Effectiveness of Stereoscopic Displays for Indirect-Vision 

Driving and Robot Teleoperation

by Jessie Y. C. Chen, Razia V. N. Oden, Caitlin Kenny, and John O. Merritt

ARL-TR-5259 August 2010

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Army Research Laboratory

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ARL-TR-5259 August 2010

Effectiveness of Stereoscopic Displays for Indirect-Vision 

Driving and Robot Teleoperation

Jessie Y. . Chen

Human Research Directorate, AEL

Razia V. N. Oden

National Research Council – Research Associateship Program

Caitlin Kenny

University of Central Florida – Institute for Simulation & Training

John O. Merritt

The Merritt Group

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Effectiveness of Stereoscopic Displays for Indirect-Vision Driving and Robot 

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National Research Council – Research Associateship Program

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University of Central Florida – Institute for Simulation & Training

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14. ABSTRACT

A three-part experiment was conducted to investigate the usefulness of two types of three-dimensional (3-D) stereoscopic 

displays (SDs) for simulated indirect-vision driving (with various terrains) and live robot teleoperation. Results showed that 

overall, participants completed their tasks significantly faster when they used an SD in 3-D mode compared to the baseline twodimensional (2-D)/monoscopic condition. They also navigated more accurately with SDs in 3-D mode. When the effectiveness 

of the SDs was examined separately, the results showed that the system with active 3-D shutter glasses appeared to be more 

effective in supporting faster responses and task completion times than did the system using passive polarized 3-D glasses. 

Participants’ self-assessed “simulator sickness” and workload after interacting with the two SD systems did not differ 

significantly between displays or between the 3-D vs. 2-D modes of operation.

15. SUBJECT TERMS

stereoscopic display, robot teleoperation, human-robot interaction, simulation, operator performance, indirect vision driving

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Jessie Y. C. Chen

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Contents

List of Figures v

List of Tables v

Acknowledgments vi

1. Introduction 1

1.1 Stereoscopic Displays......................................................................................................1

1.2 Current Study ..................................................................................................................2

2. Method 3

2.1 Participants......................................................................................................................3

2.2 Apparatus.........................................................................................................................3

2.2.1 Stereoscopic Display Systems.............................................................................3

2.2.2 Stereoscopic Images, Video, and Simulation......................................................5

2.2.3 Talon Robot.........................................................................................................6

2.2.4 Surveys and Tests................................................................................................7

2.2.5 Stereoscopic Test.................................................................................................7

2.3 Experimental Design .......................................................................................................7

2.4 Procedure.........................................................................................................................7

2.4.1 Perceptual Tests...................................................................................................8

2.4.2 Robot Teleoperation Tests...................................................................................8

2.4.3 Virtual Tests........................................................................................................9

2.5 Dependent Measures .......................................................................................................9

3. Results 11

3.1 Overall Effects of Stereovision and Display Type........................................................11

3.2 Perceptual Tests.............................................................................................................12

3.3 Robot Teleoperation......................................................................................................12

3.4 Virtual Scenarios...........................................................................................................13

3.4.1 Floating Object Course......................................................................................13

3.4.2 Obstacle Course.................................................................................................14

3.4.3 Negative Terrain Course ...................................................................................15
iv

3.5 Sickness and Perceived Workload ................................................................................15

4. Discussion 15

5. References 17

Appendix A. Demographic Survey and Summary of Results 19

Appendix B. NASA-TLX Questionnaire 23

Appendix C. Simulator Sickness Questionnaire 25

Appendix D. Scoring Procedure for the Simulator Sickness Questionnaire 27

List of Symbols, Abbreviations, and Acronyms 29

Distribution List 30
v

List of Figures

Figure 1. Stereoscopic displays used in the virtual tests.................................................................3

Figure 2. TALON robot with stereoscopic cameras. ......................................................................6

Figure 3. Robot teleoperation tests. ................................................................................................8

Figure 4. (a) Floating objects course; (b) obstacle course; and (c) negative terrain course. ........10

Figure 5. Response times (s) for the perceptual tests....................................................................12

Figure 6. Robot teleoperation completion times...........................................................................13

Figure 7. Average speed (miles per hour) for the virtual tests in 3-D condition. .........................14

Figure 8. Percentage of time off-course for the obstacle and negative terrain courses. ...............14

List of Tables

Table 1. Summary of performance measure means and standard deviations...............................11
vi

Acknowledgments

This project was funded by PEO Integration (formerly PEO FCS[BCT]). The authors would like 

to thank Dr. Abraham Kim of PEO Integration for his guidance throughout the project. We 

would also like to thank Mr. Mike Barnes (ARL-HRED), Dr. Keryl Cosenzo (ARL-HRED), 

Mr. Jeff Koshko (TARDEC), Mr. Joe Kott (TARDEC), Mr. Jeremy Gray (TARDEC), and 

Mr. John Vala (TARDEC) for their guidance and assistance and the Robotic Systems Joint 

Program Office (RS JPO) for lending us a TALON robot for the experiment. We thank Dr. Bob 

Sottilare of RDECOM-STTC for sharing his experiment room with us. We also thank Dr. Neal 

Finkelstein and Mr. Irwin Hudson of RDECOM-STTC for their assistance. Finally, we 

acknowledge the contributions of the following individuals to this project: Mr. Brian Oigarden 

(UCF-IST), Mr. Nathan Hubbard (UCF-IST), Mr. Dean Reed (UCF-IST), Mr. Brian Plamondon 

(UCF-IST), and Dr. Robert S. Kennedy (RSK Assessments, Inc.). Mr. Oigarden and 

Mr. Hubbard also contributed to part of this report.
1

1. Introduction

The U.S. Army is currently developing future manned ground vehicles that will support indirectvision driving (IVD), that is, driving via a visual display rather than direct viewing of the 

environment. The current plans for the future vehicular programs are to use a 360° horizontal/ 

90° vertical two-dimensional (2-D) display to provide local situational awareness information to 

the driver. However, recent field tests of vehicles using these IVD displays showed that drivers 

tended to drive significantly more slowly than when driving with normal direct view of the 

environment, showing as much as a 60% increase in time on their driving tasks (Flascher, 2008; 

GDLS, 2007). It was reported that driving performance decrement may have been linked to the 

drivers’ poor depth perception of the environment due to the loss of binocular depth perception 

when using the 2-D IVD video displays, resulting in their degraded judgment of the terrain 

features (GDLS, 2007). 

One proposed solution is to investigate the effectiveness of stereoscopic displays (SDs) for

improving driving performance in general, and driving speed in particular. Past research has 

shown that SDs, which rely on various techniques to present binocular images to the user, appear 

to provide advantages over 2-D/monoscopic displays, such as faster and more accurate 

perception of the environment, better distance estimation, enhanced detection of terrain hazards 

(e.g., slopes and depressions), enhanced object recognition and detection, and visual noise 

filtering (Drasic, 1991; Merritt, CuQlock-Knopp, and Myles, 1997; Scribner and Gombash, 

1998; Singer, Ehrlich, Cinq-Mars, and Papin, 1995). Studies have shown that SDs could 

enhance telerobotic vehicle operators’ speed of navigation and distance judgment (Holzhausen, 

Pitrella, and Wolf, 1993; Spain and Hughes, 1991; Umeda, Martin, and Merritt, 1991). 

1.1 Stereoscopic Displays

Stereoscopic displays can provide many advantages over traditional monocular displays in 

driving, particularly in off-road, military driving. According to Dumbreck, Smith, and Murphy 

(as cited in Drascic, 1991), remote manipulation tasks that involve “ballistic movement, 

recognition of unfamiliar scenes, analysis of three dimensionally complex scenes and the 

accurate placement of manipulators or tools within such scenes” especially benefit from SDs. 

However, empirical studies examining the utility of SDs generally report that SDs might be 

useful in only certain circumstances. For example, Drascic (1991) found that the benefits of 

SDs, while longer lasting for tasks that required binocular depth cues (i.e., using a robot to place 

an object between two “bombs” separated by 8 cm), did not last as long for tasks that did not 

require much binocular depth perception (i.e., same task with “bombs” separated by 64 cm). 

Generally, participants quickly learned how to use the monocular cues available in the 

monocular displays to accomplish those tasks.
2

Draper, Handel, and Hood (1991) had participants perform Fitts Law (Fitts, 1954) tapping tasks 

and reported that SDs were only useful for more difficult tasks and only for inexperienced 

participants. They suggested that SDs would be useful when the image quality, task structure 

and predictability, user experience, and manipulator dexterity were suboptimal. Rosenberg 

(1993) found that SDs helped depth-matching performance and the distances between the two 

cameras affected the usefulness of the SDs. They reported that the best performance was 

achieved when the inter-camera distance was less than the interocular distance (i.e., 2–3 cm vs. 

6 cm). On the other hand, Green, Dougherty, and Savacool (2003) did not find significant 

benefits of using SDs for their task, teleoperating shipboard cranes to place cargo, in terms of 

time and accuracy of task performance and depth perception. As for user preference, a 

consistent finding from various studies is that teleoperators generally prefer SDs over monocular 

displays (Drascic and Grodski, 1993; Green Dougherty, and Savacool, 2003). However, as noted 

in Scribner and Gombash (1998), artificially induced binocular stereovision may increase motion

sickness and perceived stress.

More realistic tests were conducted by researchers from the U.S. Army who investigated the 

ability of humans to detect obstacles in static and moving video terrain with three-dimensional 

(3-D) and hyperstereo (ocular distance artificially increased to accentuate depth cues) displays. 

The results indicated improved detection of negative terrain and mobility obstacles for the 3-D 

conditions vs. 2-D conditions (Merritt, CuQlock-Knopp, Kregel, Smoot, and Monaco, 2005; 

Merritt, CuQlock-Knopp, and Myles, 1997). Preliminary field demonstrations of actual Army 

systems suggest the 3-D performance gains will extend to teleoperated systems. The researchers 

caution that SDs have definite perceptual and physical limitations, as mentioned above; however, 

SDs should be an optional mode for complex terrain, especially where depth perception is 

crucial, and for arm manipulations and other tasks where normal 3-D cues are unavailable 

(B. Vaughan, personal communication, 16 March 2006).

1.2 Current Study

In the current study, we investigated whether an SD could improve the operators’ IVD and robot 

teleoperation performance, and which type of SD supported better performance. We evaluated 

two types of SD technologies: active shutter glasses (nVIDIA*) and passive polarized glasses 

(Miracube†

 *nVIDIA is a registered trademark of nVIDIA Corp.

). The experiment had three parts – Perceptual evaluation, Robot Teleoperation, and 

Virtual (simulated IVD). For the Perceptual part of the experiment, participants viewed still 

images of terrain using one of the SDs in 3-D and responded about object distance from the 

camera as well as object height on elevation planes. They also viewed videos of hazardous 

terrain using one of the SDs in 3-D and responded when they detected a terrain drop-off. For the 

Robot Teleoperation part of the experiment, participants maneuvered a robot through a course of 

cones on a grass terrain using one of the SDs in both 2-D and 3-D modes. For the Virtual part of 

†Miracube is a registered trademark of Miracube, Inc.
3

the experiment, participants drove through both 3-D stereo and non-stereo scenarios in a 3-D

rendered, simulated driving environment (VBS2*

2. Method

). Different types of terrains were simulated in 

the virtual scenarios.

2.1 Participants

Thirty-two individuals (19 males and 13 females, mean age = 25.8) from the Orlando, FL area 

participated in the experiment. Participants received payment for their time at the rate of 

$15/hour.

2.2 Apparatus

2.2.1 Stereoscopic Display Systems

The stereoscopic display systems used were an nVIDIA and a Pavonine Miracube display 

(figure 1).

Figure 1. Stereoscopic displays used in the virtual tests.

 *Virtual Battlespace 2 (VSB2) is a registered trademark of Bohemia Interactive Australia (BIA).
4

The nVIDIA system used a 22-in 120 Hz display, with a resolution of 1680 × 1050 pixels, 

viewed with active 3-D shutter glasses synchronized by an infrared (IR) emitter pointing toward 

the glasses. The stereoscopic video is sent as an alternating series of left eye and right eye 

frames. Therefore, the frame rate perceived by the user is 60 Hz. All frames have full pixel 

resolution. To achieve proper depth, convergence, and orthostereo (the ability to produce images 

in the display that are the same visual size as in the real world), the display had to be calibrated 

each time the experimental condition changed between 2-D and 3-D. To achieve this, the depth 

was set, using the scroll bar on the back of the emitter, to a predefined level (3/50 for 2-D, 30/50 

for 3-D) and the convergence was set, using Ctrl+F5 or Ctrl+F6 on the keyboard, to a predefined 

level (0 for 2-D, ~2.5 in for 3-D).

The Pavonine system used a 24-in Miracube display, with a resolution of 1920 × 1200 pixels. 

The Miracube display uses a filter sheet placed in front of the 60 Hz liquid crystal display. The 

filter has alternating lines of polarization causing the odd pixel rows of the display to be 

polarized oppositely from the even rows. Coupled with passive polarized 3-D glasses, this 

provides the viewer with a stereoscopic signal at nearly full brightness but with half 

vertical resolution per eye. To achieve proper depth, convergence, and orthostereo, the display 

had to be calibrated each time the experimental condition changed between 2D and 3D. To 

achieve this, values were set to a predefined level for 3D (convergence = –0.0306, separation 

= 562.17%), and they were toggled on and off using the “?” key on the keyboard.

2.2.1.1 Stereoscopic Video Data Processing. The video data path was as follows. Each 

camera was connected to its own dedicated 1394.b port. Using the synchronized capture feature 

of the cameras, frames are captured synchronously (measured to be <1/480 of a second 

difference). The two frame streams are then combined by the Stereoscopic Multiplexer*

software program (for more information, please see http://3dtv.at/Index_en.aspx). The 

Multiplexer provides two functions. First it compares the time stamps of frames on both 

incoming streams. If the frames are not in sync, it will drop frames until the streams are in sync. 

The Multiplexer then merges the two streams into a single, double-wide side-by-side 2048 × 768 

video stream, which is then encoded using xvid compression, packetized, and sent over the 

network using two bridged Linksys†

 *Stereoscopic Multiplexer is a registered trademark of 3dtv.at.

WRT600N routers. The receiving computer then 

depacketizes the stream and makes it available as a virtual live camera to the system. Any 

Windows direct-show compatible device can then connect to the virtual camera and receive the 

video stream. Stereoscopic Player® is then used to connect to the virtual camera, convert the 

video into the correct format for the display, and play the video.

†Linksys is a registered trademark of Cisco Systems, Inc.
5

2.2.2 Stereoscopic Images, Video, and Simulation

Twenty still pictures were used in the experiment. For ten of the pictures, participants were 

asked to choose which of two objects was closest to the camera. For the other ten pictures, 

participants were asked to choose between two planes of elevation and identify the object that 

had a higher elevation. Ten pre-recorded videos of terrain scenes were also used in the 

experiment, each scene averaging 30 to 45 s long. The 10 scenes were of wooded areas and were 

collected both locally and remotely by a consultant (Merritt et al., 2005). Participants were 

asked to respond as soon as they saw a terrain drop-off in the scene.

Bohemia Interactive’s VBS2 (U.S. Army version) and the VBS2 VTK Developer Suite v1.23 

were used to create the virtual driving simulation. Custom 3-D models that were needed to 

create the virtual scenarios were modeled in 3ds Max9*. Custom textures were created with 

Adobe† Photoshop‡

For the Negative Terrain tracks, it was important to minimize the visual cues that are typically 

present in realistically-rendered 2-D simulations (e.g., high contrast lighting, realistic textures 

which give the illusion of added depth). The Negative Terrain tracks incorporated both positive 

terrain features, such as trees and rocks, and negative terrain features, such as cliffs and ditches. 

An attempt was made to eliminate all visual hints, such as changes in texture type or shadows 

that would be a cue to the participant that a drop-off in elevation was ahead. Flatly-rendered 

textures with random repeating patterns (e.g., sand with weeds and plain grass) were used to 

eliminate visual cues caused by the textures. A script was created to change the time of day in 

VBS2 to dusk to eliminate high contrast lighting (e.g., sharp highlights and shadows in negative 

terrain areas).

CS3. The models and textures were then converted to VBS2 native formats 

(.paa and .p3d) using Oxygen2 and the Tex Viewer. All terrain models larger than 50 m had to 

be imported into VBS2 as separate models and then reassembled to appear seamless.

VBS2 is a very robust 3-D game engine that can render very high polygon environments, high 

resolution textures, and many dynamic and static objects in real-time. Due to the unique 

requirements of the Negative Terrain condition, these tracks featured a higher polygon count and 

higher resolution textures than the other virtual conditions. Computer performance varies in 3-D 

simulations depending on the polygon count and the size and number of textures being rendered 

in real-time. A drop in computer performance is magnified to a greater extent when the 

environment is being rendered in 3-D stereo in real-time, since the 3-D engine must render two 

images per frame instead of one. A slight drop in hardware performance was discovered on the 

Negative Terrain tracks due to the reasons above. When in 3-D stereo mode, at times the vehicle 

appeared to move slightly more slowly than when in 2-D mode while maintaining smooth 

driving conditions with no lag.

 *3ds Max9 is a registered trademark of Autodesk, Inc.

†Adobe is a registered trademark of Adobe Systems, Inc.

‡Photoshop is a registered trademark of Adobe Systems, Inc.
6

2.2.3 Talon Robot

The small unmanned ground vehicle (SUGV) was a TALON* robot (figure 2), which was 

controlled remotely. The TALON used in the experiment was the small, mobile version. It has 

been used in military operations since 2000, and armed TALONs began to serve as battle 

buddies for Soldiers in 2005. The two cameras used in the experiment, Sony† XCD-SX90CR 

cameras with a resolution of 1024 × 768 pixels, were mounted on the main platform of the 

TALON, with an inter-camera distance of 2.25 in, along with other processing equipment for the 

SD systems. The combined field of view for the cameras was 44.5°. The distance between the 

cameras and the ground was 30.5 in.

Figure 2. TALON robot with stereoscopic cameras.

 *Talon is a registered trademark of Foster-Miller, Inc.

†Sony is a registered trademark of Sony Corp.
7

2.2.4 Surveys and Tests

A demographics questionnaire (appendix A) was administered at the beginning of the training 

session. Participants’ perceived workload was evaluated using the computerized version of the 

National Aeronautics and Space Administration-Task Load Index (NASA-TLX) questionnaire

(appendix B), which includes a pairwise comparison weighting procedure (Hart and Staveland, 

1988). The NASA-TLX is a self-reported questionnaire of perceived demands in six areas: 

mental, physical, temporal, effort (mental and physical), frustration, and performance. 

Participants evaluated their perceived workload level in these areas on 10-point scales as well as 

completed pairwise comparisons for each subscale.

The Simulator Sickness Questionnaire (SSQ; see appendix C) was used to evaluate participants

simulator sickness symptoms (Kennedy, Lane, Berbaum, and Lilienthal, 1993). The SSQ 

consists of a checklist of 16 symptoms. Each symptom is related in terms of degrees of severity 

(none, slight, moderate, severe). A total severity score can be derived by a weighted scoring

procedure (see appendix D) and reflects overall discomfort level.

2.2.5 Stereoscopic Test

The stereoscopic test used was a Randot Stereo Test*

2.3 Experimental Design

that had four parts, three of which were 

used for this experiment. The stereo test uses polarized glasses worn by the participant and is 

held perpendicular to the ground 16 in from the participant. The first part of the test was 

identification of shapes in four boxes that required a stereoacuity of 500 s of arc, and this was the 

point at which participants were either included or excluded from the study. The second part of 

the test was identification of shapes in four boxes that required a stereoacuity of 250 s of arc. 

The third part of the test was identification of which of three circles stood out to the participant, 

and these required varying levels of stereoacuity from 200 to 20 s of arc. Participants’ scores 

(i.e., seconds of arc) were used as a covariate, stereoacuity, for the analyses.

The overall design of the study was a 2 × 2 mixed-subjects design. The between-subject factor 

was Display type (nVIDIA v Miracube) and the within-subject factor was Stereovision (2-D v

3-D). However, for the first part of the experiment, participants did not evaluate the pictures and 

videos in 2-D, as Merritt et al. (2005) have demonstrated that terrain hazards could be detected 

sooner with a 3-D display than with a 2-D display.

2.4 Procedure

After being briefed on the purpose of the study and signing an informed consent form, 

participants completed the demographics questionnaire. The experimenter explained the 

experiment to participants and answered any questions they had at the time. Not all participants 

 *Randot stereo tests are produced by Precision Vision.
8

completed the three parts of the experiment sequentially due to equipment delays. The 

participants then went through the calibration process as described in section 2.2.1. The distance 

between the participant and the SD was 23 in for nVIDIA and 25 in for Miracube.

2.4.1 Perceptual Tests

For the Perceptual tests, participants viewed 20 still pictures; for 10 of the pictures they 

identified the closer object, and for the other 10 they identified which object was on a higher 

elevation plane. Participants then watched 10 pre-recorded video clips of hazardous terrain in 

3-D (with his/her assigned SD system) and responded as soon as they detected a drop-off in 

terrain. Each video was ~30–45 s long, and after completing this portion, participants assessed 

their workload and sickness symptoms.

2.4.2 Robot Teleoperation Tests

Participants practiced teleoperating the robot (without line-of-sight) for one course in 2-D and 

one course in 3-D. They were then asked to drive a robot through four courses marked by traffic 

cones (figure 3), twice with 3-D and twice with 2-D (the same display with 3-D turned off). If 

participants drove the robot completely out of the course (i.e., the entire robot was outside the 

course), the trial was restarted, and each participant was allowed to restart each trial twice. If 

participants went outside the course more than three times (the original trial plus two restarts), 

they were moved to the next course. The performance data from the last successful trial was 

used with a restart value reflecting that participants had to be restarted. Trials were counterbalanced 

(Williams Square) to avoid order effects. Each trial took approximately one minute and after 

completing this portion, participants assessed their workload and sickness symptoms.

Figure 3. Robot teleoperation tests.
9

2.4.3 Virtual Tests

Participants were asked to drive through three types of courses, and each type of course featured 

two unique tracks (figure 4). One type of course was a “Floating Objects Course” in which 

participants drove on an enclosed, pavement course toward six sets of object pairs (figure 4a). 

Participants were instructed to drive as quickly as possible around the objects on the side (left or 

right) of the closer object. Another type of course was an “Obstacle Course,” in which 

participants drove as quickly as possible around obstacles, such as rocks and shrubs (figure 4b). 

The third type of course was a “Negative Terrain Course” in which participants drove on an 

enclosed course of varying negative and positive terrain features (i.e., holes in the ground, dropoffs, and hills; figure 4c). Participants were instructed to drive as quickly as possible through the 

course while avoiding the positive and negative terrain obstacles. Each participant was given a 

practice course for each type of trial in 2-D and 3-D prior to starting the experimental trials, and 

each participant completed each type of experimental course twice, once in 2-D and once in 3-D. 

Trials were counterbalanced (Williams Square) to avoid order effects. Each trial took 

approximately three minutes and after completing all trials, participants assessed their workload 

and sickness symptoms.

Following completion of the three parts of the experiment, participants were fully debriefed, and 

their information was taken for payment. The entire experiment lasted ~1 1/2 h.

2.5 Dependent Measures

The dependent measures for the Perceptual tests of the experiment were response time and

accuracy. The dependent measures for the Robot Teleoperation tests were course completion 

time and number of cones hit. The dependent measures for the Virtual tests were course 

completion time and accuracy of completing the simulated driving tasks (i.e., how many terrain 

hazards they failed to avoid). Subjective measures included participants’ assessments of their 

workload (i.e., NASA-TLX scores) and sickness (i.e., SSQ) scores, which were assessed after 

each part of the experiment. Mixed-design Analyses of Covariance (ANCOVAs) with Display 

(nVIDIA v Miracube) as the between-subject factor and Stereovision (2-D v 3-D) as the withinsubject factor were used to evaluate the performance measures. Participants’ stereovision was 

used as the covariate. All tests were performed using SPSS 18.0 with an alpha level of 0.05.
10

Figure 4. (a) Floating objects course; (b) obstacle course; and (c) negative 

terrain course.

(a)

(b)

(c)
11

3. Results

3.1 Overall Effects of Stereovision and Display Type

Table 1 lists several measures relating to participants’ performance. The effects of Stereovision 

on course completion times for the Robot Teleoperation and the three Virtual tests were 

evaluated. The analysis revealed that there was a significant main effect for Stereovision, 

F(4,26) = 5.53, p < 0.005, with 2-D being longer/slower than 3-D. The effect of Display in the 

3-D conditions was investigated separately to examine the effect of different display types on 

times (i.e., response times for Video viewing, course completion times for Robot Teleoperation 

and the Virtual tests). The analysis showed that there was a significant main effect for Display, 

F(1,29) = 4.46, p < 0.05, with nVIDIA being better (i.e., faster) than Miracube. The following 

segments report the results of each task.

Table 1. Summary of performance measure means and standard deviations.

Measures

2-D 3-D

nVIDIA Miracube nVIDIA Miracube

Perceptual

Stills

Response time (s) — — 8.14

(6.35)

6.43

(5.14)

Percent correct — — 73

(44)

78

(42)

Video Response time (s) — — 8.25

(6.21)

11.22

(4.21)

Robot 

Teleoperation —

Completion time (s) 65.03

(14.71)

74.78

(20.52)

62.63

(14.54)

67.94

(19.87)

No. of cones hit 3.88

(1.69)

4.56

(1.79)

3.38

(1.40)

4.19

(1.31)

Virtual

Floating

Completion time (s) 15.01

(3.84)

15.66

(4.66)

16.74

(5.13)

17.43 

(6.07)

No. of trials correct 3.69

(1.01)

4.44

(1.03)

4.44

(1.21)

5.19

(1.11)

Obstacle

Completion time (s) 152.6

(33.4)

149.2

(30.4)

160.3

(41.0)

157.7

(40.2)

Time off course (s) 16.73

(13.52)

16.92

(10.14)

14.84

(14.09)

13.85\(9.6

9)

Negative

Completion time (s) 209.8

(48.7)

217.6

(50.0)

189.2

(45.0)

217.2

(47.7)

Time off course (s) 17.44

(14.29)

10.11

(13.93)

11.23

(6.12)

11.13

(11.39)

Note: Standard deviations are presented in parentheses.
12

3.2 Perceptual Tests

For the still images, the Closer task and the Elevation task were merged for the analyses. 

Additionally, trials that less than 60% of participants got correct were not included in the 

analyses, as that performance level was just higher than chance level. The effect of Display on 

participants’ response times and accuracy (number correct out of 8 trials) was analyzed. Display 

did not have a significant effect on either response times or accuracy. For the Video viewing 

part of the tests, the effect of Display on response times for the 10 video clips was analyzed. The 

analysis revealed that there was a significant main effect for Display, F(1,26) = 5.47, p < 0.05, 

with nVIDIA (M = 7.33 sec) being better (i.e., faster) than Miracube (M = 11.87 s) (figure 5).

Figure 5. Response times (s) for the perceptual tests.

3.3 Robot Teleoperation

The effects of Display and Stereovision on participants’ course completion times and number of 

cones hit were analyzed. There was a marginally significant main effect of Display for course 

completion times, F(1,28) = 3.024, p = 0.093, with the nVIDIA display (M = 62.89 s) resulting 

in faster completion times than the Miracube display (M = 73.59 s) by more than 10 s, which is 

an ~15% reduction in completion times. While the difference in completion times between 

stereo conditions failed to reach statistical significance, the 3-D condition (M = 65.8 s) produced 

faster operating times than the 2-D condition (M = 70.6 s) by almost 5 s (figure 6). In terms of 

accuracy (number of cones hit), participants hit less cones in 3-D and with the nVIDIA display. 

However, the differences failed to reach statistical significance.
13

Figure 6. Robot teleoperation completion times.

3.4 Virtual Scenarios

3.4.1 Floating Object Course

The effects of Display and Stereovision on participants’ course completion times and accuracy 

(i.e., number of trials correct) were analyzed. For course completion times, there was a 

marginally significant interaction effect of Display x Stereoacuity, F(1,29) = 3.65, p = 0.066, 

indicating that completion times were more affected by Stereoacuity in the Miracube than the 

nVIDIA display (the Miracube display had a greater disparity in completion time over trials than 

the nVIDIA display) and that completion times were faster for those who had a better

stereoacuity (69 or below). Those participants using nVIDIA who had better stereoacuity were 

~1 s faster than those with worse stereoacuity; those using Miracube who had better stereoacuity 

and were ~4 s faster than those with worse stereoacuity. For accuracy, 3-D (M = 4.81) provided 

significantly more accurate results than 2-D (M = 4.06), F(1,29) = 4.37, p < 0.05. Additionally, 

the Miracube display (M = 4.79) provided significantly more accurate results than the nVIDIA 

display (M = 4.09), F(1,29) = 5.00, p < 0.05. Participants’ average speed (miles per hour) for the 

virtual tests in the 3-D condition is depicted in figure 7.
14

Figure 7. Average speed (miles per hour) for the virtual tests in 3-D condition.

3.4.2 Obstacle Course

The effects of Display and Stereovision on participants’ course completion times and accuracy 

(i.e., time off course) were analyzed. For accuracy, there was a marginally significant effect of 

Stereovision in that 3-D (M = 14.15 s) produced more accurate performance than 2-D 

(M = 16.25 s), F(1,27) = 3.59, p = 0.069. The percentage of time participants’ were off-course 

for the obstacle and the negative terrain courses are depicted in figure 8.

Figure 8. Percentage of time off-course for the obstacle and negative terrain courses.
15

3.4.3 Negative Terrain Course

The effects of Display and Stereovision on participants’ course completion times and accuracy 

(i.e., time off-course) were analyzed. None of the factors were significant.

3.5 Sickness and Perceived Workload

Participants’ SSQ scores and perceived workload were both slightly higher with nVIDIA than 

with Miracube. However, the difference was not statistically significant. Additionally, the 

nVIDIA display (M = 6.31) resulted in higher workload scores than the Miracube display 

(M = 4.44) for the Perceptual tests.

4. Discussion

In this experiment, we evaluated two SD systems in three different types of tasks: Perceptual 

evaluation with still pictures and pre-recorded videos, live Robot Teleoperation, and simulated

indirect driving in Virtual environments with different types of terrain. Overall, participants 

completed their tasks significantly faster when they used an SD in 3-D mode compared to the 

baseline 2-D mode. When the effectiveness of the SDs were examined separately, the analysis 

showed that the nVIDIA system appeared to be more effective than the Miracube in supporting 

faster response times and course completion times.

For the Perceptual tests, participants perceived hazardous terrains in videos significantly faster 

using nVIDIA than Miracube. Similarly, for the Robot Teleoperation tests, participants 

navigated the robot significantly faster using nVIDIA than Miracube, saving ~15% of navigation 

time per trial. Participants also operated the robot slightly faster with the 3-D mode than with the 

2-D mode, although the 3-D/2-D difference failed to reach statistical significance.

For the Floating course of the Virtual tests, the results showed that participants’ accuracy was 

significantly better with 3-D than with 2-D. In contrast to the results of the Robot Teleoperation 

tests, participants' accuracy was significantly better with Miracube than with nVIDIA. For the 

Obstacle course of the Virtual tests, the results showed that participants performed slightly better 

with 3-D than with 2-D, although the difference failed to reach statistical significance. For the 

Negative Terrain course of the Virtual tests, none of the factors were found to be significant. 

The lack of difference in operator performance for the Miracube display may be due to the fact 

that the Negative Terrain tracks had a very large amount of graphics that had to be rendered (as 

opposed to the Floating tracks and Obstacle tracks) and, for the Miracube display, two image 

streams needed to be rendered for the 3-D condition but only one image stream for the 2-D 

condition. Therefore, images in the 3-D Miracube condition could have been slightly slower or 

“sluggish” compared to 2-D, and this could account for why 3-D did not result in better 

performance than 2-D for the Miracube display. For the nVIDIA display, on the other hand, 

there was no difference in the amount of image streams that had to be rendered in the 2-D v 3-D 
16

conditions. In other words, there was no additional “cost” associated with rendering 3-D images 

for the nVIDIA display compared to the 2-D condition. As a result, participants drove faster in 

the 3-D condition (about 10% reduction in course completion time) and also more accurately 

(42% reduction in time off-course).

The data did not indicate a significant effect of Display type on sickness or workload for any of 

the tasks. This suggests that the two displays are comparable in their workload and sicknessinducing characteristics. Display type also did not have an effect on self-rated performance for 

the Robot Teleoperation or the Virtual scenarios. However, people using the nVIDIA display 

thought they performed better on the Perceptual tasks than people using the Miracube display. 

This could be due to the difficulty of the tasks combined with the viewing-position constraints of 

the Miracube display (i.e., if participants moved out of the “ideal” viewing eye height, the stereo 

was not as clear [less L/R channel separation, thus more ghosting, with consequent reduction in 

binocular depth perception], whereas the nVIDIA uses shutter glasses, which are not adversely 

affected by changes in user eye position relative to the display screen).

In conclusion, the results of this study indicate that stereoscopic displays do have advantages 

over tradition 2-D displays with respect to depth perception, specifically on driving and robot 

teleoperation tasks. Furthermore, the results show that the nVIDIA system is a superior system 

for these tasks than the Miracube system. Thus, it is recommended that the U.S. Army employ 

these systems in robot operator controls and indirect-vision driving displays. This should result

in better performance, and potentially less loss of life and equipment.
17

5. References

Draper, J. V.; Handel, S.; Hood, C. C. Three Experiments With Stereoscopic Television: When 

it Works and Why. Proceedings of IEEE International Conference on Systems, Man, & 

Cybernetics, Institute of Electrical and Electronics Engineers: New York, 1991, 

pp 1047–1052.

Drasic, D. An investigation of Monoscopic and Stereoscopic Video for Teleoperation. Master’s 

Thesis, University of Toronto. http://vered.rose.utoronto.ca/people/david_dir

/MASC/MASC.full.html 1991(Accessed 9 August 2004).

Drascic, D.; Grodski, J. Defense Teleoperation and Stereoscopic Video. Proceedings of SPIE 

(Stereoscopic Displays and Applications IV), 1915, pp 58–69, 1993.

Fitts, P. The Information Capacity of the Human Motor System in Controlling the Amplitude of 

Movement. J. Experimental Psychology 1954, 47, 381–391.

Flascher, O. Manned Ground Vehicle (MGV) Tech Challenges (briefing by Dr. Oded Flascher, 

MGV Crew Systems Technical Lead), 2008.

GDLS. VTI Synopsis_GDLS in Projectlink C/MS HIT WG, 29 October 2007.

Green, J.; Dougherty, E.; Savacool, E. Stereovision System for Shipboard Cranes, 2003. 

Proceedings of Interservice/Industry Training, Simulation, & Education Conference

(I/ITSEC)[CD ROM]. National Training Systems Association: Arlington, VA.

Hart, S.; Staveland, L. Development of NASA TLX (Task Load Index): Results of Empirical 

and Theoretical Research. In Human Mental Workload; Hancock, P., Meshkati, N., Eds.,

Elsevier: Amsterdam, 1988; pp 139–183.

Kennedy, R. S.; Lane, N. E.; Berbaum, K. S.; Lilienthal, M. G. Simulator Sickness 

Questionnaire: An Enhanced Method for Quantifying Simulator Sickness. The 

International Journal of Aviation Psychology 1993, 3 (3), 203–220.

Holzhausen, K.; Pitrella, F. D.; Wolf, H. Human Engineering Experiments Using a Telerobotic 

Vehicle. In Vision in Vehicles IV; Gale, A. G., Brown, I. D., Taylor, S. P., Haslegrave,

C. M., Eds., Elsevier Science Publishers: Amsterdam, 1993; pp 223–230.

Merritt, J. O.; CuQlock-Knopp, V. G.; Myles, K. Enhanced Perception of Terrain Hazards in 

Off-Road Path Choice: Stereoscopic 3D vs. 2D Displays. Proceedings of SPIE Targets and 

Backgrounds: Characterization and Representation III, 3062, 1997, 101–104. 
18

Merritt, J. O.; CuQlock-Knopp, V. G.; Kregel, M.; Smoot, J.; Monaco, W. Perception of Terrain 

Drop-Offs as a Function of L-R Viewpoint Separation in Stereoscopic Video. Proceedings 

of SPIE Helmet- and Head-Mounted Displays X: Technologies and Applications, 5800, 

2005, 169–176.

Rosenberg, L. B. The Effect of Interocular Distance Upon Operator Performance Using 

Stereoscopic Displays to Perform Virtual Depth Tasks. Proceedings of the IEEE Virtual 

Reality Annual International Symposium, Institute of Electrical and Electronics Engineers: 

Piscataway, NJ, 1993; pp 27–32.

Scribner, D. R.; Gombash, J. W. The Effect of Stereoscopic and Wide Field of View Conditions 

on Teleoperator Performance; ARL-TR-1598; U.S. Army Research Laboratory: Aberdeen 

Proving Ground, MD, 1998.

Singer, M. J.; Ehrlich, J.; Cinq-Mars, S.; Papin, J. Task Performance in Virtual Environments: 

Stereoscopic vs. Monoscopic Displays and Head-Coupling; Technical report no. 1034; U.S. 

Army Research Institute for the Behavioral and Social Sciences: Alexandria, VA, 1995.

Spain, E. H.; Hughes, T. W. Objective Assessments of Mobility with an Early Unmanned 

Ground Vehicle (UGV) Prototype Viewing System; Technical report; Naval Ocean Systems 

Center: San Diego, CA, 1991. http://handle.dtic.mil/100.2/ADA237317 (accessed 15 April 

2008).

Umeda, A. Y.; Martin, S. W.; Merritt, J. O. Remote Vision Systems for Teleoperated Ground 

Vehicles. Proceedings of NATO Defense Research Group on Robotics in the Air-Land 

Battle, 1991. http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA236765&Location

=U2&doc=GetTRDoc.pdf (accessed 15 April 2008).
19

Appendix A. Demographic Survey and Summary of Results 

 

This appendix appears in its original form, without editorial change.
20

Participant # _______ Age ______ Major ________________ Date ___________ Gender ___

1. What is the highest level of education you have had?

Less than 4 yrs of college ____ Completed 4 yrs of college ____ Other ____

2. When did you use computers in your education? (Circle all that apply)

Grade School Jr. High High School

Technical School College Did Not Use

3. Where do you currently use a computer? (Circle all that apply)

Home Work Library Other________ Do Not Use

4. For each of the following questions, circle the response that best describes you.

How often do you:

Use a mouse? Daily, Weekly, Monthly, Once every few months, Rarely, Never

Use a joystick? Daily, Weekly, Monthly, Once every few months, Rarely, Never

Use a touch screen? Daily, Weekly, Monthly, Once every few months, Rarely, Never

Use icon-based programs/software?

Daily, Weekly, Monthly, Once every few months, Rarely, Never

Use programs/software with pull-down menus?

Daily, Weekly, Monthly, Once every few months, Rarely, Never

Use graphics/drawing features in software packages?

Daily, Weekly, Monthly, Once every few months, Rarely, Never

Use E-mail? Daily, Weekly, Monthly, Once every few months, Rarely, Never

Operate a radio controlled vehicle (car, boat, or plane)?

Daily, Weekly, Monthly, Once every few months, Rarely, Never

Play computer/video games?

Daily, Weekly, Monthly, Once every few months, Rarely, Never

5. Which type(s) of computer/video games do you most often play if you play at least once every few months?

6. Which of the following best describes your expertise with computer? (check √ one)

_____ Novice

_____ Good with one type of software package (such as word processing or slides)

_____ Good with several software packages

_____ Can program in one language and use several software packages

_____ Can program in several languages and use several software packages

7. Are you in your usual state of health physically? YES NO

 If NO, please briefly explain:

8. How many hours of sleep did you get last night? ______ hours

9. Do you have normal color vision? YES NO 

10. Do you have prior military service? YES NO If Yes, how long __________
21

Summary of Demographic Survey Results

Age: Ranged from 18-40 Mean = 25.8 Standard Deviation = 6.2

Gender: 19 Males 13 Females

How often do you:

Drive a car? Never = 1 Rarely = 0 Once every few months = 0 Monthly = 0

Weekly = 1 Daily = 30

Use a joystick? Never = 6 Rarely = 17 Once every few months = 3

Monthly = 1 Weekly = 2 Daily = 3

Operate a radio-controlled vehicle? Never = 15 Rarely = 12

Once every few months = 2 Monthly = 1 Weekly = 1 Daily = 1

Play computer/video games? Never = 2 Rarely = 7 Once every few months = 5

Monthly = 5 Weekly = 4 Daily = 9

Which types of computer/video games do you most often play if you play at least once every few 

months? Driving games = 5 Other/None = 27

Which of the following best describes your expertise with computer/video games?

Novice = 6

Good with one type of software package = 8

Good with several software packages = 14

Can program in one language and use several software packages = 2

Can program in several languages and use several software packages = 2

What type(s) of radio-controlled vehicle do you use most often if you use one at least every few 

months? Ground vehicles = 6 Other/None = 26

Which of the following best describes your expertise with radio-controlled vehicles?

Novice = 26

Good with one type of software package = 4

Good with several software packages = 2

Can program in one language and use several software packages = 0

Can program in several languages and use several software packages = 0

Are you in your good/comfortable state of health physically? Yes = 31 No = 1

Do you have normal/corrected-to-normal vision? Yes = 30 No = 2
22

INTENTIONALLY LEFT BLANK.
23

Appendix B. NASA-TLX Questionnaire 

 

This appendix appears in its original form, without editorial change.
24

Please rate your overall impression of demands imposed on you during the exercise.

1. Mental Demand: How much mental and perceptual activity was required (e.g., thinking, looking, searching,

etc.)? Was the task easy or demanding, simple or complex, exacting or forgiving?

LOW |---|---|---|---|---|---|---|---|---| HIGH

1 2 3 4 5 6 7 8 9 10

2. Physical Demand: How much physical activity was required (e.g., pushing, pulling, turning, controlling, 

activating, etc.)? Was the task easy or demanding, slow or brisk, slack or strenuous, restful or laborious?

LOW |---|---|---|---|---|---|---|---|---| HIGH

1 2 3 4 5 6 7 8 9 10

3. Temporal Demand: How much time pressure did you feel due to the rate or pace at which the task or task 

elements occurred? Was the pace slow and leisurely or rapid and frantic?

LOW |---|---|---|---|---|---|---|---|---| HIGH

1 2 3 4 5 6 7 8 9 10

4. Level of Effort: How hard did you have to work (mentally and physically) to accomplish your level of 

performance?

LOW |---|---|---|---|---|---|---|---|---| HIGH

1 2 3 4 5 6 7 8 9 10

5. Level of Frustration: How insecure, discouraged, irritated, stressed and annoyed versus secure, gratified, content, 

relaxed and complacent did you feel during the task?

LOW |---|---|---|---|---|---|---|---|---| HIGH

1 2 3 4 5 6 7 8 9 10

6. Performance: How successful do you think you were in accomplishing the goals of the task set by the

experimenter (or yourself)? How satisfied were you with your performance in accomplishing these goals?

LOW |---|---|---|---|---|---|---|---|---| HIGH

1 2 3 4 5 6 7 8 9 10
25

Appendix C. Simulator Sickness Questionnaire 

 

This appendix appears in its original form, without editorial change.
26

Instructions: Please indicate how you feel right now in the following areas, by circling the 

word that applies. 

1. General Discomfort None Slight Moderate Severe

2. Fatigue None Slight Moderate Severe

3. Headache None Slight Moderate Severe

4. Eye Strain None Slight Moderate Severe

5. Difficulty Focusing None Slight Moderate Severe

6. Increased Salivation None Slight Moderate Severe

7. Sweating None Slight Moderate Severe

8. Nausea None Slight Moderate Severe

9. Difficulty Concentrating None Slight Moderate Severe

10. Fullness of Head None Slight Moderate Severe

11. Blurred vision None Slight Moderate Severe

12. Dizzy (Eyes Open) None Slight Moderate Severe

13. Dizzy (Eyes Closed) None Slight Moderate Severe

14. Vertigo*

 None Slight Moderate Severe

15. Stomach Awareness** None Slight Moderate Severe

16. Burping None Slight Moderate Severe

*

Vertigo is a disordered state in which the person or his/her surroundings seem to whirl dizzily: giddiness. ** Stomach awareness is usually used to indicate a feeling of discomfort which is just short of nausea.

Are there any other symptoms you are experiencing right now? If so, please describe the symptom(s) and rate 

its/their severity below. Use the other side if necessary.
27

Appendix D. Scoring Procedure for the Simulator Sickness Questionnaire 

 

This appendix appears in its original form, without editorial change.
28

Symptoms scored 0 (None) - 3 (Severe)

Nausea (Raw) - Sum of General discomfort, increased salivation, sweating, nausea, diff 

concentrating, stomach awareness, burping 

Nausea sub scale = Nausea (Raw) x 9.54

Oculomotor - Sum of general discomfort, fatigue, headache, eye strain, diff focusing, diff 

concentrating, blurred vision 

Oculomotor sub scale = Nausea (Raw) x 7.58

Disorientation - Sum of diff focusing, nausea, fullness of head, blurred vision, dizzy 

(eyes open), dizzy (eyes closed), vertigo 

Disorientation sub scale = Nausea (Raw) x 13.92

TSS = [Nausea (Raw) + Oculomotor (Raw) + Disorientation (Raw)] x 3.74
29

List of Symbols, Abbreviations and Acronyms

2-D two dimensional

3-D three dimensional

ANCOVA analysis of covariance 

IR infrared

IVD indirect-vision driving 

NASA-TLX National Aeronautics and Space Administration - Task Load Index

SD stereoscopic display

SSQ Simulator Sickness Questionnaire 

SUGV small unmanned ground vehicle 
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