functions: launch, transfer, recovery,

mission support, emplacement, displacement, emergencies, mishaps, and maintenance

during emplacement. The model was used to simulate 12- and 18-hr missions over a 24-

hr period under 15 different conditions for three consecutive days. During the missions,

there were times with 2 UAVs in-flight: one observing the targets, and one flying to 

assume control of search. Soldiers were modeled to work 2-, 3-, 4-, or 6-hr rotation 

schedules. The model does not simulate soldier activity in between shifts.

Models simulate one move (jump) per day for the TOC and one move every other day for

the LRS. Each move comprises a 1/2 break-down, 1/2-hr move, and a 1-hr setup. The

TUAV spends 5 hrs of simulation time in the air: 4 hrs of surveillance and 1 hr in transit

to/from destination. The output of model includes performance times, target detection

rates, and AV mishaps under each simulated condition. Several crew configurations

(different numbers of MCs and AVOs/MPOs) were tested.

Some conditions that can affect a TUAV mission include

--type of search: area search, person search, airfield, tanks, building, road search, bridge,

missile site, command post, air defense artillery, check points, battle damage assessment

on SAM, artillery search

--emergencies: icing, generator failure, signal degradation or intermittent link loss,

payload failure, AVO or MPO console failure, GPS failure

--weather: humidity, sun, gusting winds, crosswinds, flat clouds, ragged clouds

--terrain: high vegetation, desert (sand), high desert, city, town, village
Human Factors of UAVs 53

Workload estimates were obtained from SMEs using a scale developed to be compatible

with Wickens' (1984) multiple resources theory of attention. Four resource pools were

assumed: visual, auditory, cognitive, and psychomotor.

The model simulates 5 TUAV launches per day for an 18-hr mission. Each launch, three

types of target search were performed. Missions were repeated every day for 3 days for

each crew rotation schedule.

Results

Decreasing crew size decreased target hit rates and increased target detection times.

Workload estimates suggested that when there was no MC in the LRS, the TOC MC was

interrupted ~50% of the time to perform tasks that the LRS MC would otherwise have

performed. When there was 1 MC in the LRS, the TOC MC was interrupted ~20% of the

time with LRS MC tasks. Adding a third AVO to the LRS (compared to baseline

condition of 2 AVOs) did not improve performance.

The model was adjusted to simulate 12-hr mission profiles with and without 1-hr gaps

between flights. Three launches were simulated per day instead of 5. Results were similar

to those from 18-hr mission conditions. No performance differences were produced by 1-

hr gaps between missions.

48) Weeks, J.L. (2000). Unmanned aerial vehicle operator qualifications (AFRLHE-AZ-TR-2000-0002). Mesa, AZ: Air Force Research Laboratory, Warfighter

Training Research Division.

Report compares selection criteria for UAV operators across branches of the U.S.

military and British army.

Pioneer, USNL EP candidates go through a 24-week training course. Payload operator

and AVO compete different 8-week courses. Mission commander has to be a flight

officer. Health conditions related to hypoxia or pressure changes are not disqualifying.

Health standards include corrected visual acuity of 20/20 in each eye, normal color

vision, normal hearing, clear & distinct speech, and voice well modulated. EP requires

normal depth perception.

Pioneer, USMC AVO and PO complete the same 8-week training course. Candidates for

EP have to demonstrate satisfactory as AVO or PO, demonstrating good 3-D

cognition/perception, then complete a 19-week training course. MC has to be an aviation 

officer. Physical standards are the same as for UAN UAV operators.

Hunter, USA AVO and PO have to complete a 23-week training course. Candidates for

EP have to demonstrates satisfactory performance as AVO or PO, and are screened by 
Human Factors of UAVs 54

interview and by performance using a radio-controlled model airplane. If selected, they

must complete a 16-week training course. The AVO and PO are required to pass a class

IV flight physical, which includes requirements for medium physical demands, a normal

physical profile, and normal color vision. The EP is required to pass a class III physical,

similar to that required for air traffic controllers.

Phoenix, British Army AVO is required to take a 3-week course. Flight crews are not

required to take physicals.

Predator, USAF AVO candidate has to be a pilot of a fixed-wing aircraft or a navigator

with FAA instrument rated commercial license. Beyond undergraduate flight training,

follow-on training, then 9 weeks of Predator basic training. DEMPC and SO complete 24 

weeks of initial-skills training as an Imagery Interpretation Apprentice, then 9 weeks of

Predator basic training. AVO has to pass a Class I physical. DEMPC and SO have to pass

a Class III flight physical, but with visual acuity and depth perception standards

equivalent to Class I standards.

49) Wickens, C.D., & Dixon, S. (2002). Workload demands of remotely piloted

vehicle supervision and control: (1) Single vehicle performance (Technical report

AHFD-02-10/MAD-02-1). Savoy, IL: University of Illinois, Institute of Aviation,

Aviation Human Factors Division.

Examined the benefits of offloading tasks from visual channel in a single-UAV control

task, and compared the results to the predictions of single-channel theory (SCT), singleresource theory (SRT), and multiple-resource theory (MRT) of attention. Subjects each 

flew a series of missions involving three tasks: mission completion (flight path tracking),

inspecting command targets (CTs) / searching for targets of opportunity (TOOs), and 

monitoring system gauges for system failures (SFs) (i.e., out of bounds values). Flight

instructions (fly-to coordinates of next target and a question about the target to be

answered) were provided during the task. Instructions could be refreshed with a button 

press. In baseline condition, pilots flew with all manual flight controls and all visual

displays. In auditory condition, SF alerts and flight instructions were provided aurally. In

an automation condition, flight control was offloaded to an automated system. All

alerts/instructions/automation were perfectly reliable.

Results

Flight path tracking was unaffected by auditory offloading.

Button-press refreshes of instructions were reduced in both the auditory offloading and

the automation conditions. In auditory condition, this might have been because aural

presentation reduced visual conflict during time that instructions were first presented. In 

automation condition, it may have been because the subject was not required to 

remember target coordinates.
Human Factors of UAVs 55

TOO detection rates improved under autopilot flight control both for single and dualUAV tasks. This was in part due to the fact that the autopilot flew directly over each 

target, while the human operator sometimes did not. However, an autopilot benefit was

evident even restricting analysis to those trials on which the TOO appeared in the 3D

display. This suggests a role for the autopilot in improving cognitive/attentional

performance in TOO detection.

Auditory alerts significantly improved SF detection rates and SF response times

compared to baseline, except in cases where the subject was loitering/inspecting a target

at time of SF occurrence. This suggests that difficult image interpretation produced 

cognitive tunneling. Automation condition also improved SF detection rates (though not

under all circumstances), but did not affect SF response times.

50) Wickens, C.D., Dixon, S., & Chang, D. (2003). Using interference models to 

predict performance in a multiple-task UAV environment-2 UAVs (Technical report

AHFD-03-09/MAAD-03-1). Savoy, IL: University of Illinois, Institute of Aviation,

Aviation Human Factors Division.

Examined the benefits of task offloading to pilots performing single- and multiple-UAV

tasks and compared the results to predictions of single-channel attention theory (SCT),

single-resource theory (SRT), and multiple-resource theory (MRT) of attention. Subjects

each flew a series of missions involving three tasks: mission completion (flight path

tracking), inspecting command targets (CTs) / searching for targets of opportunity

(TOOs), and monitoring system gauges for system failures (SFs). Flight instructions (flyto coordinates of next target & a question about the target to be answered) were provided 

during the task. Instructions could be refreshed with a button press. In the baseline

condition, pilots flew with all manual flight controls and all visual displays. In the

auditory condition, SF alerts and flight instructions were provided aurally. In the

automation condition, flight control was offloaded to an automated system. All

alerts/instructions/automation were perfectly reliable.

Results

Flight path tracking was unaffected by auditory offloading. Button-press refreshes of

flight instructions were reduced in both the auditory offloading and the automation 

conditions, suggesting that these conditions freed up processing resources. The number of

refreshes was higher in dual-UAV conditions, but the effect was primarily in the baseline

& auditory offloading conditions, not in the automation condition. TOO detection rates

improved under autopilot flight control both for single and dual-UAV tasks. This was in 

part due to the fact that the autopilot flew directly over each target, while the human 

operator sometimes did not. However, an autopilot benefit was evident even restricting 

analysis to those trials on which the TOO appeared in the 3D display. This suggests a role

for autopilot in improving cognitive/attentional performance in TOO detection.
Human Factors of UAVs 56

Auditory alerts improved SF detection rates and reduced detection times. Autopilot had 

no effect. Dual-UAV costs to SF detection time obtained in the baseline and autopilot

conditions, but not in the auditory alert condition. SF detection times were longer for

faults that occurred during target inspection than for faults that occurred under normal

flight, lending some support to CST and SRT. However, this effect was mitigated 

somewhat by auditory alerts, consistent with MRT.

51) Williams, K.W. (2004). A summary of unmanned aircraft accident/incident

data: Human factors implications.

Examines military UAV accident/incident data for various UAV systems.

Army

Hunter

Hunter takes off & lands using an external pilot (EP) in visual contact using controller

similar to that used for remote controlled hobby planes. After takeoff and climb, internal

pilot (IP) assumes controls from GCS. The IP controls the aircraft using knobs to select

altitude, heading, & airspeed. 47% of accidents were HF related. The largest percentage

(47%) of HF issues arose during landing. An additional 20% arise during takeoff. Control

difficulties are caused in part by the need for the operator to reverse control inputs when 

aircraft is headed toward him/her. Other problems include:

--pilot-in-command issues

--failure of alerts/alarms to inform operator of non-normal conditions

--mode display errors

--crew failure to follow proper procedure

Shadow

The Shadow uses a launcher for takeoff and an automated system, the tactical automated 

landing system (TALS), for recovery. Landing generally does not require intervention

from the operator in the GCS. In flight, the aircraft is controlled through a menu-based 

interface that allows operator to select altitude, heading, & airspeed. During landing,

operator in GCS has no visual contact with aircraft, and receives no data from onboard

sensors. An external observer is required to communicate to the operator when the craft

has touched down, at which time the operator gives a command to stop the engine. HF

errors were less frequent with Shadow than with Hunter.

Navy

Pioneer

The Pioneer requires an EP for takeoff & landing. After takeoff, IP controls the vehicle

from the GCS in one of three modes: autonomously using preprogrammed waypoint

coordinates; semi-autonomously using airspeed, altitude and heading values specified 

with rotary knobs; manually, with a joystick. There are plans to implement an automated
Human Factors of UAVs 57

system for ship-based landings. 28% of accidents were HF related. Of these, 68%

occurred during landing and 10% during takeoff. An additional 13% were due to aircrew

coordination lapses (procedural & communication errors) and 10% were weather related

errors resulting from errors in pilot decision making.

Fire Scout

A vertical takeoff & landing vehicle, the Fire Scout was involved in one accident.

Antenna was damaged during ground handling (human error), causing incorrect altimeter

reading when vehicle was airborne.

Air Force

Predator

The Predator is flown from a GCS using a joystick and rudder pedals and a forward

looking camera with a 30-deg FOV. The camera is also used for takeoffs and landings.

Human factors lapses contributed to 67% of accidents. A majority of these (75%) were

procedural errors, including a failure to follow checklist steps during handoff between 

crews and an accidental activation of a program that erased the aircraft computer's

internal RAM.

Interface issues are discussed in 89% of Predator accidents, and are cited as a

contributing factor in 44%. Four categories of interface issues: design of HUD; design of

HDD; alerts and alarms; functioning of the autopilot.

--HUD problems: FOV (30 degs) is too narrow; attitude indicator is inadequate; RPM

indicator needs improvement; symbology obscured during low-link conditions;

symbology contrast too low; symbology inadequate.

--HDD problems: too many levels to maneuver through to reach needed info; info display 

unintuitive; critical commands unprotected or unemphasized; operational value ranges

inconsistent within display.

--Alerts/alarms problems: do not capture attention; audio warnings insufficient or absent;

info provided inadequate or poorly prioritized; info provided invalid; data that need to be

compared not always collocated on same display page.

--Autopilot problems: no indication of autopilot status on HUD; flight controls are

disabled while autopilot is engaged (i.e., no override capability) and four separate menus

have to be traversed in order to deactivate autopilot (requires about M = 7 seconds);

autopilot tends to command extreme measures and overstress aircraft; autopilot

functionality does not conform to AF standards.

Global Hawk

Global Hawk is the most fully automated of UAV systems. All phases of flight are

automated, including takeoff & landing. The crew's task is to monitor the aircraft and 

control payload. This makes flying the relatively easy, but makes mission planning 

exceedingly difficult. Mission planning process begins up to 270 days prior to mission.

Once the target set is finalized, 3-5 weeks are required to write and validate mission plan.

Of three accident reports available for Global Hawk, only one involved HF issues.

Aircraft was forced to perform an emergency landing at a preprogrammed alternate

airport. At point of airport, a taxi speed of 155 knots had been set due to software bug 
Human Factors of UAVs 58

during preprogramming. When aircraft was commanded to begin taxiing for takeoff, it

reached a speed where it was unable to turn at appropriate point and ran off the runway.

Fundamental HF problem with the Global Hawk is that the system does not encourage

close monitoring by operators, resulting in reduced SA. An additional problem is that

status reports are provided in hexadecimal and do not include trend data.

52) Wilson, G.F., & Russell, C.A. (2004). Psychophysiologically determined

adaptive aiding in a simulated UCAV task. Proceedings of the Second Human

Performance, Situation Awareness, and Automation Conference (HPSAA II), Daytona

Beach, FL, March 22-25.

An experiment tested the benefits of adaptive aiding based on psychophysiological

assessment of operator workload. The task required subjects to monitor 4 vehicles flying 

preplanned routes. When vehicles reached designated points, radar images of target area

were presented to subject. Subject searched target area then selected targets for bombing.

The search/target designation task was conducted under time stress. The subjects chose

the order in which images from the vehicles were presented. Images were presented at

two levels of difficulty. The more difficult level included more distractors and required

more difficult decisions regarding target priority.

Subjects were also required to monitor vehicles for potential emergencies (e.g., loss of

communication). Memory load was manipulated by having subject hold up to 4 

aircraft/problem combinations simultaneously until a command was given specifying

which problem to address.

EEG, ECG, and EOG data were recorded. An artificial neural network was trained to

recognize periods of low and high task difficulty using these data. During criterion task 

performance, three levels of adaptive aiding were used: 1) no aiding, 2) aiding during

times of high workload, and 3) random aiding. Aiding involved decreasing velocity of

vehicle so that time stress was reduced. Subjective workload was measured with NASATLX.

Results

The neural net was 70% accurate at classifying high/low task difficulty levels during task 

performance. For all conditions, the number of correctly selected targets was lower when 

the task was difficult. The number of designated points of impact was lower for the

difficult task level in the unaided and the randomly-aided conditions, but was unaffected 

by task difficulty in the adaptively-aided condition. Similarly, the number of missed 

weapons releases was higher in the difficult level for the unaided and randomly-aided 

conditions but was unaffected by difficulty in the adaptively aided condition. Differences

in subjective workload were marginal and inconsistent.
Human Factors of UAVs 59

Appendix B

Research Matrix

This appendix provides a cross-index of the research issues discussed in the main body of

the text with the research literature described in Appendix A. Only those articles deemed 

directly relevant to each question are included. Bold-faced italics indicate research 

articles that present empirical data.

1. To what extent should en route flight control be automated?

Relevant articles: 2, 7, 19, 25, 32, 33, 38, 40, 49, 50, 51, 52

2. What are the consequences of degraded reliability of automated UAV functions

for performance of the automated task and of concurrent tasks?

Relevant articles: 11, 12, 40

3. How will see and avoid requirements be addressed in UAV flight? Can automated

detect, see, and avoid (DSA) technology allow a UAV operator to maintain

acceptable levels of separation? What are the consequences of imperfectly reliable

DSA automation on conflict detection and on performance of concurrent tasks?

Relevant articles: 27

4. To what extent should takeoff and landing be automated?

Relevant articles: 2, 51

5. Through what form of control interface should internal and external pilots

manipulate a UAV?

Relevant articles: 37, 51

6. What compromises should be adopted between spatial resolution, temporal

resolution, time delay, and field-of-view (FOV) in the display of visual imagery for

flight control and/or conflict detection?

Relevant articles: 10, 32, 44, 45, 46, 51

7. Can augmented reality displays or synthetic vision systems successfully 

compensate for the degrade visual imagery provided by onboard sensors?

Relevant articles: 3, 13, 15, 16, 17, 18, 29, 44, 45, 46

8. Can multimodal display technology be used to compensate for the dearth of

sensory information available to a UAV operator?

Relevant articles: 4, 5, 11, 12, 14, 19, 20, 25, 32, 34, 39, 43, 44, 49, 50, 51

9. To what extent can displays and controls be standardized across UAV systems? 

What level of standardization should be mandated? (Basic T instrument panel? 

HUD overlay?)

Relevant articles: 21
Human Factors of UAVs 60

10. What are the consequences for system safety of pilot judgment when the pilot no 

longer has a “shared fate” with the vehicle? Will there be subtle shifts in risk taking 

that might affect overall airspace safety?

Relevant articles: none

11. How will hand-offs between crews be accomplished during long-endurance

flights?

Relevant articles: none

12. What are the effects of variable total loop time delays on response to ATC

instructions?

Relevant articles: 19, 27, 32, 43, 44, 45

13. What form of predictable autonomous behavior should a UAV adopt following a

loss of ground-to-air communications?

Relevant articles: none

14. How many members will each crew comprise, and what will be each

crewmember’s responsibilities? Can an operator supervise multiple UAVs

simultaneously while maintaining an acceptable level of performance?

Relevant articles: 7, 8, 15, 21, 23, 28, 30, 33, 38, 47, 51, 52

15. What are the core knowledge, skills, and abilities (KSAs) that should be

required for UAV pilot certification? What KSAs should be required for

certification to fly particular UAV systems or classes of systems?

Relevant articles: 2, 26, 48

16. Should experience piloting a manned aircraft be prerequisite for UAV pilot

certification?

Relevant articles: 2, 42, 48

17. What medical qualifications should a UAV operator be required to meet?

Relevant articles: 25, 47, 48
Human Factors of UAVs 61

Appendix C

Contact Information

This appendix provides available contact information for first and/or senior authors on

the research articles summarized in Appendix A.

Ball, Jerry T.

Air Force Research Laboratory

6030 S. Kent St.

Mesa, AZ 85212

(480) 988-6561

Barnes, Michael J.

Army Research Laboratory

4656 S. Cherokee

Sierra Vista, AZ 85650

(520) 538-4702

Cooke, Nancy J.

Cognitive Engineering Research Institute

5865 S. Sossaman Rd.

Mesa, AZ 85212

(480) 727-1331

Cummings, Mary J.

Massachusetts Institute of Technology

77 Massachusetts Ave 33-305

Cambridge, MA 02139

(617) 253-4196

de Vries, Sjoerd

TNO Human Factors

P.O.Box 23

3769 ZG Soesterberg, The Netherlands

+31 346 356 300

devries@tm.tno.nl

Draper, Mark H.

Air Force Research Laboratory/HEC

2255 H St.

Wright Patterson AFB, OH 45433

(937) 255-5779

Gluck, Kevin

Air Force Research Laboratory
Human Factors of UAVs 62

6030 South Kent St.

Mesa, AZ 85212

(480) 988-6561

kevin.gluck@mesa.afmc.af.mil

Goodrich, Michael A.

Brigham Young University

3361 TMCB, BYU

Provo, UT 84602

(801) 422-6468

mike@cs.byu.edu

Gugerty, Leo

Clemson University

Psychology Dept.

418 Brackett Hall

Clemson, SC 29634

gugerty@clemson.edu

Hancock, Peter A.

University of Central Florida

Partnership II

3100 Technology Pkwy, Suite 337

Orlando, FL 32826-0544 

(407) 823-2310

phancock@pegasus.cc.ucf.edu

Hansman, R. John

Massachusetts Institute of Technology

33-303 MIT

Cambridge, MA 02139

(617) 253-2271

rjhans@mit.edu

Martin, Elizabeth

Air Force Research Laboratory

6030 South Kent St.

Mesa, AZ 85212

(480) 988-6561

elizabeth.martin@mesa.afmc.af.mil

Mouloua, Mustapha

University of Central Florida

Phillips Hall 302M

Orlando, FL

(407) 823-2910 
Human Factors of UAVs 63

mouloua@pegasus.cc.ucf.edu

Ryder, Joan

CHI Systems

1035 Virgina Dr.

Fort Washington, PA 19002

(215) 542-1400

jrider@chisystems.com

Shively, Jay

U.S. Army/NASA Ames

MS-243-11

Moffett Field, CA 94035

(650) 604-6249

jshively@mail.arc.nasa.gov

Tvaryanas, Anthony P.

USAF

2602 Louis Bauer Dr.

Brooks-City-Base, TX 78235-5251

anthony.tvaryanas@brooks.af.mil

Weeks, Joseph L.

Air Force Research Laboratory

6030 S. Kent St

Mesa, AZ 85212-6061

Wickens, C.D.

University of Illinois

Institute of Aviation, Aviation Human Factors Division

#1 Airport Rd

Savoy, IL 61874

(217) 244-8617

cwickens@uiuc.edu

Williams, Kevin W.

FAA CAMI

5801 NW 31stTerrace

Oklahoma City, OK 73122

(409) 954-6843

kevin.williams@faa.gov