Effect of Human–Machine Interface of a Vehicle on Right-TurnManeuver at Intersections using a Driving Simulator*
Yuta Kusakari, Student Member, IEEE, Shoko Oikawa, Yasuhiro Matsui, and Naoyuki Kubota, Member, IEEE
Abstract—In the case of vehicles with low speeds at the time ofpedestrian fatality, the percentage of pedestrian collisions was
the highest for right turns, yet the mechanism of these traffic
accidents has not been clarified. In this study, we investigate thebehavioral characteristics of drivers when a vehicle makes aright turn in five situations using a driving simulator. We
conducted an experiment using a driver assistance system thatalerted drivers when the system detected pedestrians at theintersection. A human–machine interface (HMI) was firstdisplayed when the subject vehicle (ego vehicle) stopped in frontof the intersection due to a red light. The display was thenturned off when the traffic light changed to green, and the ego
vehicle started moving. It was displayed again when the ego
vehicle entered the intersection. We found that HMI display waseffective in increasing the percentage of driver’s gazing time atpedestrians and in ensuring safety by the vehicle’s stopping tomove forward. Furthermore, we found that HMI’s effectivenesswas the most significant in the situation when three preceding
vehicles made a right turn.

I. INTRODUCTION
According to a report by the Traffic Bureau of the National
Police Agency, the number of fatalities due to traffic accidents
in Japan has followed a decreasing trend. However, pedestrian
deaths (1,002) accounted for 35.3% of traffic fatalities in 2020
(2,839), which was the highest percentage by road user type[1]. Therefore, preventing accidents between vehicles andpedestrians is an extremely important issue in realizing a safe
traffic society. When focusing on fatal traffic accidents
considering road configuration, 46% of accidents occurred atintersections in 2020 [1]. When we further focus on the
behavior of vehicles (in particular, sedans) involving
pedestrian fatalities at low speeds, it was reported that thehighest percentage of accidents occurred when vehicles
collided with pedestrians when turning right [2]; however, themechanism of right-turn accidents has not been clarified.
To reduce traffic accidents, advanced safety support
systems using communication technologies such as
vehicle-to-vehicle (V2V) [3] and road-to-vehicle (R2V) [4]data transfer have been developed. Currently, driver assistancetechnologies for human drivers have been introduced [5],which corresponds to Level 2 of automated driving as definedby the Society of Automotive Engineers (SAE) [6]. In fact,driver assistance systems such as Daimler's S-Class [7] andY. Kusakari is with Tokyo Metropolitan University, Hino, Tokyo,
191-0065, Japan (e-mail: kusakari-yuta@ed.tmu.ac.jp)
S. Oikawa is with Tokyo Metropolitan University, Hino, Tokyo,
191-0065, Japan (e-mail: shoko_o@hotmail.com)
Y. Matsui is with National Traffic Safety and Environment Laboratory,
Chofu, Tokyo, 182-0012, Japan (e-mail: ymatsui@ntsel.go.jp)
N. Kubota is with Tokyo Metropolitan University, Hino, Tokyo,
191-0065, Japan (e-mail: kubota@tmu.ac.jp)

Lexus Safety Systemcommercialized.

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Among the driver assistance systems, in addition toproviding basic information such as vehicle’s traveling speed
and turning signals, the human–machine interface (HMI) is
considered to help in reducing human error; sensors detect thepresence of pedestrians and other vehicles and sends
emergency warnings to drivers, including both visual andaudible signals [4,8]. However, depending on the display
positions, timing, and information presented, HMIs may cause
confusion or misunderstanding among drivers, which maylead to unnecessary operations or dangerous behaviors.
Therefore, it is necessary to verify the appropriateness of the
HMI display and their effectiveness [9,10]. In designing the
HMI, it is important to consider how it informs the driver ofintentions accurately according to the situation.
In the present study, to clarify the driver’s behavioral
characteristics when a vehicle makes a right-turn maneuver at
an intersection, we conducted experiments using a driving
simulator (DS). In addition, to obtain knowledge about theeffectiveness of an HMI, we conducted experiments with andwithout an HMI that showed the presence of a pedestrian at
the intersection where the vehicle was turning right and alerted
the driver when there was a high risk of collision.
The rest of this paper is structured as follows: Section 2
discusses the research method, and Sections 3 and 4 show theexperimental results and discussion. Finally, Section 5 is aconclusion.
II. RESEARCH METHOD
We developed a DS that simulated an intersection indaytime and researched the effectiveness of presenting
pedestrian information using HMI when a vehicle was turning
right in the presence of pedestrians positioned in the directionof the vehicle’s travel. The test protocols employing
volunteers in the present study were approved by the ethics
committee of Tokyo Metropolitan University.
A. Participants
We chose 14 drivers with driving licenses as volunteers.
The average age was 22.9 years (SD 0.92). We asked theparticipants to drive under different scenarios in an urban areawith intersections, traffic lights, and pedestrians. Before theexperiment, the volunteers were instructed to drive as usual.
The volunteers drove one time for each of the “Without HMI”,
and “With HMI” scenarios.
B. Driving simulator
DS enables the subject to drive safely even in dangerous
situations, such as vehicle accidents with pedestrians.
Therefore, the simulator was utilized to analyze the behavior

of drivers in traffic situations involving collisions [11]. Figure
1 shows the hardware configuration of the DS used in theexperiments, which consisted of an ultrawide 21:9
monitor(34–inch), PC, steering wheel controller, accelerator,
brake pedal [12], eye tracking measurement sensor [13], andweb camera, which captured images of the driver whiledriving. The DS was developed by Unity [14], which is agame engine with an integrated development environment for
multiple platforms developed by Unity Technologies. Unity
took the roles of controlling the scenario and recording thedriving data, collected at 5 Hz.
C. Eye tracking
This system used the Tobii Eye Tracker 4C of Tobii
Technology to measure the gaze of drivers, as shown in
Figure 2; it is an improved version of traditional pupil centre
corneal reflection (PCCR) remote eye-tracking technology[15]. In this study, we used an installation-type device tomeasure viewpoints directed at a fixed monitor. To improvemeasurement accuracy, we calibrated each volunteer before
starting the experiments by Tobii’s application. Tobii
measured the position of the gaze pointing into the specified
display, and it outputs the coordinates of the gaze in response
to Unity's Excel recording script.
D. Human–Machine Interface (HMI)
The information about the presence of a pedestrian at theintersection was presented to the driver when there was a riskof collision between vehicle and pedestrian. The driverassistance system used in DS assumed that the environment
was one in which positional information about the vehicles

and pedestrians could be shared by a communication system.
The system alerted the drivers when the system detected thepedestrian present at the intersection, and if there was a highrisk of collision during right turns.
Two icons were displayed on the dashboard in front of thedriver's seat, as shown in Figure 3. They consisted of two
types of information, a pedestrian crossing, and the location of
the pedestrian at the intersection. They were first displayedwhen the subject vehicle (ego vehicle) stopped in front of theintersection due to a red light. The display was then turned off
when the traffic light changed to green, and the ego vehicle
started moving. They were displayed again when the ego
vehicle entered the intersection and came within a 20-meterradius of the pedestrian. The reason why the HMI was
temporarily turned off was to avoid unnecessary disturbances;moreover, it was easier for the driver to notice the display
clearly when the system was switched on to alert the driver toany dangerous situation.
E. Scenario
There were two scenarios, “Without HMI,” and “WithHMI” conditions. The course simulated the driving
environment in Japan, so it was a left-hand traffic road. There
were six intersections; five cases were randomly set for eachintersection, including preceding vehicles, oncoming vehicles,
and a pedestrian, as shown in Figure 4. At one of the six
intersections in one scenario, no other vehicles or pedestrians
appeared. The vehicle and pedestrian models used in thesimulator are shown in Figure 5; the vehicle was a passenger
vehicle (sedans) and the pedestrian was an adult male. All
types of vehicles appearing in the DS, including the ego
vehicle were of the right-hand-drive sedan type, and theirbody color was white. The maximum speed of all the vehicles
was set to 60 km/h. Mori and Taniguchi indicated that thewalking speed of pedestrians is about 1.4 m/s when pedestrian
density is less than 1.1 persons/m2 [16]. Therefore, we set the
average walking speed of the pedestrian to 1.4 m/s.
In all intersections, the traffic light was set to red so thatthe ego vehicle stopped before entering the intersection andwas changed to green so that it passed through the intersection.
The volunteers drove one time for each of the "Without HMI"and "With HMI" scenarios.

Figure 1. Hardware configuration of the driving simulator.

Figure 2. Tobii eye tracker 4C.

Figure 3. Design of HMI

Figure 6 shows the measurements of the intersections usedin the experiment. When collecting data, the center of theintersection was set as the origin (0,0), and the coordinates ofthe ego vehicle, preceding vehicle, oncoming vehicle, andpedestrian were obtained in relative coordinates. As shown in
Figure 6, the position of all vehicles (vehicle position) was set
at the center of their fronts. The position where the ego vehicle
was stopped before entering the intersection was defined as
Area I. We divided the intersection into four areas, anddefined each area as A, B, C, and D. The total area of theintersection was defined as Area II. After passing throughArea II, the section of the crosswalk where the pedestriancrossed was defined as Area III; we used all these areas for the
analysis. However, when we analyzed Area II, we used thedata from the time the ego vehicle entered Area II to the time it
stopped moving forward in Area II. This was done toeliminate data such as the driver’s gaze position while waitingfor pedestrians to cross.

Without any other vehicles:
In Case 1, there are no othervehicles. The pedestrian starts
crossing after the ego vehicle
has passed through the centerof the intersection.
(a) Case 1 (ego only)(a) Vehicle(b) Pedestrian

Figure 5. Vehicle and pedestrian model used in the simulator.

A preceding vehicle makes aright turn:
A preceding vehicle stops at a
traffic light, makes a right turnat an intersection. The ego
vehicle follows it and turns
right. The pedestrian starts
crossing after the preceding
vehicle turns right and passes
the crosswalk.
(b) Case 2 (pre-1)

Three preceding vehicles
make a right turn:Three preceding vehicles stopat a traffic light and make aright turn at an intersection.
The ego vehicle follows themand turns right. The pedestrian
starts crossing after the last
preceding vehicle turns rightand passes the crosswalk.

(c) Case 3 (pre-3)

The oncoming vehicle goesstraight and passes through at
the intersection:
An oncoming vehicle travels
straight
throughanintersection. The pedestrian
starts crossing when the ego
vehicle passes through thecenter of the intersection.
(d) Case 4 (oncoming-s)

An oncoming vehicle makes aleft turn:The oncoming vehicle turns
left at the intersection. The
pedestrian starts crossing after
the oncoming vehicle turns
left and passes the crosswalk.
(e) Case 5 (oncoming-l)

Figure 4. Experimental scenarios

Figure 6. Measurement of intersection, and each position defined in thisstudy.

F. Analysis parameter
The parameters analyzed in this study as follows.

TimeVisible
While the ego vehicle was passing through theintersection, there were times when the view of pedestrian
was blocked by other vehicles or A-pillars of the ego
vehicle. TimeVisible indicates the total time at whichpedestrians could be seen in Area II.
TimePedestrian
TimePedestrian indicate the averaged total time at which
driver looked at the pedestrian in Area II.
TimeHMI
TimeHMI indicate the averaged total time of the driver
looked at HMI display in Area I or Area II.
Time to Collision (TTC)
In this study, we used TTC to evaluate a vehicle's ability
to avoid a collision with a pedestrian. This study defined
TTC as the time it took for the ego vehicle to reach thepoint of potential collision with the pedestrian from thetime when the driver first gazed at the pedestrian in Area
II. It was assumed that the vehicle was traveling at its
current speed. TTC (s) was calculated as follows: (1)using the distance D (m) between the vehicle and thepedestrian, and the vehicle speed V (m/s) when the driver
first gazed at the pedestrian in Area II.

TABLE I.

Q1
Q2

ITEMS OF QUESTIONNAIRE

Was the information provided by HMI helpful?
Was the timing of HMI displays appropriate?

G. Questionnaire
After the experiments, we asked the volunteers to answer
a questionnaire regarding the HMI in the DS as a subjective
evaluation, in addition to questions on driving experience.
The questionnaire items, presented in Table I, consisted of
two questions with five possible responses: 1. Strongly
disagree, 2. Disagree, 3. Neither agree nor disagree, 4. Agree,
and 5. Strongly agree. We also asked volunteers to freely
describe their specific requests and impressions of HMI.
III. RESULTS
Figure 7 shows the position of the ego vehicle when thedriver first gazed at the pedestrian in Area II. The percentageof pedestrians found in each of the defined areas A, B, C, andD is shown, with the percentage of pedestrians found bothwith and without HMI as 100%. In the case without HMI, thepercentage of pedestrians detected in areas A (7.0%) and C(39.3%), which were far from the pedestrians, was higher thanthe 3.7% and 37.7%, respectively, in the case with HMI. With
HMI, the highest percentage was in area B, which was theclosest to the pedestrians, at 56.4%. Figure 8 shows theaverage TTC in five cases when the driver first gazed at thepedestrian within Area II. We conducted a t-test between thetwo groups with and without HMI, but no significant
difference was found in either case.

Table II shows TimeVisible, TimePedestrian, and thepercentages of the driver’s average gazing time at pedestrianwhen the ego vehicle made a right turn at an intersection in
Area II in the conditions with and without HMI. The results
showed an increase in four conditions of the cases with HMI:
Case 1 (73.8% to 74.1%), Case 2 (76.9% to 81.5%), Case 3(60.8% to 73.4%), and Case 4 (65.5% to 71.1%) compared towithout HMI. For Case 5, the percentage decreased by 2.2%
from 65.7% to 63.5%. We confirmed that the driver’s averagegazing time at pedestrian in situations where the presence ofpedestrians could be confirmed by the HMI display increased.
Figure 9 shows the percentage of ego vehicle that stopped
moving forward to avoid a collision with a pedestrian in Area
II for both with and without HMI. In Area II, the percentagesof vehicle’s stopping in the cases with HMI increased in fourcases, comparing to the cases without HMI: Case 1 (42.9% to64.3%), Case 2 (50.0% to 64.3%), Case 3 (14.3% to 71.4%),
and Case 4 (57.1% to 64.3%). In only Case 5, the percentage
decreased from 35.7% to 28.6% in the case without HMI. The
percentage of vehicles that stopped in Area II increased themost in Case 3, when three preceding vehicles turned right, an
increase of 57.2%. We conducted hypothesis testing for thedifference in the population proportions between the twogroups with and without HMI, which was a two-sided testwith a p-value less than 0.05 considered to be statisticallysignificant. In Case 3, we confirmed a significant difference
between the conditions with and without HMI (p = 0.0023).

10

w/o HMINS

w/h HMI

9

8.40

8

7.66

NS

7

Average TTC [s]

NS

NS

6

NS

5.60
5.03

5

5.58

4.96
4.42

4.89

4.40 4.34

4
3
2
1
0

Case１
(ego only)

Case２(pre-1)

Case３(pre-3)

Case４(oncoming-s)

Case５(oncoming-l)

NS: Not significant

Figure 7. Position the driver first gazed at the pedestrian within Area II.

Figure 8. Average TTCs in five cases when the driver first gazed at thepedestrian within Area Ⅱ.

TABLE II. TIMEVISIBLE, TIMEPEDESTRIAN, AND THE PERCENTAGE OF GAZING TIME AT PEDESTRIAN
Experimental
scenarios

Time Visible (a)w/o HMImean [s]Time Pedestrian (b)w/o HMIw/h HMISD

mean [s]SDmean [s]The percentage of gazing time [%]w/h HMISD

mean [s]SD

w/o HMIw/h HMI{(a) / (b)}×100{(a) / (b)}×100

Case1 (ego only)

2.01

1.95

2.26

1.91

1.49

1.37

1.67

1.48

73.8

74.1

Case2 (pre-1)

1.54

1.35

1.31

1.04

1.19

1.14

1.07

0.88

76.9

81.5

Case3 (pre-3)

2.26

1.14

1.34

0.92

1.37

0.81

0.99

0.69

60.8

73.4

Case4 (oncoming-s)

1.66

1.88

1.19

1.23

1.09

1.35

0.84

0.92

65.5

71.1

Case5 (oncoming-l)

2.59

1.56

2.74

2.01

1.70

1.22

1.74

1.21

65.7

63.5
SD: standard deviation

suggested that the information provided by the HMI increased
driver’s attention to pedestrians, and caused the drivers to stopthe vehicle to ensure safety. For Case 5, the percentage of ego
vehicle that stopped in Area II decreased by 7.1% (35.7% for
the condition without HMI display vs. 28.6% for the condition
with HMI display); however, the percentage of ego vehicle
that stopped in Area III was 14.3% (n=2) for the condition
without HMI display and 21.4% (n=3) for the condition with
HMI display. Therefore, when the percentage of ego vehicles
that stopped in Area III was taken into account, the percentage
was 50.0% for both conditions, indicating that there was nodifference between the conditions with and without HMI. In
Case 5, the oncoming vehicle turned left, the pedestrian started
crossing after it passed through the crosswalk. Moreover, it
passed through the crosswalk shortly after the ego vehicle
entered the intersection. As there were no pedestrians in the
direction of travel and safety had already been ensured, there
was no change in the percentages of ego vehicle that stopped
under the conditions with and without HMI.

Figure 9. Percentage of ego vehicles that stopped moving forward in
Area Ⅱ.
1 Strongly disagree
Q1(n=14)

21.4 %

Q2(n=14)

21.4 %
0%

2 Disagree

3 Neither agree nor disagree

4 Agree14.3 %

64.2 %

7.1%

71.4 %
20%

40%

5 Strongly agree

60%

80%

100%

Figure 10. Percentage of each item of the questionnaire.

The results of the questionnaire are shown in Figure 10. In
Q1, for the usefulness of the HMI display, the percentage ofvolunteers who answered 4 (Agree) was the highest (64.2%),
and for Q2, regarding the timing of the display, thepercentage of volunteers who answered 4 (Agree) was thehighest at 71.4 %.
IV. DISCUSSIONFirst, we considered the positions where the drivers first
identified pedestrians in Area II. Comparing the positions withand without HMI, the percentages were higher in Areas A and
C in the case without HMI, which were far from thepedestrians, whereas the percentage was higher in area B,
which was the closest to the pedestrians, in the case with HMI.
This might indicate that the HMI display delayed the timing of
the first moment of the driver’s attention to the pedestrian in
Area II. In this study, the driver was informed of the presenceof the pedestrian by the HMI when the ego vehicle stopped
under a red light (Area I). Table III shows the number andpercentage of driver gazing at HMI display in Area I and Area
II. Most drivers (92.9%) checked the HMI display when they
stopped ego vehicle in Area I. Therefore, it was assumed thatthe driver confirmed the presence of pedestrians in the
sidewalk in the direction of the vehicle’s travel before entering
the intersection. And thus, in the case with HMI, the time
when the drivers first identified the pedestrians in theintersection might be later than the case without HMI.
As shown in Figure 9, the percentage of the ego vehicle
stopped moving forward in Area II increased when theinformation was provided by the HMI display in all cases
except for Case 5. The increased percentage in the four cases

The most significant difference in the percentage of ego
vehicles stopped moving forward in Area II was seen in Case3, the scenario in which three preceding vehicles turned rightat the intersection in the same direction as the ego vehicle. The
pedestrians standing on the sidewalk did not start walking
when the three preceding vehicles passed. The ego vehicle
itself tried to turn right following the preceding vehicles,
making it difficult to pay attention to the pedestrians.
Therefore, this scene is considered to be the most dangerous
among the five cases.
When we looked at TTCs, which was an evaluation forcollision avoidance, the average TTC for Case 3 was 4.40 s
under the condition without the HMI display, and 4.34 s underthat with the HMI display, which were the shortest among allcases under both conditions (Figure 8). Figure 11 shows adangerous situation that occurred without HMI. When the ego
vehicle passed through the intersection in Case 3 without the
HMI display, the driver's gaze remained on the preceding
vehicle, and the driver did not notice the pedestrian walking;the driver passed through the intersection without stopping theego vehicle. Therefore, the presence of other vehicles
traveling in the same direction as the ego vehicle in Case 2 and3 may cause the driver to pay attention to the preceding
vehicles other than pedestrians that they should have. In thesecases, the HMI can improve safety by directing the driver’s
attention to pedestrians.
The number and percentage of the drivers gazing at the
HMI display in Areas I and II are shown in Table III. The
average percentage (92.9%) of drivers gazing at the HMI
display in Area I was higher than that (54.3%) in Area II. As
shown in Figure 12 and Table III, the average gazing time at

TABLE III. NUMBER AND PERCENTAGE OF DRIVERSGAZING AT HMI DISPLAY IN AREA I AND AREA II (N=14)
Case１(ego only)

Case２(pre-1)

Case３(pre-3)

[n]

[n]

[n]

[%]

[%]

[%]

Averaged percentage of
Case４
Case５
driver's gazing HMI(oncoming-s) (oncoming-l)
in total cases
[n]

[%]

[n]

[%]

[%]

Area I13

92.9

14 100.0

14 100.0

11

78.6

13

92.9

92.9

Area II

5

35.7

7

9

8

57.1

9

64.3

54.3

50.0

64.3

Figure 11. Dangerous situation that occurred without HMI. (The driver kept
his eyes on the preceding vehicle and passed through the intersection
without noticing the pedestrian who had started walking.)various colors of clothes, and pedestrians from different
directions.
V. CONCLUSION

Figure 12. Average time that the drivers gazed at the HMI display.

the HMI display was longer in Area I than in Area II for all
five cases. Conversely, most of the drivers gazed at thepedestrian inside the intersection (Area II), for which thepercentages of gazing time at pedestrians were 63.5–81.5%, asshown in Table II. These results implied that the driver was
more often directly checking for pedestrians while the ego
vehicle made a right-turn maneuver in the intersection, evenwhen the HMI display was shown.
The icons in this study were displayed on the dashboard.
When the ego vehicle was approaching the pedestrian, thewalking pedestrian and the icons on the screen were moving
apart on the screen, which might make it take time for thedriver to change the gazing target from the pedestrian to the
HMI display. The gazing at the HMI display did not allow thedrivers to check their outside environment; if the visualdisplay of the HMI is far away from the pedestrian that truly
needs attention, there is a risk that it will take time to reorient
gaze from the HMI display to the pedestrian, thereby
inducing a delay in judgment. In urgent situations, such as
when the ego vehicle is approaching a pedestrian, auditory
information, such as an alarm, may be required to avoid
interference with the driver's visual perception of theenvironment. Conversely, when the vehicle is not moving,
there is enough time for a driver to grasp the surrounding
situation; hence, it is considered that a high level of safety canbe maintained by clearly communicating information to thedriver through visual presentation. Therefore, it is necessary
to present information according to the situation andcharacteristics of the driver.
There were some limitations to this study. We conducted
an experiment with adult drivers with an average age of 22.9
years and relatively little driving experience. There is apossibility that elderly drivers might tend to process
information at a slower rate while driving compared toyounger drivers. In this experiment, we were unable toconfirm the effects of age on the processing time of
information using the HMI display. It is necessary to clarify
the differences in the provision of information by the HMI
display for elderly drivers depending on their age and driving
proficiency in future work. In addition, the pedestrian model
used in this experiment was based on a limited scenario in
which only one adult male walked in one direction. However,in an actual traffic environment, pedestrians with various
attributes, such as the number of pedestrians, clothing, gender,
age, are mixed together; hence, it is very complicated.
Therefore, it is necessary to expand the scenario byintroducing pedestrians of shorter stature, such as children,

In this study, using a DS capable of measuring driver’s
gaze, we investigated the effect of the presence of othervehicles and pedestrians on the driver's characteristics at theintersection where the vehicle was turning right. We
conducted experiments with and without HMI, which showed
the presence of a pedestrian at the intersection where thevehicle was turning right and alerted the driver. It was foundthat when turning right at an intersection, the percentage ofgazing time at pedestrians was affected by the presence ofother vehicles. The results also indicated that the HMI display
was effective in increasing the percentage of gazing time atpedestrians and ensuring safety. Furthermore, we found that
HMI’s effectiveness was the most significant in the situation
when three preceding vehicles made a right turn.
REFERENCES[1]

National police agency, “Traffic fatalities and traffic law violations andenforcement of violations of the road traffic law in 2020.”, 2021.
[2] Y. Matsui, and S. Oikawa, “Situational characteristics of fatalpedestrian accidents involving vehicles traveling at low speeds inJapan,” Traffic Injury Prevention, vol. 20, supplement 1, pp.S1–S6 ,2019.
[3] T. Kojima, T. Hirose, T. Takeuchi, and T. Hatano, “Basic study
regarding acceptability of automated driving system which adopts
vehicle to vehicle and vehicle to pedestrian communication system”,
Transactions of the Society of Automotive Engineers of Japan, vol. 49,
no. 5, pp. 1080-1086, 2018 (in Japanese).
[4] Toyota, “ITS connect”, https://toyota.jp/technology/safety/itsconnect/,
Retrieved 11 Mar 2021.
[5] Nissan, “Lane departure warning,” https://www.nissan-global.com/EN/
TECHNOLOGY/OVERVIEW/ldw.html, Retrieved 11 Mar 2021.
[6] Society of Automotive Enginieers (SAE), “SAE-J3016: Taxonomy anddefinitions for terms related to driving automation systems for on-road
motor vehicles”, 2018.
[7] Mercedes-Benz, “The S-class sedan,” https://www.mercedes-benz.co.jp/passengercars/mercedes-benz-cars/models/s-class/saloon/explore.ht
ml, Retrieved 5 Mar 2021.
[8] Lexus, https://lexus.jp/technology/safety/, Retrieved 5 Mar 2021.
[9] X. Wu, and L. N. Boyle, “Auditory messages for intersection
movement assist (IMA) systems, Effects of speech- andnonspeech-based cues,” Human Factors, vol. 63, Issue 2, pp. 336-347,
2021.
[10] G. Abe, and J. Richardson, “Alarm timing, trust and driver expectation
for forward collision warning systems,” Applied Ergonomics, vol. 37,
Issue 5, pp. 577-586, 2006.
[11] X. Li, A. Rakotonirainy, and X. Yan, “How do drivers avoid collisions?
A driving simulator-based study,” Journal of Safety Research, vol. 70,
pp. 89-96, 2019.
[12] Hori, “Racing wheel apex for PS4,PS3,PC”, https://hori.jp/products/p4/RWA/, Retrieved 5 Mar 2021.
[13] Tobii, “Specifications for the Tobii eye tracker 4C,” https://help.tobii.c
om/hc/en-us/articles/213414285-Specifications-for-the-Tobii-Eye-Tra
cker-4C, Retrieved 5 Mar 2021.
[14] Unity, https://unity.com/, Retrieved 5 Mar 2021.
[15] Tobii Technology K.K., “How do Tobii eye trackers work?”
https://www.tobiipro.com/service-support/learning-center/eye-tracking-essentials/how-do-tobii-eye-trackers-work/, Retrieved 5 Mar 2021.
[16] M. Mori, and H. Tsukaguchi “Pedestrian movements on footways,”
Proceedings of the Japan Society of Civil Engineers, vol. 1977, no. 268,
pp. 99-108, 1977 (in Japanese).