ARMY RESEARCH LABORATORY

ARL-TR-2984 July 2003

An Analysis of the Abilities Required by Future

RSTA Vehicle Commanders and Drivers

Bruce S. Sterling and Cheryl A. Burns

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Army Research Laboratory

Aberdeen Proving Ground, MD 21005-5425

____________________________________________________________________________

ARL-TR-2984 July 2003

____________________________________________________________________________

An Analysis of the Abilities Required by Future RSTA

Vehicle Commanders and Drivers

Bruce S. Sterling and Cheryl A. Burns

Human Research and Engineering Directorate

____________________________________________________________________________

Approved for public release; distribution is unlimited

____________________________________________________________________________
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Sterling, B.S.; Burns, C.A. (both of ARL)

An Analysis of the Abilities Required by Future RSTA Vehicle Commanders and Drivers

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U.S. Army Research Laboratory

Human Research & Engineering Directorate 

Aberdeen Proving Ground, MD 21005-5066

Abilities needed by future commanders and drivers of reconnaissance, surveillance, and target acquisition (RSTA) vehicles were 

examined in a soldier-in-the-loop simulation of a Future Combat System unit of action. We used the Job Assessment Software 

System to administer the abilities taxonomy developed by Fleishman and Quaintance (2000). We found that in the simulation 

used, (a) vehicle drivers needed a higher level of overall abilities than the vehicle commanders; (b) vehicle commanders’ jobs 

appeared to become easier with experience, while those of the drivers did not; and (c) conceptual, communications, and speedloaded abilities were the most critical abilities for vehicle commanders and drivers. We recommend changes in human factors 

engineering, training, and personnel selection to improve performance of RSTA vehicle commanders and drivers.

ability taxonomies FCS abilities and skills JASS

502-624-1964
iii

Contents

List of Figures iv

List of Tables iv

1. Introduction 1

1.1 Overview .............................................. 1

1.2 Objective Force Environment................................. 1

1.3 Human Performance Taxonomies .............................. 2

1.4 The Future Combat System Experiment .......................... 3

2. Method 6

2.1 Participants ............................................ 6

2.2 Instrument ............................................. 6

2.3 Procedure .............................................. 8

2.4 Analyses............................................... 8

3. Results 8

3.1 Position ............................................... 8

3.2 Administration .......................................... 9

3.3 Ability Clusters .......................................... 9

3.4 Comparison to Related Duties................................. 10

4. Discussion 11

5. Recommendations 13

6. References 15
iv

List of Figures

Figure 1. RSTA vehicle sensor suite.................................... 4

Figure 2. Interior of simulated RSTA vehicle.............................. 4

Figure 3. Cluster scores for RSTA and M1A2 vehicle commanders................ 10

Figure 4. Cluster percentages for RSTA and M1A2 vehicle drivers ................ 11

List of Tables

Table 1. Example of an ability rating scale............................... 3

Table 2. Selected demographics ...................................... 6

Table 3. JASS ability clusters and skills ................................. 7

Table 4. Ability cluster means and (standard deviations) by position and administration.. 8

Table 5. Overall cluster means by position and administration .................. 9

Table 6. Estimated overall ability cluster means ........................... 9
1

1. Introduction

1.1 Overview

The abilities required to maintain situation awareness as the commander or driver of a

reconnaissance, surveillance, and target acquisition (RSTA) vehicle were examined for future

Army operations as described in the Objective Force concept. Situation awareness or situation

understanding (SU), as situation awareness is referred to by the Army, is key to the Objective

Force concept (Department of the Army, 2002). Situation awareness is the perception of

relevant objects in the environment, comprehension of the overall situation, and projection of

what will happen in the near future (Endsley and Garland, 2000). This research was conducted

because reconnaissance tasks in the Objective Force will be much different than current

reconnaissance tasks, particularly at the vehicle commander and driver level. Reconnaissance

tasks will involve much more sensor management and use of displays, compared to current

scouting tasks. As such, different abilities may be involved or at least differences in importance

among the current abilities used. We wanted to examine abilities that are important for future

RSTA vehicle commanders and drivers, to ascertain if those abilities are the same for both

positions, and if the amount of those abilities needed changes with training. These questions are

important, since the Army needs to design equipment and select and train personnel for the

abilities needed in the Objective Force environment. The report begins with a brief overview of

the Objective Force environment and how the RSTA vehicle crew is critical to this environment.

We then discuss the area of human performance taxonomies as a method to determine the ability

requirements for jobs. Next, we review the experiment in which we participated, the data

collection method, and results. Finally, we present conclusions and recommendations related to

changes in human factors engineering, training, and selection, which could enhance performance

concerning the abilities required for RSTA commanders and drivers.

1.2 Objective Force Environment

The Objective Force will be a strategically deployable, tactically superior and sustainable force

that will provide a quick reaction capability for a continuum of conflicts that arise in the 21st

century. The Objective Force is envisioned to be a mixture of manned and unmanned combat

systems. This force will incorporate and exploit information dominance to develop a common,

relevant operating picture and achieve SU to dominate the battlespace. The Future Combat

Systems (FCS) that comprise the Objective Force will have to be relatively light (20 tons) and

small in order to be deployed quickly and sustained efficiently.

The Objective Force’s success in combat will depend on soldiers’ seeing and killing the enemy

at a distance, as opposed to closing with and destroying the enemy. In order to detect and engage

the enemy at a distance, extensive use of sensor-equipped aerial or ground robotic vehicles will
2

be needed. Personnel will use operational control units (OCUs) to control robotic vehicles, and

by doing so, will be able to detect, classify, recognize, identify, and engage the enemy and report

battle damage and maintain SU. In addition to the OCU operator, someone will need to drive

and position the vehicle and act as gunner to defend the vehicle from unanticipated, close range

threats.

The enhanced sensor and network-centric information gathering capabilities of the Objective

Force are focused on maintaining SU. SU is the basis upon which individuals and commanders

make decisions and adapt to changes in the environment. Thus, without the ability of soldiers to

maintain SU, the sophisticated sensor and weapons systems of the Objective Force are of

substantially less value.

1.3 Human Performance Taxonomies

In order for the Army to select, assign, train, and equip RSTA vehicle commanders and drivers,

it is necessary to determine what abilities are used to perform these jobs. Fleishman and

Quaintance (2000) describe a variety of taxonomies of human performance or ways to categorize

human performance capabilities. One approach is called “ability requirements.” Under this

approach, tasks are described in terms of the abilities necessary to perform them and tests are

used to assess the required abilities. These tests are then factor analyzed to identify common

factors among groups of tests. These common factors are interpreted as abilities according to the

factor loadings of the tests on the factors. Numerous physical and mental abilities have been

documented with this approach. Research, described by Fleishman and Quaintance (2000), has

established statistically significant relationships between abilities identified by factor analysis

and learning and performance of a job.

Another use of abilities taxonomies is to determine the abilities necessary to perform specific

jobs. First, a description of an ability and examples of various levels of that ability are

developed (see Table 1). Then, job incumbents and experts are asked to rate how much of that

ability is necessary to perform the job being rated. Research described by Fleishman and

Quaintance (2000) demonstrates good levels of agreement (correlations in the .60 to .80 range)

between ratings by job incumbents and experts of abilities required by a specific job. This

method of ability rating is used in the research described in this report.
3

Table 1. Example of an ability rating scale

 Written comprehension: The ability to understand written words, sentences, and paragraphs

 How much of this ability is needed to perform the job?

Understand an instruction book about

repairing a missile instrument system

7 A great amount of this ability is needed

6

5 Quite a bit of this ability is needed

Understand an apartment lease 4

3 A moderate amount of this ability is needed

2

Read the words on a roadmap 1 A minimum amount of this ability is needed

1.4 The Future Combat System Experiment

The Army will, in partnership with the Defense Advanced Research Projects Agency, develop

Objective Force systems concepts, perform experiments to validate and refine those concepts,

and conduct technology demonstrations. This report addresses data collected during an FCS

experiment on command, control, communications, computer, intelligence, surveillance, and

reconnaissance. A range of scenarios was used in the experiment run by the Institute for Defense

Analysis (IDA). Although the U.S. Army Research Laboratory collected the data described in

this report, it was not in charge of the experiment. The RSTA vehicles in this experiment were

virtual simulations in the Mounted Maneuver Battlespace Lab at Fort Knox, Kentucky.

In this experiment, there were two manned cells (a company-sized element), with six RSTA

vehicles each (see Figures 1 and 2). The RSTA vehicles each possessed several robotic ground

and aerial sensors that could be controlled through the OCU. These RSTA vehicles were the

primary means of target acquisition for the cells, although the cells had some other sensor

capabilities that belonged to the cell, as well as weapon systems to engage the targets at long

ranges. The two manned cells, plus four unmanned (constructive) cells, which were each

controlled by one person, composed the unit of action (a battalion-sized entity in this research).

The unit of action provided overall command and control, possessed additional organic sensors

and long-range weapons, and provided access to higher level sensors (e.g., satellites) and weapon

platforms (e.g., B-2 bombers). A central European terrain base was used to simulate a variety of

traditional and asymmetric scenarios. Peacekeeping and disaster relief were not simulated.
4

PTN = paint the night

JSAF PVD = joint semi-automated forces plan view display

BCV = battle command vehicle

SOV = staff operations vehicle

OTB = one semi-automated forces test bed

NLV PTNL = night vision lab paint the night light

MAH UAV = medium altitude helicopter unattended aerial vehicle

Figure 1. RSTA vehicle sensor suite.

ACRT = advanced concept research tool

OTW = out-the-window view

VSRG = virtual reality signal generator

Figure 2. Interior of simulated RSTA vehicle.
5

Crew tasks were divided between the vehicle commander and the driver. The vehicle

commander operated the OCU and directed the driver in positioning the vehicle. Route planning

was impromptu. In general, the vehicle commander received a request for information about a

certain part of the area of operation from the cell level. The commander then directed the driver

to move to a certain location (if applicable) and deploy the sensors. The commander had a

computer workstation that functioned as an OCU. Primarily using a mouse, the commander

could navigate through a menu to deploy ground and aerial sensors, design routes for these

sensors, change direction or stop them en route, or even give control of vehicle sensors to

another commander or take control of another vehicle’s sensors (provided the other commander

relinquished control). Any entities detected by these sensors automatically appeared on the OCU

screen. Different sensors could provide different levels of target acquisition from detection

(something man-made) to classification (e.g., wheeled versus tracked vehicle), to recognition

(e.g., enemy versus friendly) to identification (BMP 90). The commander could then transfer

targets to the cell for engagement or could engage with one of the weapon systems under his

control (non-line of sight mortar). In addition to the OCU, the commander had access to a

command and control computer called a simulated command, control, communications, and

computer (SC4), which provided a “common operating picture” based on information about a

variety of entities provided by a wide variety of sensors, over the entire battlespace. Some of

these sensors were controlled by the cell, and others were not. Another important aspect of the

vehicle commander’s job was to communicate with the driver, other vehicle commanders, or cell

personnel via a headset.

The driver of the simulated RSTA vehicle had typical vehicle controls (gear shift, accelerator,

brake, steering wheel) as well as several outside views of the vehicle. The driver also had a 30-

millimeter gun for defense of the vehicle. The gun was (conceptually) on the vehicle turret and

was controlled by a screen in front of the driver. In addition, the driver had the same SC4 and

headset capabilities as the commander. The driver could also switch modes and control robots if

the commander needed assistance, although he had to look across the vehicle to view the OCU

screen in order to do so. No written job descriptions were provided for either crew member.

The experiment was conducted from 17 September 2001 to 18 January 2002 in four sets of

2-week trials. Each trial occurred 3 weeks apart. The first trial (17 September to 5 October) was

devoted to familiarization with the Objective Force environment and individual skills training.

For example, crew members learned how to use the OCU software and how to drive the RSTA

vehicle. The second trial (29 October to 9 November) concentrated on collective skills training.

Crew members learned how to function as an OCU operator in a cell and how to function as a

commander-driver team. The second trial culminated with a pilot scenario. The third (3 to

14 December) and fourth trials (7 to 18 January) involved operational Objective Force scenarios.

Scenarios involved attack and defend missions in a central European area and were fought

against opposing force personnel with semi-automated computer forces. Missions lasted about

8 to 12 hours.
6

2. Method

2.1 Participants

Participants were 12 RSTA vehicle commanders and 11 drivers who participated in trials 2 and 3

of the Objective Force experiment described before. One driver did not complete the second

administration of the survey. All but two of these individuals (one commander and one driver)

were from National Guard (NG) units. The NG participants lived in the Fort Knox area and had

participated in other Battle Lab experiments. The active duty participants worked for IDA.

Selected demographics are presented in Table 2.

Table 2. Selected demographics

Rank Vehicle

Commanders

Drivers

E6 3 0

E5 3 3

E4 6 7

E3 0 1

Age

< 20 0 1

20 to 25 3 3

26 to 30 4 4

31 to 35 2 1

> 35 3 2

Civilian Education

non-high school graduate 0 1

high school graduate 3 5

some college 5 5

college graduate 4 0

Time in Guard

not applicable 1 1

< 1 year 3 2

1 to 2 years 3 4

> 2 to 5 years 1 3

> 5 to 10 years 4 1

2.2 Instrument

The particular human abilities taxonomy used, shown in Table 3, was based on one developed by

Fleishman and Quaintance (2000) and modified by Knapp and Tillman (1998). This taxonomy

has two major groups of ability clusters (cognitive skills and experience and perceptual motor).

Each of these main groups contains four ability clusters (as in Table 3). The ability clusters in

turn contain from three to ten specific skills. There were 50 specific skills in all. The taxonomy
7

was administered via a computer program called the Job Assessment Software System (JASS).

Individuals were presented a series of “yes/no” questions that asked which specific skills were

necessary to perform a job. For example, in order to perform the job, is it necessary that the

person know and use language? If an individual indicated that a skill was not used, the computer

program did not present that skill to be rated, and the skill was given a 0 rating. If an individual

indicated that a skill was used, individuals then rated the relevant skill on a scale similar to the

one in Figure 1. Individuals could move a cursor to set the level of ability needed on a 1 to 7

scale. In this experiment, all participants addressed the task of “maintain situational

understanding.” This was defined as “being aware of the location of your own units, enemy

units, and the commander’s intent in order to perform the mission”.

JASS has been used to determine abilities required for a variety of jobs such as home health care

professionals (Knapp and Tillman 1998) and M1A2 System Enhanced Package (SEP) crew

members (Gill et al., 1999). For the M1A2 SEP, results showed that skill demands in the area of

maintaining SU were reduced by additional digital capabilities of the M1A2’s inter-vehicular

information system.

Table 3. JASS ability clusters and skills

Cognitive Skill and Experience Clusters

Communications Conceptual Reasoning Speed-Loaded

1. Oral comprehension 5. Memorization 13. Inductive reasoning 19. Time sharing

2. Written comprehension 6. Problem sensitivity 14. Category flexibility 20. Speed of closure

3. Oral expression 7. Originality 15. Deductive reasoning 21. Perceptual speed/

accuracy

4. Written expression 8. Fluency of ideas 16. Information ordering 22. Reaction time

9. Flexibility of closure 17. Mathematical reasoning 23. Choice reaction time

10. Selective attention 18. Number facility

11. Spatial orientation

12. Visualization

Perceptual-Motor Ability Clusters

Vision Audition Psychomotor Gross Motor

24. Near vision 31. General hearing 35. Rate control 41. Extent flexibility

25. Far vision 32. Auditory attention 36. Wrist-finger speed 42. Dynamic flexibility

26. Night vision 33. Sound localization 37. Finger dexterity 43. Speed of limb

movement

27. Visual color

discrimination 38. Manual dexterity 44. Gross bodyequilibrium

28. Peripheral vision 39. Arm-hand steadiness 45. Gross body

coordination

29. Depth perception 40. Multi-limb

coordination 46. Static strength

30. Glare sensitivity 47. Explosive strength

48. Dynamic strength

49. Trunk strength

50. Stamina
8

2.3 Procedure

Participants completed the JASS after they completed individual and collective training on trial 2

and a second time during the first week of the third trial. The second administration of the JASS

was originally scheduled for the second week of trial 3 but was given earlier at the request of the

experiment leader. After completing the day’s mission, participants completed the JASS on

stand-alone computers in a briefing area. Vehicle commanders rated abilities requirements on

seven tasks, and drivers rated abilities requirements on two tasks. The data in this report are a

comparison of ratings on the only task rated in common: maintain situational understanding.

This task was the first task rated by vehicle commanders and drivers.

2.4 Analyses

The data were analyzed with a 2 x 2 x 8 between- and within-subjects’ analysis of variance

(ANOVA) factorial design. The between-subjects factor was position (vehicle commander or

driver). The two within-subjects factors were administration (first versus second) and ability

clusters (one through eight, as in Table 3). The alpha level chosen for statistical significance was

p < .05. 

3. Results

Table 4 presents ability cluster means and standard deviations broken down by position and

administration.

Table 4. Ability cluster means and (standard deviations) by position and administration

Cluster

First

Administration

Vehicle

Commander

First

Administration

Driver

Second

Administration

Vehicle

Commander

Second

Administration

Driver

Conceptual 3.57 (1.42) 4.04 (1.60) 2.76 (1.70) 4.18 (1.24)

Communication 3.33 (2.07) 3.35 (1.91) 3.09 (2.02) 4.33 (.96)

Reasoning 2.56 (2.19) 3.33 (1.55) 1.04 (1.16) 2.81 (1.80)

Speed loaded 3.13 (1.60) 3.50 (1.57) 2.02 (1.28) 3.68 (.90)

Audition 2.34 (2.25) 3.05 (2.16) 1.36 (1.92) 3.17 (2.11)

Vision 3.51 (2.36) 4.43 (1.75) 1.99 (2.19) 3.95 (1.55)

Psychomotor 2.27 (1.49) 3.17 1.83) 1.47 (1.29) 3.15 (2.35)

Gross motor 0.23 (0.27) 1.81 (1.57) .11 (.23) 2.34 (2.27)

3.1 Position

The main effect for position was statistically significant (F (1, 21) = 5.73, p < .05). The overall

mean (averaging over all eight clusters and both administrations) was higher for drivers (M =
9

3.39) than for operators (M = 2.17). However, there was also a statistically significant Position ×

Administration interaction (F (1, 21) = 7.34, p < .05). As shown in Table 5, while the mean for

vehicle commanders decreased from the first to second administration, the mean for drivers

slightly increased.

Table 5. Overall cluster means by position and administration

Administration Vehicle Commander Driver

First 2.62 3.34

Second 1.73 3.45

3.2 Administration

The main effect for administration was statistically significant (F (1, 21) = 4.31, p < .05). The

overall mean (averaging over all eight clusters and both positions) was higher for the first

administration (M = 2.98) than the second (M = 2.59). However, as noted before, there was a

statistically significant Position × Administration interaction so that the decrease between

administrations was only for vehicle commanders. Further repeated measures ANOVAs by

position showed that the difference between administrations was statistically significant only for

vehicle commanders (F (1, 11) = 9.20, p < .05).

3.3 Ability Clusters

The main effect for ability clusters was statistically significant (F (1, 7) = 15.55, p < .001).

There were no statistically significant interactions among ability clusters and either position or

administration. Table 6 shows the results of paired comparisons between overall cluster mean

estimates (averaging over both survey administrations and both positions). Clusters within the

same box do not differ significantly (p < .05). Clusters in different boxes do not differ

significantly unless they have the same subscripts (a or b). Results suggest that conceptual,

communication, vision, and speed loaded are the most important clusters for both positions, over

both administrations because of their high scores.

Table 6. Overall ability cluster means

Cluster Estimated Mean

Conceptual 3.63

Communication (a) 3.52

Vision (a) 3.47

Speed loaded (a, b) 3.08

Psychomotor 2.52

Audition (b) 2.48

Reasoning 2.44

Gross motor 1.12
10

3.4 Comparison to Related Duties

In order to provide an idea of how skills for the (future) RSTA crew members compare to skills

for a known duty, we compared cluster scores of RSTA commanders and drivers with cluster

scores of four M1A2-SEP commanders and four drivers. The M1A2 SEP crews operated actual

vehicles, not simulations. The M1A2 SEP data were obtained during materiel testing in

November 2000. For the RSTA crew, we used scores on “maintain SU” (the only task discussed

in this report) from the second administration. For the M1A2 SEP commander and gunner, we

also used the cluster scores for “maintain SU” from the only administration of JASS. Because of

the small number for scores for the M1A2 SEP participants (n = 4), no statistical comparisons

were attempted.

Figure 3 shows the results for vehicle commanders. RSTA commanders had substantially higher

scores on communication, vision, audition and psychomotor clusters, and comparable scores on

conceptual, speed loaded, reasoning and gross motor clusters. The M1A2 commanders did not

have substantially higher scores on any clusters.

0

0.5

1

1.5

2

2.5

3

3.5

Communciation

Conceptual

Speed Loaded

Reasoning

Vision

Audition

Psychomotor

Gross Motor

RSTA CDR

M1A2 SEP CDR

Figure 3. Cluster scores for RSTA and M1A2 vehicle commanders.

Figure 4 shows the results for vehicle drivers. RSTA drivers had substantially higher scores on

all clusters.
11

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Communciation

Conceptual

Speed Loaded

Reasoning

Vision

Audition

Psychomotor

Gross Motor

RSTA Driver

M1A2 SEP Driver

Figure 4. Cluster percentages for RSTA and M1A2 vehicle drivers.

4. Discussion

Drivers appeared to indicate that higher levels of skills were required to maintain SU than did

vehicle commanders, immediately after training and after “job experience”. This was

unexpected, since vehicle commanders, as OCU operators, were expected to have the more

demanding job. However, it appears that with such responsibilities as driving the vehicle,

maintaining one’s overall orientation on the battlefield, being responsible for vehicle selfdefense, and assisting the commander in controlling robots, maintaining SU is even more

demanding in terms of human abilities than maintaining SU while one is more or less exclusively

operating robots.

Interestingly, while the rated level of abilities required for maintaining SU stayed essentially the

same for drivers from training to job experience, the rated level of abilities declined for vehicle

commanders. This suggests that while the training for drivers matched job experience (in terms

of ability demands), training for vehicle commanders exceeded job experience in terms of skill

demands. Put another way, commanders’ reliance on certain skills to maintain SU declined from

the first exercise compared to immediately after training, but drivers’ reliance on the skills

remained constant from training through the first exercise.

The highest rated skill clusters for “maintain SU” for vehicle commanders and drivers are

“conceptual,” “communication,” “vision,” and “speed loaded.” That these clusters are important

seems to make sense. The cluster “conceptual” involves such skills as flexibility of closure (the

ability to detect a known pattern), visualization (the ability to predict how a pattern will appear

after changes are made), problem sensitivity (the ability to tell when something is wrong or
12

likely to go wrong), and spatial orientation (the ability to tell where you are in relation to the

location of some object or vice versa). Communication is also critical in maintaining SU.

Providing information to or acquiring information from others is critical in verifying or rejecting

hypotheses about the situation. The cluster “speed loaded” involves such skills as speed of

closure (combining different pieces of information into a meaningful pattern quickly), perceptual

speed and accuracy (comparing patterns quickly), time sharing (shifting back and forth between

sources of information, such as different screens or human versus computer input). Visual skills

involve near vision, color discrimination and glare sensitivity. These skills would be useful in

identifying entities represented on the screen the first step toward pattern recognition and SU.

Drivers would need these skills, for example, to detect from the SC4 and communications traffic

that a group of nearby icons represented a threat, to advise the vehicle commander of the threat,

and to understand where the vehicle could be positioned to avoid the threat while continuing the

mission. These skills would be useful to a vehicle commander, for instance, in determining that

the group of wheeled vehicles to the north could be trucks that could arrive in the nearby village

within a half hour, which would be bad if they were enemy, and further, that one’s unmanned

aerial vehicle is close enough to provide confirmation before the vehicles reach the village.

One should also note that while “psychomotor” skills played only a minor role and “auditory”

and “gross motor” clusters no role at all, these vehicles were situated on a concrete floor and

“moving” only across virtual terrain. If the commander and driver had been in a real vehicle,

pitching over the terrain with gears grinding and engine howling, auditory and gross motor

clusters may well have played some role, and psychomotor skills an even greater role.

Also, the cluster “reasoning” did not rank highly for any task. However, the skills involved in

this cluster were defined in mathematical terms (e.g., inductive and deductive reasoning, number

facility) rather than the more general use of this term.

Concerning comparison of RSTA crews with M1A2 SEP crews, skill requirements in general

were higher for RSTA crews. RSTA commanders had substantially greater skill requirements

for communication, vision, and audition. Since the RSTA commanders had a much wider area

of responsibility than would tank commanders, it seems to make sense that obtaining and

providing information related to maintaining SU would be more difficult for them than for tank

commanders. However, considering that the RSTA commanders were in a simulation, the higher

skill requirements for vision and audition are more surprising. Apparently, it is more difficult

(vision-wise) to maintain SU by observing several screens in an RSTA vehicle than to maintain

SU by observing a Force XXI Battle Command at Brigade and Below screen and the external

environment in a tank. Also, we observed that RSTA commanders frequently communicated

with RSTA drivers, as well as with unit of action personnel outside the vehicle, perhaps

increasing the audition skill requirements over those of a tank commander. For the RSTA

drivers, overall skill requirements were higher. On the cognitive skill clusters, perhaps this

would be expected, since tank drivers have a much smaller area in which they must maintain SU,
13

as well as no gunnery duties. However, considering that M1A2 SEP drivers were in field

environment, their physical skill requirements might be expected to be higher. Thus, the

differences on these skills could well be magnified if the RSTA drivers were in a field

environment.

There are some limitations of this research. Since the scenarios were not controlled (at least at

vehicle level), it is possible that the scenarios were inadvertently more demanding for drivers

than for vehicle commanders. Demographics show that commanders tended to be of higher rank,

better educated (more college graduates), and had more time in the National Guard. Thus, the

commanders’ greater experience and education could have made their job seem easier. It is

difficult to assess how much of differences in skill ratings are attributable to differences in

experience versus differences in the job. Also, had the second administration been conducted

later, as originally scheduled, the drivers’ ratings may have decreased because of more

experience. Of course, these results are based on simulation versus a field environment.

5. Recommendations

The important skill clusters for OCU personnel appear to be “conceptual,” “communication,”

“vision,” and “speed loaded.” One strategy to improve performance of these skill clusters is

human factors engineering (HFE). HFE could be used to improve conceptual skills by providing

decision aids in pattern recognition. Subject matter expert input could be used to identify

recurring, important patterns. Then, when a pattern is located by the software, commanders and

drivers could be cued to its presence, for instance, “enemy air defense units located on aerial

sensor route,” or “threat vehicle near direct fire range.” In order to help improve vision,

optimizing human factors design seems to be advisable. For instance, icons should be designed

to be easily recognizable as representing a specific entity, and the software should allow for

rapidly “zooming” in and out of views. Communication skills could be facilitated by “chat

room”-type software, making it easy for key players on a specific task to communicate easily

without having to depend on line-of-sight radios. One way to improve quick pattern fusion (i.e.,

speed-loaded skills) would be to have a true common display that shows all information, rather

than our having to look at several screens to combine information. The “chat” channel could

even be at the bottom of the screen.

For the “concept,” “communication,” and “speed load” clusters, specifically designed training

seems to be important. High-paced scenarios involving multiple tasks, when personnel must

combine information from several sources in order to recognize patterns and problems while

maintaining their individual spatial orientation, would probably be beneficial. One example of

this would be, while directed to move one set of robotic vehicles to cover a bridge, personnel are

alerted to multiple vehicles approaching a village. The commander and driver must then
14

determine if the vehicles represent, for instance, an enemy force, coalition vehicles “out of

position” or a Red Cross convoy bringing aid.

Personnel selection or assignment might also play a role. While personnel could be screened for

any of these skills, color vision seems to be among skills that are difficult or impossible to teach

and are therefore good candidates for selection.

As was mentioned, no written job responsibilities for vehicle commanders and drivers were

provided. More research is needed during more controlled conditions to determine the optimum

division of duties between the commander and driver at various stages of mission progression

and the effects of such distributions on abilities needed and task performance.

Abilities for future jobs, such as RSTA vehicle commander and driver may be different than

abilities required for present-day equivalent positions, such as scouts. Although no scout data

were available, a comparison with high technology tank crews suggests that more abilities may

be required for future jobs. It is important that the determination of abilities and the level of

abilities that may be needed for future jobs be done empirically, versus using someone’s best

guess. This report demonstrates one method of empirical research into the abilities required for

future jobs before the materiel to perform those jobs has been fielded.
15

6. References

Department of the Army. The Objective Force C4ISR Concept, TP 525-3-0.1. U.S. Army

Training and Doctrine Command: Fort Monroe, VA, 2002.

Endsley, M.R.; Garland, D.J. Situation Awareness Analysis and Measurement. Lawrence

Erlbaum Associates: Mahwah, NJ, 2000.

Fleishman, E.A.; Quaintance, M.K. Taxonomies of Human Performance: The Description of

Human Tasks. Management Research Institute: Alexandria VA, 2000.

Knapp, B.G.; Tillman, B.W. Job Skill Assessment Software System. HFES Annual Meeting

Proceedings 1998, 42, 1319.

Gill, L.; Schmidt, R.; Baldauf, A.; Brabson, G.; Myers, J.; White, D.; Dorsey, J. System Analysis

Report for the M1A2 Abrams System Enhancement Package. U.S. Army Test and Evaluation

Command: Alexandria, VA, 1999.
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