DETECTION OF HASTY STATE BY MEANS OF USING
PSYCHOSOMATIC INFORMATION
Masahiro Miyaji
1
, Kenji Takagi
2
, Haruki Kawanaka
2
and Koji Oguri
2
1
Institute of Information Sciences and Technology, Aichi Prefectural University, Nagakute, Aichi, 480-1198, Japan
2
Graduate School of Information Sciences and Technology, Aichi Prefectural University
Nagakute, Aichi, 480-1198, Japan
Keywords: Psychosomatic information, Haste state, Heart rate, Useful field of view, Driver monitoring, Preventive
safety, Advanced Driver Assistance System, Advanced Safety Vehicle, Intelligent Transport Systems.
Abstract: We introduced an Internet survey and analyzed driver’s psychosomatic state immediately before traffic
incident by using 7 models of traffic accidents. We identified driver’s hasty is one of key factors which may
result in traffic accidents. Aiming at the reduction of the number of traffic accidents, we studied to detect
hasty state of a driver while driving by way of using the psychosomatic signals of a driver, which were heart
rate and useful field of view. Finally we proposed a concept of a function for detecting driver’s states for an
intelligent drive support system.
1 INTRODUCTION
The number of traffic fatalities in Japan as of 2010
has declined lower than 5,000 for two years,
meanwhile the number of traffic injuries has still
remained some 0.8 million (Cabinet Office,
Government of Japan, 2011). Reducing the number
of road traffic accidents is said as one of major
challenges for the creation of sustainable mobility
society. It is said that the above reduction has been
brought by installation of airbag system and seatbelt
into vehicle, enhancement of crashworthiness of
vehicle, intensive enforcement of the Road Traffic
Law (forbidding drunken drive, etc.) and effective
education. Preventive safety may be one of
important measures to reduce the number of traffic
accidents, which have been studied globally in
academia and automobile industries and developed
into production vehicles such as electronic stability
control system, blind spot detection system, and pre-
crash safety system with functions that detect
direction of a driver’s face movement and eye
movement (Ohue, 2006). AS main cause of traffic
accidents is thought to human factors (Klauer, 2004),
driver’s state adaptive function into an intelligent
drive support system which detects driver’s
psychosomatic states may enhance performance to
lower a risk to be involved in a traffic accident.
Accident investigation and analysis is regarded
as effective for helping to reduce traffic accidents. It
is stated that in addition to investigation and analysis
of traffic accidents, other important issues include
understanding the human factors involved in traffic
accidents, as well as investigation and analysis of
traffic incidents to detect the signs of an imminent
accident (The expert committee for safety
engineering in Japan, 2000).
In our study we identified driver’s hasty is one of
key factors which may result in traffic accidents by
way of Internet survey where we applied 7 models
of traffic accidents used in third phase ASV
promotion program in Japan (Toji, 2006). When
driver is in a hasty state, we thought that it might
affect driver’s visual task of looking far forward and
peripheral vision, which causes limitation of useful
field of view (hereinafter; UFV) .
Previous study indicates that visual load affected
driving performance (Engström, 2005), and eye-
movement were highly sensitive to the demand of
visual tasks while driving (Victor, 2005). With
regard to UFV many studies have been carried out,
of which is statistically reported that both UFV and
mental states affected on crash frequency, which
indicates prediction of crash problems in the elderly
is available (Ball, 1993). Reduction of UFV by 40%
increases the potential risk of being involved in a
traffic accident (Owsley, 1998, Sims, 2000,
33
Miyaji M., Takagi K., Kawanaka H. and Oguri K. (2012).
DETECTION OF HASTY STATE BY MEANS OF USING PSYCHOSOMATIC INFORMATION.
In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, pages 33-40
DOI: 10.5220/0003723200330040
Copyright
c
 SciTePress

Allahyari, 2007). Relation between UFV and other
test have been statistically studies to apply on-road
testing (MYERS, 2000, Roge, 2005, CLAY, 2005).
Therefore we studied to detect the hasty state of
a driver while driving by means of using the
psychosomatic signals such as heart rate and UFV.
In order to establish the methodology to detect the
states, we used a mock-up type driving simulator
(hereinafter; DS). Finally we proposed a concept of
a function to detect driver’s psychosomatic states for
an intelligent drive support system.
2 INCIDENTS AND
INVESTIGATION AREA
2.1 Investigation Area
The methods used for previous analysis of traffic
incidents involved drive recorders, which are
triggered by sudden braking and therefore operated
in areas of apparent risk when an accident is
relatively close at hand. We expanded the survey
area shown in Fig. 1 to include an area of potential
accident risk. Traffic incidents are defined as
circumstances in which the driver’s vehicle seemed
likely to strike another vehicle or a pedestrian. As
previous analysis did not include analysis of driver’s
psychosomatic states, we collected information
concerning traffic incidents which occurred during
ordinary driving by means of a questionnaire survey,
regardless of whether or not the brakes were
operated. We defined seven traffic incident models
of potential risks as right turn, crossing path
collision, person to vehicle, head on collision, rear-
end collision, left turn and lane change, which were
used as traffic accident models in the Phase 3 ASV
Promotion Project of Japan in 2006 (Toji, 2006).
SafetyIndex
Rescue
system
Incidentssurveyedbydrive
recorders
Drive
support
system
Collision
damage
mitigation
Post‐
accident
safety
EVENT
Pre‐crash
safetysystem
Incidentsbasedonexperiencessurveyed
byquestionnaires
Apparent
Potential
riskarea
Information,
warningsystem
Figure 1: Investigation area
2.2 Investigation Method, Applicants
We introduced the preliminary survey to check any
problem of misunderstanding and wrong answer for
our intention on particular questions. We sent our
questionnaire to candidate applicants by controlling
our delivering system to avoid the deviation in
gender, age, drive career and residential area, which
were consisted of seven sections based on traffic
incident types, with 19 questions concerning driver
behaviours (no safety confirmation, inappropriate
assumption, careless driving, etc.) and ten questions
concerning psychosomatic states (haste, distraction,
drowsiness, etc.) immediately before a traffic
incident occurred per incident type. After an
explanation, applicants answered questions with
regard to traffic incidents which they encountered in
the last two or three years using a questionnaire that
included illustrations of accident types. A total of
2,000 subjects consisting of 1,117 male and 883
female between 19 and 69 years old were responded
for the web survey. The average age was 41.6 years
old and the average driving experience was 19.9
years. A 2.2% sampling error used statistical
formulas was found for the questionnaire items. This
sampling error value falls within the 1 to 3% range
used for investigations conducted by Japanese
government agencies and appears to be reasonable.
2.3 Traffic Incidents
Traffic incident types obtained by the Internet
survey were crossing path (17.5%), lane change
(15.9%), left turn (12.8%), right turn (12.1%), and
rear-end collision (10.7%) in descending order of
frequency, while those released by the National
Police Agency as of 2006 were rear-end collision
(31.3%), crossing path (27.0%), right turn (9.1%),
and pedestrian (8.8%). Results of the Internet survey
indicated that the seven incident models account for
86% of traffic accidents of real fields according to
report of Japanese Police Agency.
2.4 Psychosomatic State
Psychosomatic states immediately before traffic
incident obtained by Internet survey were haste
(22%), distraction (21.9%), and normal (15%) in
descending order of frequency shown in Fig. 2.
From the results, it is clear that haste and
distraction are key factors of psychosomatic states
immediately before traffic incident in consideration
of driving areas that hold potential risks for
accidents, and these factors should be addressed in
ICPRAM 2012 - International Conference on Pattern Recognition Applications and Methods
34

order to help reduce traffic accident.
0
10
20
Haste
Distractio
n
Normal
I
n
e
xperie
n
c
e
d
d
r
i
ve
r
D
r
owsine
s
s
Anger
Un
s
table e
m
oti
o
n
No
t
good
c
o
n
d
i
tio
n
Un
k
n
o
w
n
O
t
h
e
r
Figure 2: Driver’s psychosomatic states
3 REPRODUCTION OF
DRIVER’S HASTY STATE
When a driver is in a state of hasty by mental loads
such as rush driving to a destination, its influence
may appear in heart rate by acceleration of the
sympathetic nerve, and leads pupil dilating,
eventually, reducing the gaze angle of a driver.
Furthermore it may increase potential risk of being
involved in a traffic accident. Because concentrated
gaze angle a driver while driving may be related to
the decrease of UFV, capturing the changes of both
heart rate and UFV of a driver may have possibility
to detect driver’s hasty state.
3.1 DS and Experiment
We used a stereo camera based tracking unit (the
seeing machine’s faceLAB) shown in Fig.3, and DS
with a driving course of six scenes of traffic incident
shown in Fig.4 and Table1 for reproduction of
driver’s hasty states while driving.

faceLAB’s
Stereo Camera
Outsidemirror(right)
Outsidemirror(left)
Rearviewmirror
Figure 3: Driving simulator.
The stereo camera system with a 60 Hz frame rate
and an information processing unit of gaze angle and
direction was fitted at the top of the dashboard. The
DS was equipped with left and right outside mirrors
and a rear view mirror. A projector was installed in
the ceiling of DS to picture a driving course onto the
frontal screen. The distance of the driving course
was 1.2 kilo-meters. The number of the subjects was
5, which age was between 20 - 57 years old.
G
S
1. Blue sedan
2. Truck
3. Bicycle
4. White sedan
5. Child
6. Silver wagon
G
S
1. Blue sedan
2. Truck
3. Bicycle
4. White sedan
5. Child
6. Silver wagon
Figure 4: Driving course.
Table 1: Traffic incident scenes.
Scene No. Traffic incident scenarios
1
Blue sedan merges into the driver’s lane from
the right
2 Truck merges from the left and turns right
3
Bicycle suddenly appears from behind a car
and crosses the road from the left
4
White sedan merges into the driver’s lane
from the right
5
Child suddenly appears from behind a car and
crosses the road from the left
6
Silver wagon suddenly appears from behind a
fence and crosses the intersection from the left
After learning themselves with the DS for about
an hour, the subjects were instructed to run the
following sequences; twice ordinary driving, once
hasty driving, and 5 minutes rest in between each
driving. We instructed the subjects to drive faster for
the hasty driving duration than for the ordinary
driving duration in order to reproduce the hasty
driving state, where the subjects took safety actions
on their own judgment basis. Due to the
specification of the DS, in the event of an accident
such as a near-miss crash occurred with another
vehicle along the course, the driving was
compulsory stopped and the system promptly
returned and restarted the course from a nearby
position.
3.2 Gaze and Head Direction
The gaze and head direction are both output as
DETECTION OF HASTY STATE BY MEANS OF USING PSYCHOSOMATIC INFORMATION
35

vertical rotation “pitch angle
” components (Up
direction = Positive) and a lateral rotation “yaw
angle
” component (Rotation in a left direction =
Positive) as shown in Fig.5. Other information
obtained from the tracking unit included eye
position, gaze direction, head position, head
orientation angle, heart rate, driving time as well as
counter force of brake pedal.
Pitchangle
Pupildiameter
Yawangle
x

y
z
Figure 5: Pitch angle and yaw angle.
3.3 Heart Rate
We measured an electrocardiogram (hereinafter;
ECG) waveform by the monitor lead method,
involving standard limb lead (II) and measurement
with 3 chest electrodes as shown in Fig.6. Using a
poly-mate AP1000 (DIGITEX Lab. Co., Ltd), we
obtained ECG, and removed ripple noise from the
waveform by using 4th butter-worth band pass
filters, of which sampling rate was 60 Hz with 5
seconds time window.
ch1(‐)
ch3(E)
ch2(+)
Figure 6: Measurement of ECG.
3.4 Measurement of UFV and Gaze
Time
We analyzed a still picture from the recorded movie
of the tracking unit at the frame rate of about 20 fps
with regard to eye position, gaze direction, head
position, and head direction, driving time as well as
heart rate. When a moving object appeared in the
peripheral field, a extent of viewing angle of central
vision field in the width of ±6.5 degree (hereinafter;
13 degree) in lateral rotation moved to the moving
object and contact it instantly. We defined an
amount of change of gaze angle in yaw component
was as “UFV”, and an amount of the change of time
was defined as “gaze time”.
3.5 Calculation of UFV and Gaze Time
We calculated UFV and gaze time as follows. Firstly
the extent of viewing angle of central vision field
was put as 13 degree. When a part of moving object
is contacted in the viewing angle of central vision
field, the state of the driver is defined as onset of
gazing at the moving object. Then the extent of
viewing angle of central vision field is transformed
into (u
+
, v
+
) or (u
-
, v
-
) , which are coordinate of the
tracked picture by using formula (1), (2), (3), (4), (5)
and (6). The subscripts used in the formula are
defined as following; r indicates datum of right eye,
l indicates that of left eye. c
r
and l
r
indicate
reliability factor of output datum of the tracking
system (faceLAB) for right eye and left eye
respectively. e indicates the position of eyeball. p
indicates coordinates of the gaze point. λ indicates
constant number. M indicates the matrix of
projective transformation.
(
)
(
)
()()
()()
()()
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎝
⎛
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
−−
−
−−
+
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
−−
−
−−
+
=
+
ll
l
ll
l
rr
r
rr
r
lr
cc
cc
θφ
θ
θφ
θφ
θ
θφ
cos13cos
sin
cos13sin
cos13cos
sin
cos13sin
1
d
(1)
(
)
(
)
()()
()()
()()
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎝
⎛
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
+−
−
+−
+
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
+−
−
+−
+
=
−
ll
l
ll
l
rr
r
rr
r
lr
cc
cc
θφ
θ
θφ
θφ
θ
θφ
cos13cos
sin
cos13sin
cos13cos
sin
cos13sin
1
d
(2)
edp +=
++
λ
(3)
edp +=
−−
λ
(4)
[
]
[
]
TT
11
++++++
≈
zyxi
pppvu M
(5)
[
]
[
]
TT
11
−−−−−−
≈
zyxi
pppvu M
(6)
Then the coordinate of moving object in the
tracked picture is put as (u
ob
, v
ob
). When u
ob
complies a condition in the formula (7), a state of the
driver is defined to gaze at the moving object shown
in Fig.7.
+−
≤≤ uuu
ob
(7)
Centralvision
u
u
v
u
+
u
MovingObject
Figure 7: Relation ship between central vision and gaze of
a driver.
ICPRAM 2012 - International Conference on Pattern Recognition Applications and Methods
36

Secondly t
ap
is defined as appearance time when
a moving object appears in the peripheral field,
is
defined as yaw angle in lateral component of
driver’s gaze direction at that time. Meanwhile tat is
defined as gaze time when the driver gazes at the
moving object. Accordingly
is defined as yaw
angle in lateral component of driver’s gaze direction
at that time. Furthermore T [sec] is defined as gaze
duration of time between appearance time of the
moving object and gazing time (hereinafter; gaze
duration), and change of gaze angle is defined as
[degree] as UFV followed by formula (8) and (9).
Hereafter we used the indexes to examine driving
time, heart rate and UFV.
apat
ttT
−
=
(8)
apat
φφ −=Φ
(9)
4 RESULTS OF EXPERIMENT
4.1 Driving scene of Traffic Incident
Six driving scenes of ordinary driving and hasty
driving were indicated in Fig.8. A blue rectangle in
the picture indicates viewing angle of central vision
field in lateral rotation, which was derived from the
yaw angle of gaze direction of the tracking unit. Red
rectangle indicates that of head direction. Each
picture includes superposed five frames of blue and
red rectangle, which was superposed by past two
frames to post two frames including present frame
for the capture of the both movement of blue
rectangle and red rectangle closely. The moment
when the moving object contacted the vertical dotted
lines such as u
+
, or, u
-
in Fig.7 was defined as onset
of gazing. We measured appearance time of the
moving object (t
ap
), and gazing time (t
at
) by means of
analyzing the yaw angle data of the recorded video
picture on manual procedure basis.
The measurement results for the gaze time and
UFV of subject E are shown in Table 2 for the six
traffic incident scenarios. For almost cases in hasty
driving for the gaze to turn to moving objects was
found to have tendency that are closer to the driver
than in ordinary driving.
Table 2: Gaze time and UFV (subject E).
No.
Ordinary driving Hasty driving
Gaze
duration
(sec)
Eccentricity
(deg)
Gaze
duration
(sec)
Eccentricity
(deg)
1 1.72 27.8 0.81 30.6
2 0.65 35.3 2.72 3.1
3 0.05 35.9 1.81 17.4
4 0.05 -7.5 0.00 -7.6
5 0.00 25.1 0.00 30.7
6 2.38 21.4 2.16 27.2
4.2 Heart Rate and Drive Time
Results of the change of heart rate between hasty
driving and ordinary driving was shown in Fig. 9.
No. Normal Haste Driving
1
2
3
4
5
6
Figure 8: Measurements results for subject E.
Heart rate in hasty driving was higher than ordinary
driving in all the subjects. The results agreed with
previous study (Kahneman, 1969). Furthermore the
counter force of brake pedal in hasty driving
indicated stronger than that of ordinary driving. This
verified that the subjects were fallen in the state of
tense by hasty driving.
75.0
80.0
85.0
Ordinary
Driving
Hasty
driving
Figure 9: Heart rate change (Unit: beats/min).
DETECTION OF HASTY STATE BY MEANS OF USING PSYCHOSOMATIC INFORMATION
37

Results of the change of total driving time
between hasty driving and ordinary driving were
shown in Fig.10. When the subjects ran a hasty
driving, the driving time became shorter than that of
ordinary driving.
150
200
250
Ordinary
Driving
Hasty
driving
Figure 10: Driving time (Unit: sec).
4.3 Counter Force of Brake Pedal
Results of the change of counter force of brake pedal
between hasty driving and ordinary driving shown in
Table 3.
Table 3: Counter Force of Brake Pedal.
Subject
Ordinary driving
(kgf)
Hasty driving (kgf)
A 22.0 57.1
B 22.1 43.4
C 18.5 32.7
D 11.8 27.3
E 19.2 27.9
Counter force of brake pedal of the all subject
increased in all scene of the hasty driving. The
above result indicates the subjects had tendency to
take stronger braking to avoid unintended collision
when they recognized the moving objects
encountered in the hasty driving. From the results, it
is said that the method of this study reproduced
hasty driving in the driving course
4.4 Gaze Duration and UFV
Results of the change of gaze duration and UFV
(indicated as Eccentricity) between hasty driving
and ordinary driving shown in Table 4. UFV of the
all subject decreased in all scene of the hasty
driving. Accordingly gaze duration was mostly
prolonged in hasty driving except subject C. It is
said that the potential risk of being involved in a
traffic accident increases when a driver runs a hasty
driving because of decreasing concentration to
surroundings of the vehicle caused by a narrower
UFV. From the results capturing the change of UFV
may indicates a potential risk of being involved in a
traffic accident.
Table 4: Gaze time and Eccentricity.
Subject
Ordinary driving Hasty driving
Gaze
duration
(sec)
Eccentricity
(deg)
Gaze
duration
(sec)
Eccentricity
(deg)
A 1.46 16.8 1.67 14.7
B 1.40 16.0 1.59 14.4
C 1.55 18.2 1.29 10.6
D 0.25 26.4 0.81 23.9
E 0.81 24.3 1.25 16.9
5 POTENTIAL DRIVE\ING
SUPPORT SYSTEM
We identified the relationship in a particular
combination among driver’s behaviour, driver’s
psychosomatic states and expected intelligent safety
systems derived from our survey shown in Table 5.
To illustrate the function, for example, a driver’s
psychosomatic state monitoring system could detect
a state of hasty driving as well as insufficient
recognition of a driving environment. Upon
detection, the system could provide appropriate
information to the driver, to give warnings, or to
intervene in the driver’s operation in order to help
minimize the risk of incidents in combination with
information surrounding the vehicle provided by the
surrounding monitoring system. If the driver’s
psychosomatic state is normal but driver makes
inappropriate assumptions while driving, traffic
safety information from road infrastructures such as
represented by ITS services (VICS in Japan) and the
driving safety support system (DSSS in Japan) could
be employed. The realization of intelligent drive
support systems activated by detecting driver’s
psychosomatic states is expected as one means to
help minimize road traffic safety risks.
ICPRAM 2012 - International Conference on Pattern Recognition Applications and Methods
38

Table 5: Expected intelligent safety system.
Driver’s
behaviours
Driver’s
psycho-
somatic states
Expected intelligent safety system
In-appropriate
assumption
Normal
(1)
Providing information from the roadside
infrastructure
a)
ITS services (AHS)
b)
Driving Safety Support Systems (DSSS)
c)
Traffic information collected by prove
cars
Haste (2)
Monitoring of surroundings
Distraction a)
Pre-crash safety system
No safety
confirmation
Haste b)
Night view system
Distraction c)
Rear-end monitoring system
Desultory
driving
Distraction
Drowsiness
d)
Side-view monitoring system (Blind spot
monitoring)
(3)
Driver psychosomatic states monitoring
Not look ahead
carefully
Distraction
a)
UFV detection
b)
Driver’s drowsiness detection
c)
Driver’s distraction detection
From the results, it is clear that in addition to
providing support for recognizing potential risks in
the driving environment during ordinary driving,
future intelligent drive support systems could detect
driver’s psychosomatic information in real time, and
effectively provide support for correct driving
decisions and carry out intervention into vehicle
control system. Fig. 11 shows the functional concept
for such an integrated intelligent drive support
system.

Vehicle Control
System
Vehicle
A
Traffic
Environment
Driver
IntelligentDriving
C
B
Judgment/Prediction
Operation
Detection
Detection
D
Recognition
Figure 11: Intelligent drive support system.
The system works as follows;
A. Detect and estimate risk factors in the
environment.
B. Detect and estimate a state of the driver
with regard to driver’s behaviour and
psychosomatic states (hasty driving).
C. Estimate the reliability of the driver's
decision concerning risk (presence of
human error).
D. Evaluate the driver capacity for
receiving information and warnings. If
a driver’s capacity is insufficient or the
danger exceeds the human ability to
react, the intelligent drive support
system intervenes, either via the
vehicle control system or directly, to
operate the vehicle safety systems.
6 SUMMARY, FUTURE ISSUES
We introduced Internet based survey with regard to
traffic incidents and identified driver’s
psychosomatic states while driving. Then we studied
the method to detect driver’s psychosomatic states
by means of measuring the change of heart rate in
ECG and UFV. The following was revealed;
• Internet survey using questionnaire may be
one of effective means to collect information
with regard to traffic incidents
• Hasty driving is one of key factors of human
errors which likely being involved in traffic
accidents.
• Hasty driving may be detected by means of
capturing the change of heart rate and UFV.
• Driver’s psychosomatic states adaptive
intelligent drive support system may have
potential ability to help minimize the
potential risks of encountering traffic
accidents such as hasty driving as well as
driver’s distraction.
Future issues include further enhancing the
performance of detecting driver’s hasty driving by
means of introducing three dimensional visual field
tracking unit to detect the distance of the moving
object and improving the method of determining of
the onset of the gazing as well as realization of s
driver’s hasty driving monitoring function for the
intelligent drive support system for the reduction of
the number of traffic accidents.
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