Motion SIckness in Automated Vehicles

Abstract

In an automated vehicle, the driver is a passenger. It is said that the driver seldom gets motion sickness or carsickness compared to passengers. This suggests that motion sickness could be a problem in highly automated vehicles. Other changes such as seating posture or engaging in non-driving tasks can occur by introducing automated driving. In addition to such changes for drivers, changes in vehicle systems occur that are caused by the automated system taking control of the vehicle. This paper discusses motion sickness in automated driving by dividing the changes for drivers and those in vehicles. The countermeasures are also discussed from both viewpoints with emphasis on a vehicle control method that minimizes motion sickness based on a mathematical model of motion sickness.

Full text

1 INTRODUCTION
The technological aspects of automated vehicles
or self-driving cars have been actively studied in re-
cent years. The human factors in automated vehicles
have also been studied. These research studies in-
clude a driver’s response when the driving authority
is being transferred back to the driver (Louw et al.
2015)(Blanco et al. 2015) and the driver’s trust in
automated driving (Abe et al. 2015).
In this study, the automated vehicle was assumed to
be operated at level 3 or higher in SAE-J3016 levels
of automated driving, in which the driver does not
need to attend to the vehicle control or even monitor
the traffic environment. In automated vehicles, the
driver becomes a passenger. It is known that drivers
seldom get carsickness compared to passengers.
Thus, it is thought that the drivers of automated ve-
hicles will tend to experience motion sickness more
often.
Originally, automated vehicles began to be devel-
oped to increase the driver’s comfort and productivi-
ty during transportation (Diels & Bos 2015). An in-
crease in the occurrence of motion sickness during
automated driving has a significantly negative im-
pact on this original purpose. However, few studies
have been performed on motion sickness in auto-
mated driving. Diels et al. discussed factors relating
to motion sickness for automated driving in specific
potential scenarios involving a change for drivers
and proposed countermeasures for decreasing the
occurrence of motion sickness (Diels & Bos 2015).
Sivak et al. estimated the number of passengers who
would experience carsickness during automated
driving based on survey results about activities that
people would like to do in an automated vehicle as
well as some potential countermeasures (Sivak &
Schoettle 2015). In addition to such changes for
drivers, the vehicle system also undergoes certain
changes that are caused by the automated system
taking control of the vehicle.
Therefore, this paper discusses motion sickness in
automated driving by dividing the changes for driv-
ers and those in the vehicles. The countermeasures
will be also discussed from both viewpoints. A vehi-
cle control method that minimizes motion sickness
will be introduced based on a mathematical model of
motion sickness. An individual who is released from
vehicle operation is referred to as an “occupant” in
this paper even if he/she is expected to take over the
driving operation from the vehicle automation when
necessary.
2 QUANTIFICATION OF MOTION SICKNESS
INCIDENCE IN AUTOMATED VEHICLES
When drivers are relieved of the driving operations
in automated vehicles, the following two changes
occur:
1) The individual who previously drove the car is
transformed into a passenger;
Motion Sickness in Automated Vehicles
T. Wada
Ritsumeikan University, Kusatsu, Shiga
ABSTRACT: In an automated vehicle, the driver is a passenger. It is said that the driver seldom gets motion
sickness or carsickness compared to passengers. This suggests that motion sickness could be a problem in
highly automated vehicles. Other changes such as seating posture or engaging in non-driving tasks can occur
by introducing automated driving. In addition to such changes for drivers, changes in vehicle systems occur
that are caused by the automated system taking control of the vehicle. This paper discusses motion sickness in
automated driving by dividing the changes for drivers and those in vehicles. The countermeasures are also
discussed from both viewpoints with emphasis on a vehicle control method that minimizes motion sickness
based on a mathematical model of motion sickness.
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2) The person who controls the vehicle is replaced
by the automation.
The possibility of motion sickness in those riding
in an automated vehicle is discussed based on these
two points (Table 1).
Table 1 Potential effects of automated vehicle on
motion sickness
What is changed Examples of chang-
es
Possible risk factors
of motion sickness
Changes
for drivers
caused by
being re-
lieved of
the driving
operation
Difference by
presence or
absence of
performing
the driving
operation
Difference between
driver and passen-
gers
(control, activity,
visual information,
anticipation)
- Decrease of opera-
tion activity and an-
ticipation
Changes in posture
and body move-
ments (e.g., Seating
in rear-facing posi-
tion)
- Decrease in antici-
pation of road ge-
ometry ahead
Increase of
non-driving
tasks
Addition of non-
driving tasks
(reading documents,
tablet operation,
etc.)
- Decrease in antici-
pation due to lack of
visual information
Gazing at an in-
vehicle display or
tablet
- Sensory conflict by
inconsistency be-
tween the passen-
gers body movement
and what they see
- Decrease of antici-
pation due to ab-
sence of visual in-
formation from the
road environment
Changes in
the vehi-
cle sys-
tem
caused by
the auto-
mation
taking
control of
the vehi-
cle
Changes in
vehicle mo-
tion
Driving velocity pat-
tern in curves
- Vehicle motion af-
fects motion sick-
ness
Changes in in-
vehicle
equipment
Changes in the size
of the front window
- Narrowing front
visual image
- Decrease in antici-
pation of the vehicle
motion
2.1 Changes for drivers: Transformed into
passenger
(a) Motion sickness susceptibility difference be-
tween driver and passenger
Rolnick & Lubow (1991) reviewed the factors con-
tributing to motion sickness that differ between
drivers and passengers, namely, controllability, per-
ceived control, activity, visual information, and pre-
dictability. The perceived control represents whether
the driver feels he/she is in control of the vehicle;
thus, it is thought to be identical to sense of agency
(David et al. 2008). In a real-world automotive envi-
ronment, the controllability and perceived control
are identical. It is thought that increasing these fac-
tors reduces motion sickness (Reason 1978b). In the
automated driving, the drivers are relieved of the
control operation and become a passenger; thus, the
susceptibility to motion sickness can be increased
(Rolnick & Lubow 1991)(Reason 1978b). Activity
is difficult to quantify. It has been reported that the
effect of an activity on a person’s motion sickness
susceptibility depends on the type of activity being
performed. A driving-related activity might reduce
motion sickness, but a non-driving-related activity
can increase it. The ability to predict future motion
is thought to differ between the driver and passen-
gers: a passenger’s predictability might be reduced
because they do not need to control the vehicle’s
motion. Regarding visual information, the visual
field or other optical input to the eyes affects motion
sickness. In addition, visual information is also
strongly related to predictability. If the occupants
could change their seating position or direction such
as by facing toward the rear, this could affect their
visual information and predictability. These points
were discussed in Diels & Bos (2015) and Sivak &
Schoettle (2015).
In addition, the driver is known to tilt his or her
head toward the center of the curve when negotiat-
ing a curve, whereas a passenger's head moves in the
opposite direction Wada et al. (2012). Wada et al.
(2012) and Wada & Yoshida (2015) revealed that if
a passenger tilts their head against the centrifugal
force in slalom driving, in imitation of the driver’s
head movement, it significantly reduces the severity
of motion sickness compared with tilting their head
toward the centrifugal force. This indicated that head
tilt affects the susceptibility to motion sickness.
(b) Increase of non-driving tasks
Being relieved of the driving operation increases an
occupant’s ability to use a tablet, watch a handheld
display, or engage in other non-driving activities.
Because the occupant is not required to monitor the
road in theory, he/she can look at the visual display
for a long time. Inconsistency between a passenger’s
body movement and what they see can increase mo-
tion sickness (Reason 1978a). A decrease in antici-
pation of body movement as a result of looking at a
display is also thought to be a risk factor.
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2.2 Changes in vehicle system: Driving operation
replaced by automation
No study could be found that investigated from the
viewpoint of changes in vehicle system or driving
operation being replaced by automation. It is possi-
ble to dramatically change the motion pattern or ve-
locity profile of an automated vehicle. A human
driver is thought to drive in a manner that prevents
motion sickness even without passengers because a
driver that does not want to get carsick functions as
a type of motion sickness detector or predictor. On
the other hand, to design the motion pattern for an
automated vehicle with a view to minimizing motion
sickness, it is necessary to quantitatively evaluate
the motion sickness severity for a given vehicle mo-
tion. Section 3 will introduce a mathematical model
of motion sickness to estimate its incidence.
In addition, because no human controls the vehi-
cle motion, the size and shape of the windows can be
changed. Employing smaller windows may increase
passengers’ susceptibility to motion sickness be-
cause of reductions in the visual field and the ability
to predict future movements (Griffin & Newman
2004). The effect could include the decrease in an-
ticipation of body motion due to lack of visual in-
formation in front.
3 QUANTIFICATION OF MOTION SICKNESS
INCIDENCE IN AUTOMATED VEHICLES
As a method to quantify severity of motion sick-
ness, the motion sickness dose value (MSDV) is
used, which is the integration of frequency-weighted
acceleration (McCauley et al. 1976)(Kato & Kita-
zaki 2006). On the other hand, Bles et al. (1998)
proposed the subjective vertical conflict (SVC) theo-
ry, which postulates that motion sickness is caused
by the accumulation of conflict between the vertical
direction of the body sensed by the sensory organs
and the predicted motion by an internal model that is
thought to be established in the central nervous sys-
tem. A mathematical model for the SVC theory was
proposed, detailed for one degree-of-freedom (DOF)
motion (Bos & Bles 1998). We propose the 6DOF-
SVC model (Fig.1), which predicts motion sickness
incidence (MSI) in 6 DOF motion in 3D space as an
extension of the Bos & Bles (1998) model by intro-
ducing semi-circular canal and canal-otolith interac-
tion.
Please refer to Wada, Fujisawa, et al. (2010) for the
detail of the mathematical model.
OTO
SCC
LP
SCCSCC
OTOOTO
LPLP
Δv
Δ
a
Δω 





















2
2
/
1/
b
b
Δv
Δv
ω
f
∫
∫
∫
a



f

s
a
s
ω
s
v
ˆ
s
a
ˆ
s
ω
ˆ
s
v

2
1
I
P
s


MSI
c
K
ω
vc
K
ac
K
a
K
K
ω
a
Internal model
Sensory organs
Head Acc.（Gravity + Inertial accel.)
Angular velo.
Head accel.
Internal model
Figure 1. Mathematical model of motion sickness
(Wada, Fujisawa, et al. 2010).
4 COUNTERMEASURES
4.1 Vehicle control
4.1.1 Design of vehicle motion minimizing motion
sickness
As mentioned before, it is necessary to consider
motion sickness explicitly in designing vehicle mo-
tion of an automated vehicle. Given the mathemati-
cal model of motion sickness, the vehicle control
that minimizes the MSI can be designed. In this sec-
tion, a vehicle motion generation method that mini-
mizes the predicted MSI by the 6DOF-SVC model is
introduced (Wada et al. 2015).
(a) Road geometry
Consider single lane roads with curves where a
driver should decelerate to an appropriate velocity
before and/or during negotiating a curve, and then
accelerate again when returning to a straight if nec-
essary. Suppose that road geometry is given by a
plane algebraic curve. For the sake of simplicity, as-
sume that both ends of a curve with a constant cur-
vature are connected to a straight via clothoid curves
(Fig.2). Let parameter s be an arc length at a point
measured from a given origin; then, the road geome-
try can be described by specifying

(s), which is the
angle of tangent line at s measured from the x-axis
of the road-fixed Cartesian coordinate system o-xy
(Fig.2).
0
f
Figure 2. Description of road geometry (Wada et al. 2015).
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(b) Vehicle control method
The deceleration control method that was derived
from expert drivers’ deceleration profile (Wada,
Doi, et al. 2010) is expanded to accelera-
tion/deceleration control here.
In a single block of road, including a curve defined
as 0][,
f
s
s
s, as shown in Fig.1, velocity patterns in
which it is assumed that a vehicle drives at a con-
stant velocity, decelerates to a certain velocity,
drives at the constant velocity, accelerates to a given
velocity, and drives at the velocity, are formulated as
eq.(1).
01 1
012 1 2
23 2
34
00 0
3
34
14 4
()
()
() := ( ) (1)
( )
( )
a
aa
af
ff f
ss
s
fv,v,
s
ss
ss
s,s s
f v ,v ,s ,s s
v s f v ,v ,s ,s s
f v ,v ,s ,s s
f v ,v ,s ,s sss















3
)
:=v ( )exp{3(1- ( )},
(
(2)
() := (3
(
)
jkl
ji l kl
l
kl
lk
i
jk
f v ,v ,s ,s
v,s ,sv)d s,s ds,s
ss
ds,s ,
s
ss
,




where vo, vf denote the velocity at s=s0 and sf, re-
spectively, and va denotes the velocity at the con-
stant velocity that is expected to appear in the curve.
Scalars s1 and s2 are the start and end positions of
the deceleration, and s3 and s4 are the start and end
positions of the acceleration. Assuming that the oc-
cupant’s head moves in accordance with the vehicle
motion, the head acceleration and the angular veloci-
ty are determined by specifying the vehicle velocity
on the road; thus, the predicted MSI is calculated us-
ing the 6DOF-SVC model. Therefore, the design of
the velocity pattern can be formulated by following.
[Design of velocity profile]
Given: 0,,()
f
vv
s

( 0][,
f
s
s
s)
Find 1234
:[,,,,]
a
x
ssssv
which minimizes MSI calculated using 6DOF-
SVC model.
s.t. 1234
s
sss
00, 0, 0
fa
vv v
0f
ttT
where t0 and tf are the times when the vehicle is at s
= s0 and s = sf, respectively.
This problem can be solved by employing appropri-
ate nonlinear optimization method such as Neldar-
Mead Simplex Method.
(c) Results
Fig.3 shows the generated velocity profile for a road
course designed to be driven at 20 km/h following
the Road Construction Ordinance of the Ministry of
Land, Infrastructure, Transport and Tourism in Ja-
pan, in which 010m/s
f
vv was set. Driving time, T,
was changed as 10 through 60 s. The velocity pro-
files in which the vehicle decelerated before entering
the curve, drive at a constant speed, and accelerate
again were successfully generated, and they con-
firmed our prediction.
4.2 Behaviors
An occupant’s behavior affects motion sickness.
The seating posture can be modified to reduce mo-
tion sickness because it is known that sleeping and a
supine posture reduce motion sickness (Diels & Bos
2015)(Sivak & Schoettle 2015). Thus, the location
and direction of the seats should be considered care-
fully.
In addition, from Wada et al. (2012)and Wada &
Yoshida (2015), tilting the head angle toward the
center of a curve reduces motion sickness. However,
it should be noted that passive head movement
might conversely increase motion sickness because
motion sickness increases in a tilt train (Persson
2008). Thus, a method to encourage such a head tilt
naturally is needed to facilitate this. For this pur-
pose, we proposed a postural control device using
pneumatic balloon installed on the drivers’ seat that
encourages drivers’ head tilt towards centripetal ac-
celeration when negotiating a curve (Konno et al.
2011).
4.3 User interfaces
The chances that the occupants use a tablet or other
devices can be increased while riding in automated
vehicles. The display design of such devices plays a
Curvedregion
Figure 3. Generated velocity profile(Wada et al. 2015).
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crucial role in decreasing motion sickness. In Diels
& Bos (2015), a design guideline for such user inter-
faces is provided, but it is omitted here owing to
lack of space.
There is a possibility to add visual information to
provide road geometry ahead, etc., to increase the
anticipation to reduce motion sickness. For this, ef-
fort in software to reduce motion sickness is possi-
ble. For example, Morimoto et al. (2008) successful-
ly reduced motion sickness while watching a video
in a passenger car by deforming a video image that
was played on an in-vehicle monitor.
5 CONCLUSION
This paper discussed factors related to motion
sickness in automated vehicles from the viewpoint
of changes for drivers caused by being relieved from
the driving operation and those in the vehicle system
controlled by the automation. Considering the im-
portance of quantifying motion sickness, a mathe-
matical model of motion sickness was introduced
with the goal of providing countermeasures. Finally,
some countermeasures were proposed from the
viewpoints of vehicle control, behavior, and a user
interface, which put an emphasis on a vehicle con-
trol method to minimize MSI.
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