Building a Driving Simulator with Parallax Barrier Displays

Christoph Schinko

, Markus Peer

, Daniel Hammer

, Matthias Pirstinger

, Cornelia Lex

Ioana Koglbauer

, Arno Eichberger

, Jürgen Holzinger

, Eva Eggeling

, Dieter W. Fellner

and Torsten Ullrich

Fraunhofer Austria Research GmbH / Institut für ComputerGraphik & Wissensvisualisierung,

TU Graz, Graz, Austria

Institut für Fahrzeugtechnik, TU Graz, Graz, Austria

AVL List GmbH, Graz, Austria

Keywords:

Driving Simulator, Parallax Barrier, Optimization.

Abstract:

In this paper, we present an optimized 3D stereoscopic display based on parallax barriers for a driving sim-

ulator. The overall purpose of the simulator is to enable user studies in a reproducible environment under

controlled conditions to test and evaluate advanced driver assistance systems.

Our contribution and the focus of this article is a visualization based on parallax barriers with (I) a-priori

optimized barrier patterns and (II) an iterative calibration algorithm to further reduce visualization errors in-

troduced by production inaccuracies. The result is an optimized 3D stereoscopic display perfectly integrated

into its environment such that a single user in the simulator environment sees a stereoscopic image without

having to wear specialized eye-wear.

1 INTRODUCTION

A parallax barrier is a device placed in front of a dis-

play enabling it to show stereoscopic, or multiscopic

images without the need for the viewer to wear 3D

glasses. Multiscopy differs from stereoscopy in that it

displays multiple angles at once, allowing a viewer to

see the content from different angles – not just a left-

eye / right-eye angle. The parallax barrier consists of

a material with a set of precision slits, or a translucent

material with an opaque pattern, allowing each eye to

see a different set of pixels. With this separation of

the pixels it is possible to create an autostereoscopic

display – a display without the need for glasses. This

effect is illustrated in Figure 1.

The intended purpose for the parallax barrier tech-

nology in this context is an autostereoscopic display

system for a driving simulator. It consists of a mod-

iﬁed MINI Countryman chassis with eight liquid-

crystal displays (LCDs) mounted around windscreen

and front side windows. Four 55inch LCDs are placed

radially around the hood of the car in a slanted an-

gle. Two 23inch LCDs are used for each of the two

front side windows. The four LCDs in the front are

equipped with parallax barriers made of 2cm thick

acrylic glass to minimize strain caused by the slanted

angle. Each barrier is printed with a custom-made

striped pattern, which is the result of an optimiza-

tion process. The displays are connected to a cluster

of four “standard” computers with powerful graph-

ics cards. In order to account for movement of the

driver’s head inside the car, an eye-tracking system

from SmartEye consisting of two cameras with in-

frared ﬂashes is installed on the dashboard of the

car. The position-depended rendering of the simula-

tion scenario is performed on a cluster using the In-

stantReality framework.

Figure 1: An autostereoscopic display can be realized us-

ing a parallax barrier. The barrier is located between the

eyes (visualized in blue and red) and the pixel array of the

display. It blocks certain pixels for each eye, which results

in the eyes seeing only disjoint pixel columns (at least in

an optimal setting). If the display is fed with correct image

data, the user sees a stereo image. The free parameters (to

optimize) are its line distance l

and its line width l

Schinko, C., Peer, M., Hammer, D., Pirstinger, M., Lex, C., Koglbauer, I., Eichberger, A., Holzinger, J., Eggeling, E., Fellner, D. and Ullrich, T.

Building a Driving Simulator with Parallax Barrier Displays.

DOI: 10.5220/0005711302810289

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 1: GRAPP, pages 283-291

ISBN: 978-989-758-175-5

283

2 RELATED WORK

2.1 Teaching, Learning, Testing in a

Safe Environment

It is easy to understand why simulators have become

such a critical tool for researchers and engineers in

many ﬁelds of application. They not only consti-

tute an excellent means of research for understanding

human behavior, but also are an effective and cost-

saving tool for designing human-machine-interaction

interfaces (Eugen, 2008). Driving simulators for re-

search and development are common today, for many

reasons. The main advantages of simulators are (Pinto

et al., 2008):

1. they provide the opportunity to study either rare or

dangerous situations, as well as the environment

and conﬁgurations that do not yet exist in the real

world;

2. the parameters relevant to the conditions under

study can be recorded and experimental setups

can be strictly controlled;

3. they save time and money.

In the context of driving simulators, the applica-

tions range from research on emergency maneu-

vers (Malaterre, 1995), driving in fog (Cavallo and

Pinto, 2001), and driver vigilance (Rogé et al., 2001)

to driver training (Flexman and Stark, 1987), (Farmer

et al., 1999), (Monin, 2004), dashboard design

(Champion et al., 1999), road ergonomics (Hoekstra

and van der Horst, 2000), and prototyping of lighting

systems (Dubrovin et al., 2000).

2.2 3D Displays & Parallax Barrier

As already established in the introduction, a graphical

display is autostereoscopic when all of the work of

stereo separation is done by the display (Eichenlaub,

1998), so that the viewer does not need to wear special

glasses.

Parallax barrier technology can be used to create

autostereoscopic displays. While the technology is

harder to apply for television sets, because of the re-

quirement for a wider range of possible viewing an-

gles, it is used in the Nintendo 3DS hand-held game

console and in various smartphones (Benzie et al.,

2007). Besides the use for 3D displays, the tech-

nology also allows to separate two views to use one

display at the same time. These dual-view displays

are, for example, used for the navigation system in the

2010-model Range Rover allowing driver and passen-

ger to view different content.

Parallax barrier displays are quite simple to pro-

duce, but they have a number of limitations. The

most important one is that they are limited to one user

whose position needs to be ﬁxed or is at least limited

to a few viewing spots that depend on the viewing an-

gle with respect to the display. In our scenario the

limitation to a single user is not a problem, since it is

limited to one user (the driver) anyway. The other part

is the fact that the viewer must remain in a ﬁxed posi-

tion. This drawback can be eliminated by virtually ad-

justing the pixel columns such that the separation re-

mains intact, as presented by (Sandin et al., 2001) and

by (Peterka et al., 2007). In order to accurately ad-

just the pixels, some kind of user tracking is needed,

which also limits the number of possible users. Most

consumer products containing autostereoscopic dis-

plays, however, just combine parallax barriers with

lenticular lenses. This approach does not put any con-

straints on the number of possible users at a time, but

is relatively restricted concerning the possible view-

ing position(s).

Since the user’s possible viewing positions are

very limited in our scenario anyway, there is no need

to adjust for large distance variations, for example, us-

ing a dynamic parallax barrier (Perlin et al., 2000). A

static setup of the two cameras used by the eye-tracker

is enough to cover the typical movement of a user.

Another drawback of the parallax barrier tech-

nique is that the resolution drops down to about a half

for just a single user. In the presented scenario, this is

not a critical point since the available resolution is suf-

ﬁciently with respect to the distance of the driver (four

displays each with a resolution of 1920 × 1080 pix-

els results in an overall resolution of more than eight

megapixels).

Finally, the loss of brightness caused by the par-

allax barrier is a limitation for many use cases.

Lanman et al. presented an approach for content-

adaptive parallax barriers to overcome this issues

by using an adaptive mask in a dual-stacked LCD

(Lanman et al., 2010). However, the real-time appli-

cation for dynamic content in a driving simulator is

ruled out due to the involved computational complex-

ity.

Glasses-free 3D Displays are not limited to use

parallax barrier technology. Recent research by

Wetzstein et al. provides alternatives to static par-

allax barrier solutions. One approach is based

on a light ﬁeld display using volumetric attenua-

tors (Wetzstein et al., 2011). Another approach uses

a family of compressive light ﬁeld displays employ-

ing a stack of time-multiplexed, light-attenuating lay-

ers (Wetzstein et al., 2012).

GRAPP 2016 - International Conference on Computer Graphics Theory and Applications

284

Figure 2: The result of the optimized display arrangement

has been the basis of the simulator construction. This Figure

illustrates the ﬁnal setup with four 55" front displays and

two 23" displays on each side.

3 PARALLAX BARRIER DISPLAY

CONFIGURATION

During the design of the driving simulator, the dis-

play conﬁguration has been optimized in a two step

procedure (see Figure 2).

3.1 Display Arrangement

The ﬁrst optimization step determines the best posi-

tion of each display. The displays have to be arranged

around a MINI Countryman, which is the basis of the

driving simulator, without interfering or penetrating

the vehicle’s bodywork. Furthermore, the displays

should obstruct the driver’s view as much as pos-

sible in order to avoid distracting parts of the non-

simulation environment being visible.

The optimization uses a cylindrical rendering as

a cost function: the cameras are placed at the posi-

tion of the driver’s eyes, occluding geometry by the

vehicle is supplied with a black / non-visible mate-

rial, the displays are black / non-visible as well, and

the whole scene is placed in surrounding, illuminat-

ing sphere in cyan and yellow – for each eye sepa-

rately. The geometric setting is illustrated in Figure 3,

while the result of the cost function of the optimal so-

lution is shown in Figure 4. Each non-black pixel is

a disturbing view past the displays. Minimizing the

number of such pixels, improves the overall simulator

experience.

Figure 3: In order to optimize the display arrangement, the

displays’ visibility has been analyzed using cylindrical ren-

derings from the driver’s position.

Figure 4: Using the setting illustrated in Figure 3 with oc-

cluding vehicle geometry and displays rendered in black,

and the environment rendered in cyan and yellow for each

eye separately, the cost function can be minimized easily:

Each non-black pixel is a disturbing view past the displays.

These pixels should be removed. For ease of understanding

Figure 3 is overlaid semi-transparently.

3.2 Pattern Optimization

The ﬁrst step of the optimization of the display con-

ﬁguration determined the best position of each dis-

play. The second step uses the display position and

the driver’s position in order to optimize the barrier

pattern. In detail, the barrier pattern is a set of lines,

which is printed on 2cm thick acrylic glass in the front

of each display. The pattern is described by three pa-

rameters; i.e. the line width l

, the distance between

two consecutive lines l

measured between medial

axes, and their deviation from a vertical alignment l

The cost function of the second optimization routine

depends on these three parameters. In each evaluation

a geometric scene is generated (including the vehicle,

the displays, and the line of the barrier pattern). For

each scene, the rays – from the position of the driver’s

eyes to each display pixel – check the visibility of a

display pixel for each eye.

The result for each pixel can be: (i) visible by both

eyes, (ii) visible by left eye only, (iii) visible by right

eye only, or (iv) not visible at all. The objective func-

tion simply counts the number of pixels, which can

be seen by both eyes, or which cannot be seen at all

(i.e. which are not separable). More details on this op-

timization process have been published in (Eggeling

et al., 2013).

The optimization returns the optimal conﬁgura-

tion. Unfortunately, the manufactured driving simu-

lator differs signiﬁcantly from its speciﬁcation:

• As the displays and the barriers are arranged in

an inclined plane, sagging effects in the order of

millimeters occur.

• The printed parallax barrier pattern has not the

required precision; that is, the lines are up to

0.15mm (+12.4%) wider than speciﬁed.

• The refractive index of the acrylic glass has not

been considered in the planning stage.

Building a Driving Simulator with Parallax Barrier Displays

285

While the ﬁrst problem has been ﬁxed with additional

mountings, the second and the third problem have

been solved via the calibration described below.

4 PARALLAX BARRIER DISPLAY

CALIBRATION

Due to the already mentioned deviations of the man-

ufactured acrylic glasses from the ideal ones, addi-

tional calibration steps became necessary. We opted

for a calibration solution on-site because of possi-

ble inaccuracies in the direct measurement of display

and parallax barrier parameters, and the inability to

make exact measurements of the distance from the

display’s pixel array to the parallax barrier – at least

without disassembling the displays. Also we need to

take inﬂuences of the environment, like lighting con-

ditions and heat development of all participating com-

ponents, into account.

To establish a ground truth, we need accurate mea-

surements of a number of display and barrier param-

eters. Some parameters are scalar values (e.g. dis-

tance between two consecutive lines of a parallax bar-

rier l

), while others are vectors (e.g. the global po-

sition of the parallax barriers and the displays) that

need to be registered to a global coordinate system. A

ﬁrst idea to measure vector-valued parameters using

a robotic arm was soon discarded due to the intricate

construction of the mounting frame for the displays

and the resulting inaccessibility of the corner points.

A second idea to use a tracked (using the eye-tracker)

laser distance measuring unit was discarded as well

due to line of sight problems.

The solution we opted for is an indirect system

using a digital video camera. Since we already have

initial values from direct measurements and the digi-

tal construction of the driving simulator, we can opti-

mize the parameters by evaluating a video feed from

a camera. In this way, we do not optimize and cali-

brate the display parameters directly, but we calibrate

the end result.

This calibration is done for each display sepa-

rately. It requires a custom mounting for the camera to

be able to account for horizontal rotation. Therefore,

the optical center of the camera was measured using

a Panosaurus device from Gregwired. Afterwards we

created a matching mounting to be able to rotate the

camera horizontally around it’s center (initial tests re-

vealed that no vertical rotation is necessary). Attach-

ing the mount in the driving simulator was no difﬁcult

task, since we already had a mounting for the calibra-

tion pattern of the eye-tracking system. We modiﬁed

it for the camera mount to be placed exactly in the

position of left and right eye (separated by 65mm).

Our parallax barrier display calibration consists of

several steps that are designed to be performed one

after the other.

4.1 Video Mask Determination

In a ﬁrst step, we render two images and capture them

with the video feedback loop. One image is com-

pletely black; the other one is completely white (see

Figure 5, left). We perform this step in order to au-

tomatically create a mask, which is used in all subse-

quent steps to restrict the calibration to the display’s

area and to ignore irrelevant parts of the video images.

For each pixel the mask stores a value between zero

and one indicating whether the pixel shows a part of

the display (1.0) or not (0.0 otherwise). The result is

shown in Figure 5, right.

Figure 5: The parallax barrier display calibration only uses

clipped / weighted video input. The corresponding mask

is generated in the ﬁrst step. Although the automatically

generated mask is sufﬁcient, we advise to use a manually

clipped mask as shown on the right hand side. (The oc-

cluder in the upper right part of the images is the MINI’s

interior mirror).

4.2 Color Calibration

The next step is to perform a single color test to com-

pare input colors (sent to a display) with output col-

ors (captured by the video camera) for a conﬁgurable

amount of colors (256 per default) spread evenly in

the RGB color space. A smaller example is shown

in Figure 6. This step is done in order to take color

deviation into account. Color deviation occurs due to

non-calibrated hardware and due to color shifting ef-

fects caused by looking at the displays at an angle.

The error function of the color calibration is the sum

of per-pixel differences weighted with the normalized

mask of the previous step.

4.3 Display Calibration

The third and the fourth steps are the optimization of

the barrier parameters: we start in the third step with

the sampling of the parameter space using the theo-

retical parameters (the values the parameters should

GRAPP 2016 - International Conference on Computer Graphics Theory and Applications

286

Figure 6: The color calibration step is performed to re-

duce hardware-based color shifting effects. These 15 im-

ages show the captured results of fully saturated colors with

varying hue. In our setting the color red shows exceedingly

few shifting effects. Consequently, it is the selected color

for the following calibration steps.

have according to the construction plan) extended to

a sufﬁciently large interval. The fourth step uses the

best conﬁguration of the third step and performs a

ﬁne-tuning using a minimizing optimization routine.

In both steps we use the following setup. We start

with a parameter vector, i.e., the assignment of all

variables (barrier line width l

, barrier line orienta-

tion l

, barrier line vertical offset l

, etc.). Based on

these values, a test image is calculated. It shows a

black pixel, if the pixel is visible from the eye posi-

tion at which the camera is currently positioned. Oth-

erwise a colored pixel (using the color determined in

the color calibration step) is shown. Having calcu-

lated the test image, our calibration tool displays the

image and captures the video image to check the re-

sult. The goal is to have a completely black image.

Consequently, the error function simply counts the

number of non-black pixels weighted with the cali-

bration mask.

While the third step is a sampling of the multidi-

mensional parameter space to obtain uniformly dis-

tributed samples (see Figure 7, left), the fourth step

uses the same error function and the best sample in

order to ﬁne-tune the settings with a conjugate direc-

tion search routine (see Figure 7, right).

During several calibrations we made some inter-

esting observations:

• The parameters barrier line width l

and barrier

line distance l

are not independent (see Figure 1).

If the optimization routine does not have addi-

tional constraints, it returns a parameter setting,

which corresponds to a black test image and there-

fore in a black video captured image. We solved

this problem by introducing the linear constraint

2 · l

= l

, which we identiﬁed heuristically in a

manual optimization test.

• The optimization routine returns the same conﬁg-

uration for both eyes (with the numerical preci-

sion of the termination condition used by the op-

timization routine), although the calibrations have

been performed separately. Therefore, we have

not been confronted with the problem of handling

two different parameter settings and how to merge

/ combine them.

• The complete calibration is not optimized for

speed, but it runs completely automatically and

can be started at any step, if previous results are

available. In practice, the color calibration is per-

formed once, for one eye position only. Also it is

also possible to directly deﬁne a staring set of par-

allax barrier parameters to skip the time consum-

ing sampling step in the second stage and directly

start with the optimization routine.

4.4 Parameter Tuning

With the optimized set of parameters at hand, the ﬁrst

tests with real persons using the eye-tracking data re-

vealed problems with ghosting effects, thus the pixel

separation was not ideal. Having ruled out any prob-

lems in the visualization pipeline, we encountered

two problems.

1. The size of a pixel with respect to the line width

of the barrier is rather large. Depending on the

viewing angle, it is possible to have a pixel classi-

ﬁed as visible by one eye, while nearly half of the

pixel is visible for both eyes.

2. We also found the eye-tracking data to be the

source of inaccuracy – the two camera system is

struggling with partial occlusion and/or bad light-

ing conditions.

A solution to overcome these issues is to virtually in-

crease the parameter value of the barrier line width.

Figure 7: The display calibration optimizes the parameter

conﬁguration using a sampling of the multidimensional pa-

rameter space to obtain uniformly distributed samples and

a directed conjugate search routine. On the left hand side

the Figure shows a bad conﬁguration captured during the

sampling stage. The right hand side shows a good, ﬁnal

conﬁguration.

Building a Driving Simulator with Parallax Barrier Displays

287

This way the virtual barrier becomes larger and more

pixels are considered as non-separable. They are vis-

ible by both eyes or not visible at all. This solution

reduces ghosting at the cost of resolution and bright-

ness (as non-separable pixels are rendered black). The

reduced resolution was observable in driving scenar-

ios with detailed models and textures. We did not en-

counter any problems concerning brightness, because

the environment in the driving simulator is very dark.

4.5 Runtime

Visualizing a scene in the driving simulator is handled

by the InstantReality framework. Several components

work hand in hand to incorporate user tracking as well

as autostereoscopic rendering. User tracking is car-

ried out by a SmartEye eye-tracking system with two

cameras mounted at the base of the A-pillar on the

dashboard and inside the left ventilation vent of the

co-driver. This placement ensures minimal occlusion

caused by the steering wheel and the necessary degree

of inconspicuousness for drivers to not feel observed.

The mandatory infrared ﬂashes however are clearly

visible (see Teaser Figure). The eye-tracking system

provides head position and rotation as well as the po-

sition of left and right eye. These values are needed

to calculate the correct perspective projection and to

take the parallax barrier into account using a custom

InstantReality node.

In detail, the perspective projection is directly

handled by the InstantReality framework, while all

parallax barrier calculations are performed using a

multi-pass shader pipeline. In a ﬁrst step, the images

for left and right eye are rendered off-screen into two

textures. A fragment shader in the following pass per-

forms intersection tests (viewing ray ↔ parallax bar-

rier) at runtime from both eyes to all display pixels for

the classiﬁcation of the pixels (visible by both eyes,

visible by left eye only, visible by right eye only, not

visible). With the classiﬁcation of the pixels at hand,

the corresponding values from the textures are used to

create the ﬁnal image. Pixels visible by both eyes as

well as pixels not visible are set to black.

For the simulation part of the driving simulator,

the visualization is a black box for visualizing a scene

graph. All updates are sent using the External Author-

ing Interface of the InstantReality framework. The vi-

sualization system on the other hand is not dependent

on the simulation.

5 EVALUATION

The complete optimization (including video mask de-

termination, color calibration, display calibration, and

parameter tuning) ran fully automatically and needed

approximately 2

hours per display. The ﬁnal bar-

rier parameters returned by the calibration routine has

led to an error of 14%–16%; i.e. approximately one-

seventh of all pixels show a wrong color for at least

one eye. These values have been used in a ﬁnal user

study with 21 test persons. Each test consisted of

different, combined driving situations including in-

ner city scenarios, overland tours and high-speed mo-

torway driving. The participants answered several

questions during the test phase and afterwards. The

questionnaire consisted of the standardized simulator

sickness questionnaire (Kennedy et al., 1993), gen-

eral questions about the overall system and speciﬁc

questions on relevant subsystems such as visualiza-

tion, physical simulation, haptic realism of the force-

feedback devices, acoustics, etc. In the following sub-

sections we focus on the overall impression including

simulator sickness and visualization speciﬁc aspects.

5.1 Visualization System

The visualization system has a large inﬂuence for the

overall experience with the driving simulator. While

some preliminary tests have been conducted using 2D

visualization, the majority of the scenarios was tested

in 3D. Since it is not easily possible to remove the

parallax barriers, the participants had to deal with the

parallax barriers during tests in 2D as well.

On a scale from unrealistic to realistic, 16 out

of 21 participants (about 76%) evaluated the visual-

ization system as being rather realistic or realistic.

Three participants deemed the visualization system to

be unrealistic. Two of them had problems with ghost-

ing effects and projection offsets which are caused by

problems of the eye-tracking system. The last partic-

ipant of the three was not experiencing these effects,

but suffered from general discomfort.

With the same scale, 13 out of 21 participants

(about 62%) experienced the representation of the en-

vironment (i.e. the modeled 3D scene) as rather real-

istic or realistic. One of the participants voted with

unrealistic. The participant was one of the three ex-

periencing the visualization system as unrealistic.

From the 21 participants, 20 (about 95%) an-

swered neutral or positive when asked to rate the driv-

ing simulator towards usefulness – 15 of them rated

positive. The remaining participant was the one suf-

fering from general discomfort.

A total of 14 participants (about 67%) would deﬁ-

nitely partake in another study using this driving sim-

ulator; two participants would not. When asked to

recommend the driving simulator to other drivers, 17

GRAPP 2016 - International Conference on Computer Graphics Theory and Applications

288

(about 81%) participants would do so. One partici-

pant answered negative when asked which impression

of the driving simulator other drivers would get.

Answering open questions, one participant

deemed the 3D visualization as being the most useful

part of the driving simulator. On the other hand,

participants had problems with dizziness, ghosting

effects, the parallax barrier lines as well as stiﬂing air.

One participant struggling with ghosting effects had

difﬁculties in perceiving speed with the 2D visualiza-

tion, but had a better perception in 3D. We also had a

suggestion to change the color of the parallax barrier

lines from black to some color with less contrast.

Regarding the choice of the preferred visualization,

we had an almost neutral result between 2D and 3D.

5.2 Simulator Sickness Questionnaire

Simulator sickness and motion sickness result in

feelings of nausea, dizziness, vertigo, and sweating

(among other symptoms). Simulator sickness is gen-

erally the result of the discrepancy between simulated

visual motion and the sense of movement stemming

from the vestibular system (Balk et al., 2013). In

many simulators, the visual system receives informa-

tion that suggests movement (e.g., roadway scenes

passing by the viewer), yet the vestibular system in-

terprets a stationary status. It is this discrepancy that

causes simulator sickness in many people.

There are many different ways to assess simula-

tor sickness. The most popular way is the Simulator

Sickness Questionnaire (SSQ) by R. S. Kennedy et

al. (Kennedy et al., 1993). This questionnaire asks

participants to score 16 symptoms (of three general

categories: oculomotor, disorientation, and nausea)

on a four point scale (0-3). Weights are assigned to

each of the categories and summed together to obtain

a single score.

The average values of our driving simulator us-

ing parallax barrier techniques in the categories ocu-

lomotor, disorientation, and nausea are listed in Ta-

ble 1 (evaluated according to Fisher et al (Fisher et al.,

Table 1: The driving simulator using parallax barrier has

been evaluated using the Simulator Sickness Question-

naire (Kennedy et al., 1993). In order to interpret the scores

we include comparative values for non-stereoscopic dis-

plays (Virtual 2D) and stereoscopic displays using shutter

techniques (Virtual 3D) (Häkkinen et al., 2006).

Parallax Virtual Virtual

Barrier 2D 3D

nausea 34.9 11.8 29.9

oculomotor 49.8 14.0 26.9

disorientation 79.5 21.1 41.1

2011)). The Table includes comparative values pub-

lished by Häkkinen et al. (Häkkinen et al., 2006),

who investigated “Simulator sickness in virtual dis-

play gaming: a comparison of stereoscopic and non-

stereoscopic situations”.

6 CONCLUSION & FUTURE

WORK

This article describes the construction, design, opti-

mization, calibration and evaluation of a parallax bar-

rier on a driving simulator.

6.1 Contribution

This work presents a real system by a selective exam-

ination of speciﬁc aspects embodied in the system. To

the best of our knowledge, our driving simulator is the

ﬁrst one with parallax barrier stereoscopic displays.

While each single component has been investi-

gated before (Eggeling et al., 2013), the combina-

tion and interaction of all parts has been a challenge.

Therefore, the description of the lessons learned (see

Section 3. and 4.) are a valuable contribution – espe-

cially, the approach to use a video feedback loop and

the calibration routine to compensate for

• hardware inaccuracies and

• problems due to inaccessible displays, respec-

tively intricate direct measurements.

6.2 Beneﬁt

When faced with the question which visualization

technique to be used in the context of a simulator en-

vironment, our evaluation gives decision support: it

allows a reader to assess parallax barrier techniques.

Some aspects (hardware costs, etc.) can be estimated

in advance, while other ones (user acceptance, simu-

lator sickness, etc.) cannot be estimated beforehand.

Based on the “in advance estimation”, we have cho-

sen the parallax barrier technique mainly due to its

reduced costs.

Concerning user acceptance, the evaluation uses

the “standardized” Simulator Sickness Question-

naire (Kennedy et al., 1993) and relates the scores

of the barrier approach with shutter-based, stereo-

scopic solutions. Unfortunately, the parallax barrier

approach is still not the ﬁrst choice considering simu-

lator sickness.

Building a Driving Simulator with Parallax Barrier Displays

289

6.3 Future Work

A question of future research is what to do with the

pixels that are visible by both eyes. Preliminary tests

using a blend of both images for these pixels resulted

in signiﬁcant ghosting effects. These effects are no-

ticeable in areas of high frequency (i.e. with large

differences in the pixel values of the two images), but

are less noticeable in homogeneous regions. An idea

would be to use the pixels only for these areas to ob-

tain more overall brightness.

With the availability of the gaze direction pro-

vided by the eye-tracking system it is possible to re-

strict the parallax barrier calculation to a certain re-

gion. The idea is to have a two-dimensional render-

ing for peripheral areas and do intricate barrier calcu-

lations only in an area around the gaze direction. This

kind of rendering may reduce the simulator sickness.

An open question in this context is whether there is

a need to predict the movement of the eye, or the up-

date rate of the eye positions (totally depending on the

eye-tracking system) is sufﬁciently high.

Especially in driving scenarios, dazzling effects

play an important role; they are difﬁcult to implement

using standard display hardware because of low dy-

namic range of the light source. Such High Dynamic

Range effects can be approximated using special ren-

dering techniques – but only to a limited degree.

ACKNOWLEDGEMENT

This work is part of the project “MueGen Driving”,

funded by the Austrian Ministry for Transport, In-

novation and Technology and the Austrian Research

Agency in the FEMtech Program Talents. Further-

more, it is supported by the government of the Aus-

trian federal state of Styria within the project “CUBA

– Contextual User Behavior Analysis”.

REFERENCES

Balk, S. A., Bertola, A., and Inman, V. W. (2013). Simula-

tor Sickness Questionnaire: Twenty Years Later. Pro-

ceedings of the International Driving Symposium on

Human Factors in Driver Assessment, Training, and

Vehicle Design, 7:257–263.

Benzie, P., Watson, J., Surman, P., Rakkolainen, I., Hopf,

K., Urey, H., Sainov, V., and von Kopylow, C. (2007).

A Survey of 3DTV Displays: Techniques and Tech-

nologies. IEEE Transactions on Circuits and Systems

for Video Technology, 17:1647–1658.

Cavallo, V. and Pinto, M. (2001). Does Speed Perception

Indeed Fog up as Visibility Drops? Proceedings of the

International Conference Vision in Vehicles, 9:308–

314.

Champion, A., Mandiau, R., Kolski, C., Heidet, A., and

Kemeny, A. (1999). Trafﬁc Generation with the

SCANER II Simulator: Towards a Multi-Agent Ar-

chitecture. Proceedings of Driving Simulation Con-

ference, 5:1–15.

Dubrovin, A., Lelevé, J., Prévost, A., Canry, M., Cherfan,

S., Lecocq, P., Kelada, J. M., and Kemeny, A. (2000).

Application of Real-Time Lighting Simulation for In-

telligent Front-Lighting Studies. Proceedings of Driv-

ing Simulation Conference, 6:32–38.

Eggeling, E., Halm, A., Fellner, D. W., and Ullrich, T.

(2013). Optimization of an Autostereoscopic Display

for a Driving Simulator. Joint Conference on Com-

puter Vision, Imaging and Computer Graphics Theory

and Applications, 8:318–326.

Eichenlaub, J. B. (1998). Lightweight compact 2D/3D au-

tostereoscopic LCD backlight for games, monitor, and

notebook applications. Stereoscopic Displays and Vir-

tual Reality Systems, 5:180–185.

Eugen, T. (2008). Use and validation of driving simulators.

Doctoral Theses at NTNU (Norwegian University of

Science and Technology).

Farmer, E., Reimersma, J., Moraal, J., Jorna, P., and

Van Rooij, J. (1999). Handbook of Simulator-Based

Training. Ashgate Publishing.

Fisher, D. L., Rizzo, M., and Caird, J. K. (2011). Handbook

of Driving Simulation for Engineering, Medicine, and

Psychology. Crc Press Inc.

Flexman, R. P. and Stark, E. A. (1987). Training simulators.

Handbook of Human Factors, 1:1012–1037.

Häkkinen, J., Pölönen, M., Takatalo, J., and Nyman, G.

(2006). Simulator sickness in virtual display gaming:

a comparison of stereoscopic and non-stereoscopic

situations. Proceedings of the conference on Human-

computer interaction with mobile devices and ser-

vices, 8:227–230.

Hoekstra, W. and van der Horst, A. R. A. (2000). New road

designs tested in a driving simulator. Proceedings of

Driving Simulation Conference, 6:1–6.

Kennedy, R. S., Lane, N. E., Berbaum, K. S., and Lilienthal,

M. G. (1993). Simulator Sickness Questionnaire: An

enhanced method for quantifying simulator sickness.

Journal of Aviation Psychology, 07:203–220.

Lanman, D., Hirsch, M., Kim, Y., and Raskar, R. (2010).

Content-adaptive Parallax Barriers: Optimizing Dual-

layer 3D Displays Using Low-rank Light Field Fac-

torization. ACM Trans. Graph., 29(6):163:1–163:10.

Malaterre, G. (1995). Comparisons between simulation and

actual driving situations: Some experiments. Proceed-

ings of Driving Simulation Conference, 2:60–76.

Monin, C. (2004). TRUST3000: The truck simulator for ad-

vanced continuous professional training. Proceedings

of Driving Simulation Conference, 8:389–399.

Perlin, K., Paxia, S., and Kollin, J. S. (2000). An Au-

tostereoscopic Display. Proceedings of the annual

conference on Computer graphics and interactive

techniques, 27:319–326.

GRAPP 2016 - International Conference on Computer Graphics Theory and Applications

290

Peterka, T., Kooima, R. L., Girado, J. I., Ge, J., Sandin,

D. J., and A., D. T. (2007). Evolution of the Varrier

Autostereoscopic VR Display: 2001–2007. Stereo-

scopic Displays and Virtual Reality Systems, 14:1–11.

Pinto, M., Cavallo, V., and Ohlmann, T. (2008). The devel-

opment of driving simulators: toward a multisensory

solution. Le travail humain, 71:62–95.

Rogé, J., Pebayle, T., and Muzet, A. (2001). Variations of

the level of vigilance and of behavioral activities dur-

ing simulated automobile driving. Accident Analysis

and Prevention, 33:181–186.

Sandin, D. J., Margolis, T., Dawe, G., Leigh, J., and A.,

D. T. (2001). The Varrier Auto-Stereographic Dis-

play. Stereoscopic Displays and Virtual Reality Sys-

tems, 8:1–8.

Wetzstein, G., Lanman, D., Hirsch, M., and Raskar, R.

(2012). Tensor displays: Compressive light ﬁeld syn-

thesis using multilayer displays with directional back-

lighting. ACM Trans. Graph., 31(4):1–11.

Wetzstein, G., Lanman, D., W., H., and R., R.

(2011). Layered 3D: Tomographic image synthesis for

attenuation-based light ﬁeld and high dynamic range

displays. ACM Trans. Graph., 30(4):95:1–95:12.

Building a Driving Simulator with Parallax Barrier Displays

291