FCV2016, the 22nd Korea-Japan Joint Workshop on Frontiers of Computer Vision

Visibility Study on Design Pattern of Car Tail Lamp
Using Perceptual Sensitivity on
Face Recognition Abilities
Kensuke TOBITANI, Kae NAKAJIMA. Kenji KATAHIRA, Katsuhito NISHIJIMA and Noriko NAGATA
School of Science and Technology / Research Center for Kansei Value Creation, Kwansei Gakuin University
Email: tobitani@kwansei.ac.jp
Abstract—In recent years, there has been concern about
collision accidents involving vehicles driving at nighttime. In
order to prevent such accidents from occurring, it is necessary todevelop tail lamps with high visibility. In our study, we examined
how differences in tail lamp design affect visibility with a view
to developing a tail lamp design that offers better visibility,
thereby enhancing safety. Focusing on visibility in human facerecognition, we conducted an impression-rating test and visual
search task using rear shots of vehicles. The results revealed thata human’s impression of a rear shot of a vehicle is structurally
similar to their impression of a face, and that the tail lamp design
affects search time.

I. I NTRODUCTION
There has been a growing demand in recent years forpreventive safety technology designed to prevent or mitigate
accidental damage; for example, automatic emergency braking(AEB) systems. In the EU, AEB became mandatory for
all newly launched commercial vehicles in 2013[1]. It is
expected that not only AEB mitigating accidental damage but
various other preventive safety technology, such as airbags,
will become a mandatory international standard.
Against this backdrop, we aimed to propose a rear designfor vehicles that offers high visibility with a view to preventing
or mitigating rear-end collisions. To this end, we focused on
how humans react speciﬁcally to the face of another human.
Research has shown that an angry expression is detected
quicker than a happy expression. It would therefore be possible
to create a high visibility rear design by incorporating theperceptual superiority of a human’s angry expression into thedesign - particularly that of the tail lamps where increasinguse of LED’s is creating greater design freedom - and thus
enable the prevention of rear-end collisions.
II. P RECEDING R ESEARCH
Regarding the research on visibility, there have been manystudies on visual displays such as revealed characters andmarkers /signs. Visibility of the character displays has beenassessed primarily in terms of readability, and discussions
have focused on the inﬂuence of character size, luminance,and color, and their relationship with the reader’s age orrecognition characteristics. For example, using a mock driving
scenario in which LED signs of varying colors were shown toparticipants, Yamamoto et al. investigated how reaction times
and the way in which colors are seen varied between the

different colors[2]; and Oda et al. investigated the various
luminous intensities and installation positions of attentionarousing visual guide lights[3]. Both studies were conducted
more from a practical perspective than an academic perspective. There have been a number of studies on the inﬂuenceof environment and the inﬂuence of color; such studies haveapproached the intrinsic “visibility” of visual signs primarily
from a visibility perspective. For example, Iizuka et al. examined color perception in heavy fog[4]. Tests were conductedusing a fog generator, and the results demonstrated that theluminosity and color intensity of the apparent color declined
in tandem with increasing fog concentrations. Visual displays
have been used to examine the visual conspicuity of LED
lights. Such research has demonstrated that the inﬂuence ofcolor is greater at night compared to day. Research has also
shown that in their peripheral vision, humans are good atrecognizing the presence of blue lights and the shape of red
lights. There has been a plethora of research on the conspicuityof different colors or combinations of colors[5][6], but none ofthese conspicuity studies focused on LED placement patterns
or design.
On the other hand, with regard to research on human facerecognition, a study examined the recognition characteristics
humans exhibit when looking at a face and those whenlooking at another object, and it was reported that the cerebral
activity is different in each case[7]. In fact, one study whichused Event-Related Potentials (ERP) to compare the cerebral
activity during face recognition and object recognition reported
that N170, an ERP component that responds over occipitotemporal electrode sites around 170 ms after stimulus presentation, responded signiﬁcantly. The study also reported aninversion effect only in response to a face stimulus[8]. Morton
and Johnson proposed two cognitive models, CONSPEC andCONLERN, which explain the human face-speciﬁc response
from a developmental psychology perspective[9]. CONSPEC
is an instinctual cognitive system that guides the recognitionof the basic human face structure found in newborn infants.
The above ﬁndings suggest the possibility that when viewing avehicle rear design that elicits the instinctual face recognition,
the recognition characteristics would tend to be similar to thatwhen viewing a face. Furthermore, Ohman et al. conducted
tests where participants were asked to rate their impressionof visual stimuli consisting of simple schematic faces with-356-

modiﬁcations to individual features (shape of eyebrows, eyes
and mouth, direction of gaze, etc.) Based on the results,
Ohman et al. drew up a schematic face recognition model[10].
In this model, they categorized faces with negative features
as “threatening,” and faces with more symmetrical features
as “friendly faces.” They then set participants a visual searchtask using the facial schematic stimuli. The results indicatedthat search times were shorter when viewing threatening facial
stimuli which convey feelings of anger, compared to friendly
face stimuli. This ﬁnding demonstrated the perceptual superiority of angry expressions[11]. In view of the above ﬁndings,
we posited the hypothesis that an anger-evoking vehicle rear
design, particularly with respect to the positioning of tail lamp
LED’s, will have high visibility. To validate this hypothesis,
we set an subjective evaluation experiment and visual searchtask using vehicle rear designs as stimulus material.
III. S UBJECTIVE E VALUATION E XPERIMENT OF R EAR
D ESIGN
The purpose of this study was to experimentally examine
and then propose a highly visible vehicle rear design. To
this end, it was necessary to investigate people’s impression
structure with respect to vehicle rear designs. We therefore
conducted tests focusing on the emotions associated withvarious vehicle rear designs. In this chapter, we describe four
tests that we conducted to build the impression structure.
A. Test 1: Impression Word Extraction Test
1) Purpose: To build an impression structure, it is necessary to use a range of impression words (adjectives) for
rating/describing vehicle rear designs. Therefore, by meansof a free descriptive answer-based questionnaire, we collecteda variety of impression words rating vehicle rear designs,
particularly tail lamp designs.
2) Method: The test was conducted on ﬁve university students in their 20’s (mean age: 23.0; age range: 22-24). For the
stimulus material, we prepared 3DCG images based on vehicle
rear design images. If we had used photographed images, theparticipants may have formed a judgment based not only on
the tail lamp but on the entirety of the vehicles morphologicalelements. Therefore, in order to simulate a nighttime driving
scenario, we dimmed the luminosity of morphological elements outside the tail lamps so that participants would focus on
the tail lamps while maintaining recognition of the overall rear
design. Regarding the procedure for preparing the stimulus
material, in order to obtain contemporary impression words,
we gathered 70 photographed images of newly launchedvehicles, selected from this pool 10 apparently typical tail lamp
designs, and then prepared 10 3DCG images based on thesephotographs. The ten 3DCG images we prepared are shownin Figure III-D1.
The participants were asked to write down their impressionof this stimuli in a free descriptive format.
3) Results: We extracted 235 impressions words. We theneliminated words with overlapping meaning, which left 125
words. We then divided these 125 words into two categories:(a) Sample A (b) Sample B (c) Sample C (d) Sample D (e) Sample E(f) Sample F (g) Sample G (h) Sample H (i) Sample I(j) Sample J

Fig. 1. 3DCG Images of Rear Designs

words describing the participants’impressions of the designof the tail lamp or vehicle, and words describing the emotion
conveyed by the tail lamp design. As the focus of the study wason the emotions conveyed by tail lamp design, we eliminatedthe words in the former category.
4) Discussion: The test results demonstrated that whengiving their ratings, the participants were comprehensively
assessing a variety of information, including the shape of thetail lamps, their position, and the distance between the left
and right tail lamp. The results also yielded a plethora of
impression words, ranging from words describing the shapeof tail lamps such as “big,” “round,” “spaced far apart,” towords describing the emotion associated with the shape suchas “smiling,” “in a good mood,” and “sad-looking.”
B. Test 2: Impression Word Appropriateness Test
1) Purpose: Using the impression words obtained from theprevious test, we conducted a test to obtain words that would
be appropriate to use for rating tail lamps.
2) Method: The test was conducted on ten university students in their 20’s (mean age: 22.8; age range: 22-24). We
used a total of 106 words for this test, including the words
yielded from the impression word extraction test as mentioned
previously, and also words taken from preceding research on
people’s impression of schematic faces and vehicles.
The participants were shown the words in a random orderand they were asked to rate each word in terms of whether or
not it ﬁts their impression of tail lamps. The participants rated
each word on a seven point scale (1: Very inappropriate; 2:Inappropriate; 3: Rather inappropriate; 4: Not sure either way;
5: Rather appropriate; 6: Appropriate; 7: Very appropriate).
When brieﬁng the participants about the content of the test,
we presented them with the ten 3DCG images in Figure III-D1
as speciﬁc examples of vehicle design.
3) Results: The ratings were scored as follows: “1: Veryinappropriate” = -3 points; “2: Inappropriate” = -2 points; “3:Rather inappropriate” = -1 point; “4: Not sure either way” = 0
points; “5: Rather appropriate” = 1 point; “6: Appropriate”= 2 points; “7: Very appropriate” = 3 points. As for thecriteria we used to qualify rating words, we decided that wewould adopt words with mean score of 1.0 or higher and astandard deviation of less than 1.5. Based on these criteria, we
ultimately adopted 49 words as “rating words.” Table 1 lists
the rating words.
4) Discussion: By conducting an impression word extraction test and then an impression word appropriateness-357-

We performed multi-dimensional scaling (MDS) using thetwo variables deﬁned above, interpreted the impression structure of tail lamp visually, and also performed a Ward’s methodbased hierarchical cluster analysis on the rating word groupused in the study. In this way, we determined the key words.
4) Results: Using MDS with D (psychological distance)as the variable, we mapped the rating words on a twodimensional plane. The results of this mapping are shown in
Figure 2.

TABLE IA DOPTED I MPRESSION W ORDS
Impression Wordarrogant
dubiousmajestic
bad-tempered
frowning
weird
smirking
pudgy
uneasyslight smile
craggyangry
severeunnaturalsullenacrimonious
strangeglaring
stupideasefulunconﬁdent
scaryclumsygrinning
cold

Average
2.8
2.3
2.4
2.5
2.5
2.5
2.2
1.7
2.4
2.4
2.9
2.9
2.9
1.9
2.6
1.4
2.1
1.9
2.2
1
1.7
2.6
2.1
2.2
1.7

SD
0.42
0.48
0.52
0.53
0.53
0.53
0.63
0.67
0.70
0.70
0.74
0.74
0.74
0.74
0.84
0.84
0.88
0.88
0.92
0.94
0.95
0.97
0.99
1.03
1.06

Impression Word
gloomyfriendly-looking
offensive
smilingodd
surprisedrelaxedexpressionless
warm
calm
oppressive
amiablelonelyimpressive
joyfulconﬁdent
sad-looking
sleepystrictin a good moodpleasant
beaming
stiff
slender

Average
1.5
1.9
2.2
2.2
2
1.6
1.6
1.6
1.2
1.8
2.2
2
1.3
2.3
1.6
1.1
2
1.6
1.5
1.1
1.9
2
1.3
1.7

SD
1.18
1.20
1.23
1.23
1.25
1.26
1.26
1.26
1.32
1.32
1.32
1.33
1.34
1.34
1.35
1.37
1.41
1.43
1.43
1.45
1.45
1.49
1.49
1.49

2

slendersleepytest, we extracted from a diverse pool of impression words
diverse and valid rating words that would be appropriate forrating/describing tail lamps.

gloomyunnatural
expressionless

0.5

smirking-1.5
grinning
warm-1
pleasantrelaxedsmilingbeamingin a good mood

-0.5

0

-0.5

slight smile
easefulamiablecalm
joyfulfriendly-looking

1) Purpose: In order to systematically ascertain the various
psychological states involved in impressions of tail lamps, weestimated the impression structure of tail lamps by focusing
on the distances between these rating words extracted from theprevious two tests. We used degree of similarity to calculate
the distances between the rating words, and we used a cluster
analysis on groups of analogous rating words. In this way, we
determined “key words” to use in the subjective evaluation
experiment.
2) Method: The test was conducted on ten university students in their 20’s (mean age: 23.0; age range: 22-24).
We presented the participants with pairs of rating words
and asked them to rate, using a two-point scale, whether thewords accurately represented their impression of tail lamps.
We presented the participants with the ten 3DCG images
in Figure 3 as speciﬁc examples of tail lamps. Each of the
49 image rating words extracted from the impression word
extraction test and impression word appropriateness test werepaired with all the other words in a round-robin fashion,
making a total of 1,176 rounds.
3) Analysis: If the rate at which a given word i is judged toconvey an analogous impression is deemed the concordance
rate Ri , this concordance rate may be used to express thedegree of similarity Xi of given word i with other words in
49 vectors as shown in Formula (1) below.
(1)

Furthermore, psychological distance Dij from rating word
j can be deﬁned according to the formula below.
1
2
49
21 
(Rik − Rjk )2(2)
Dij =3
49

cold

0
-2

C. Test 3: Rating Word Distance Measurement Test

k=1

clumsyweird

1stupidXi = (Ri1 , Ri2 , · · · , Ri49 )sad-looking
dubiouslonelyunconfident
surpriseduneasystrange

odd 1.5

confident

0.5

1

scary
severestrictoffensive
stiff
oppressive

impressive-1

1.5

2

sullenbad-tempered
acrimonious
frowning
angry
craggyglaring
arrogant
majestic

pudgy
-1.5

-2

Fig. 2. Mapping of Rating Words

5) Discussion: Looking at the horizontal axis, “angry,”“sullen,” and “bad-tempered” have high values, whereas “smiling,” “slight smile,” and “friendly-looking” have low values.
Based on this ﬁnding, we surmised that the horizontal axis is ascale of comfort (comfortable - uncomfortable). Furthermore,
given that when at the same time looking at the vertical axis,“sad-looking,” “surprised,” and “weird” have high scores, we
surmised that the vertical axis is a scale of activity (high low). These axes are typically seen in the results of general
research about human emotion, which implies that our test
results have validity.
Next, in order to determine the key words, we performeda hierarchical cluster analysis (Ward’s method) and hierarchically categorized the 49 rating words. Using the resulting
hierarchical structure, we determined the number of clusters.
The criteria for determining the appropriate number of clusters
was that the analogous rating words in each cluster should haveno semantic incongruity and that they should be as consistent
as possible. Following this procedure, we categorized the 49
words into three clusters. We named the cluster containing
words such as “smiling,” “relaxed,” and “pleasant” the “happy
cluster.” We named the cluster containing words such as “arrogant,” “glaring,” and “majestic” the “angry cluster.” Finally, we
named the cluster containing words such as “odd,” “slender,”and “sad-looking” the “sad cluster.”-358-

(a) Stimulus A(b) Stimulus B(c) Stimulus C(d) Stimulus D(e) Stimulus E(f) Stimulus F(g) Stimulus G(h) Stimulus H

Fig. 3. Recreated Stimulus Material

We then selected 15 words from among these three clusters
to use as key words. We deﬁned “key word” as a rating wordthat can encapsulate/cover the semantic space of its cluster.
Accordingly, we calculated the distance of each rating wordfrom the center of its cluster, sorted the words in order ofcloseness to center, and adopted the words that were in the top
three rankings in their each cluster. In addition, we performeda principal component analysis (PCA) on the rating words
within each cluster, and adopted (in addition to top 3 words)
the highest and lowest values in the ﬁrst principal component
axis (PC1). When doing so, we performed an outlier detection
test based on Mahalanobis distance, and removed from thepool of candidate key words rating words judged to be outliers.
Table 2 shows the adopted key words in each cluster.

3. These stimuli were presented in an LCD monitor 1.6 meters
away from the participants. We set the size of the presented
images such that the participants would view the vehicle as if
it was 30 meters away from them. This is the stopping distanceof a vehicle travelling at 60km/hour on a dry asphalt road.
The test was conducted in a darkened room, and thestimulus material was presented on an LCD monitor (EIZO
ColorEdgeCG210). The LCD monitor was recalibrated foreach test.
The participants were shown the images in a random order,
and they were asked to rate how accurately each of the 15 keywords describe the image shown. The participants rated eachword on a seven point scale (1: Very accurate; 2: Accurate; 3:Rather accurate; 4: Not sure either way; 5: Rather inaccurate;
6: Inaccurate; 7: Very inaccurate).
3) Results: We took the mean scores for the 15 persons andperformed factor analysis based on the principal factor method
and varimax rotation. The criteria for deciding the number offactors was set at 1 or over of eigenvalue. Table 3 shows thefactor loadings for each adjective as per the factor analysis.
According to the factor analysis, the cumulative contribution
ratio is 81.070%, and the eigenvalue is 1.045 for the totalfactors/variables up to Factor 3.
TABLE III
FACTOR A NALYSIS R ESULTS
Key words
smiling
pleasantsmirking
joyfulsad-looking
friendly-looking
majestic
strictacrimonious
severebad-tempered
weird
dubiousgloomyexpressionless

TABLE II
T HE S ELECTED K EY W ORDS

Close to centroids
Max PC1 score
Min PC1 score

Happy cluster
pleasant
smiling
joyfulfriendly-looking
smirking

Angry cluster
strictsevereacrimonious
majestic
bad-tempered

Sad cluster
expressionlessdubious
sad-looking
gloomyweird

D. Test 4: Subjective Evaluation Experiment
1) Purpose: Test 4 was designed to clarify the tail lamp impression structure derived from a speciﬁc constituent elementof a vehicle rear design, namely, tail lamp shape using the key
words determined in Tests 1, 2 and 3. Therefore, we conductedsubjective evaluation experiment based on the 3DCG images
as shown in Figure , but controlling for the morphologicalelements outside the tail lamp. The aim in doing so was todetermine the stimulus material to use in the visual searchtask described in the next chapter.
2) Method: The test was conducted on 20 university students in their 20’s (mean age: 22.2; age range: 20-24).
We recreated stimulus material, focusing on the curvatureof the tail lamps. Speciﬁcally, we made a total of eight rear
designs. We based these designs on tail lamp shapes, and weonly modiﬁed them by changing the direction of the convex
side. In addition, in order to increase the focus on tail lamp
shape, we eliminated curvature in parts outside the tail lamps
and darkened the areas outside the tail lamps. The eight
designs we used for the stimulus material are shown in Figure

Factor 1
.896
.866
.861
.833
-.720
.624
-.133
-.199
-.099
-.343
-.579
.030
.029
-.396
-.070

Factor 2
-.295
-.246
-.291
-.320
-.221
-.317
.921
.897
.798
.754
.601
.042
.256
-.085
.021

Factor 3
.016
-.105
.117
-.093
.150
-.481
.017
.040
.085
.225
.224
.791
.708
.616
.044

Factor 4
.012
-.169
.026
-.047
.235
.221
.040
.029
-.053
.057
-.108
.011
.027
.134
.534

4) Discussion: We interpreted the factors as follows. We
determined that Factor 1 is “happy,” given that the factor
loadings for “smiling, “smirking,” “pleasant,” and “joyful”
were high, at .600 and over. We determined that Factor 2
is “angry,” given that the factor loadings for “strict,” “acrimonious,” “severe,” “majestic,” and “bad-tempered” were
high. We determined that Factor 3 is “scary,” given that“suspicious,” “weird,” and “gloomy” were high. As for Factor
4, it only consisted of “expressionless,” and so we deemed it
an independent variable.
Next, we plotted the three factors as two-dimensional graphs
and then mapped the stimulus material on these graphs in orderto determine the impression structure of vehicle tail lamps (seeFigure 4).
According to the scatter plot with Factor 1 as the x-axis andFactor 2 as the y-axis, the rear designs that have high Factor 2scores, speciﬁcally, “strict,” “acrimonious,” and “severe,” arethe designs shown in Stimulus C and Stimulus G. This ﬁnding

-359-

0.5

1.5

Factor 2
Smulus C

Factor 3

0.5

IV. V ISUAL S EARCH TASK

Factor 3

Smulus F

Smulus F

Smulus A

0.4

Smulus A

0.4

1

0.3

0.3

Smulus B

Smulus H

0.2

Smulus B

0.5

0.1

0.1

Smulus E
Factor 1-1-0.5

A. PurposeSmulus A

0
-1.5

0.2

0

0.5

1

Smulus F

1.5

-1

-0.5

Smulus E

Factor 2

0
-1.5

0

0.5

1

Factor 1

0

1.5
-1.5

Smulus H

-1

-0.5

0

0.5

1

1.5

We set a visual search task using the key images that weselected as described in the previous chapter. The purpose of
the task was to verify whether reaction time is affected by theemotion evoked by the stimulus material.

Smulus H

-0.1
-0.1

Smulus B
-0.5

-0.2
-0.2

Smulus D
Smulus D
Smulus E

Smulus G

Smulus C-0.3

Smulus C-0.3

Smulus D

-1

-0.4
-0.4

Smulus G

Smulus G

-0.5
-1.5

-0.5

(a) Factor 1 by Factor 2 (b) Factor 2 by Factor 3 (c) Factor 1 by Factor 3
Fig. 4. Factor Scores for the Stimuli

B. Method

reveals that tail lamp designs in which the convex side faces
outward or downward create an impression of anger.
Next, according to the scatter plot with Factor 1 as the xaxis and Factor 3 as the y-axis, the rear designs that havehigh Factor 1 scores, speciﬁcally, “smiling,” “pleasant,” and“joyful,” are the designs shown in Stimulus F and Stimulus
G. Stimuli F and G both have high Factor 1 scores, but the
Factor 3 scores are either positive or negative depending onwhether the convex side is facing inward or outward. Figure 5
presents a summary of how factor scores depend on directionof convex side.

The test was conducted on nine university students in their
20’s (six males, three females; mean age: 21.7; age range:
20-24)The stimulus material comprised the three types of key
images. Of these, we used the expressionless face as the distractor, and the friendly face and threatening face as the targets.
We set an image (hereinafter, “visual stimulus”) consistingof points directing in a circle subtending 3.44◦ visual angle
centered around the ﬁxation point, along which the key images
would be positioned. Figure 7 shows a target-present stimulus,
in which one of the key images within the visual stimulus
is the target, and a target-absent stimulus, which is entirelycomprised of distractors.

(a) Target-absent Visual Stimulus(b) Target-present Visual Stimulus

Fig. 7. Visual Stimuli

Fig. 5. Differences in Impression depending on Direction of Convex Side

Based on these results, we determined the rear design
images (key images) to use in the visual search task discussed
in the next subchapter (see Figure 6).

(a) Happy
face(friendly) (b) Angry (threatening) (c) Expressionless facefaceFig. 6. Key Images

In our selection of the key images, we selected Stimulus
G as a “happy (friendly) face,” Stimulus H as an “angry(threatening) face,” and Stimulus D as an “expressionlessface.”The participants were required to answer as quickly as
possible (by pressing a response key) whether or not the target
appears within the visual stimulus they were presented with.
We instructed the participants to press the response key with
the index ﬁnger of their dominant hand as quickly and as accurately as possible. We randomized and counterbalanced theleft-right allocation of the response key among the participants.
There were 240 rounds each for the target-present stimulus
and target-absent stimulus. In the case of the target-present
stimulus, there were six potential positions where the target
could appear. Because there were two types of target (friendly
face and threatening face), it was possible to obtain measurements for 20 rounds of a single visual stimulus per participant.
We presented the visual stimulli in a random order. Figure 8
shows the actual procedure in the test.
We set the size of the key images shown on the monitor
so that when viewed from 1.2 meters from the monitor,
they would correspond to the size of actual vehicles 30
meters ahead. We also used a chin support to control physicalmovement. The test was conducted in a darkened room, andthe stimulus material was presented on an LCD monitor (EIZO
ColorEdgeCG210). The LCD monitor was recalibrated foreach test.

-360-

Fixation

given that there was a main effect of target type, the results
imply that in vehicle rear designs, an angry expression has
perceptual preeminence as is the case with human faces.

Visual stimulus

+

Blank
Next trial

V. C ONCLUSION AND F UTURE O UTLOOK

500ms

+

Waiting for
response
1500, 2000
or 2500ms
Response

Fig. 8. Experiment Procedure

After being briefed, the participants waited in the darkened
room for ﬁve minutes so that their eyes would grow accustomed to the dark, undertook a practice session consisting of
24 rounds, and then undertook the actual test. To limit theburden on the participants, we set a rest period after the 240th
round.
C. Results

1

1200

0.95

1100

0.9

1000

0.85

900

Reaction time [ms]

0.8
0.75
0.7
0.65

R EFERENCES

800
700
600
500

0.6

400

0.55

(a) Response Accuracy

ft
en
fri

fri

en

dl

re

y

dl

y

rig

le

ft
le

ht

at

ig
tr
re
a

th

fri

dl
en
fri

en

y

dl

y

rig

le

ft

ht

ft
le

ht

at

rig

re
th

at
re
th

200

ht

300

0.5

th

Accuracy

The results of the test are shown in Figures 9. Regarding
values that pertain to improper/defaulted rounds or to rounds
where the reaction time was 3SD or more from that participant’s mean reaction time, we deemed such values as abnormal
values and excluded them from the analysis (excluded roundrate = 1.94%).

In this study, we examined how visibility varies according
to differences in rear design in order to enhance motoringsafety. We conducted tests with a view to proposing a tail lamp
design that offers better visibility. Speciﬁcally, focusing on
perceptual sensitivity in human face recognition, we conductedan subjective evaluation experiment and visual search taskusing images of vehicles.
The results revealed that a human’s impression of a rear
design of a vehicle is structurally similar to their impressionof a face, and that the tail lamp design affects search time. The
results also revealed that when a tail lamp design conveys anangry expression, the response time will be shorter comparedto tail lamp designs that convey other expressions. This ﬁnding
suggests that a vehicle tail lamp will have better visibility if
it evokes angry emotion.
An ongoing challenge concerns the design of the visual
stimulus used in visual search task. In particular, it will be necessary to further examine the inﬂuence of the morphologicalelements of rear design constituents other than the tail lamps.
Furthermore, we plan to verify the validity of our hypothesis
by using physiological indices in addition to behavioral indicessuch as response time.

(b) Response Time

Fig. 9. Experimental results

We subjected each of the results obtained to a two-factor
repeated measures ANOVA in which the two factors weretarget type and target position.
With regard to response accuracy, the ANOVA did notindicate any statistically signiﬁcant results for either factor.
With regard to response time, the ANOVA indicated a main
effect for target type (F(1,8) = 9.738, p < .005) but not fortarget position, and it did not indicate an interaction effect.
D. Discussion
Looking at response accuracy, the fact that there were no
statistically signiﬁcant results for either factor demonstrates
that in this visual search task, there was no discrepancy in
difﬁculty level among the target types or among the target
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