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Received: 16 March 2022
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Revised: 19 July 2022
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Accepted: 18 August 2022
DOI: 10.1002/hfm.20973
RESEARCH ARTICLE
A comfort evaluation method based on an intelligent
car cockpit
Jian‐Jun Yang
1,2,3
|Yi‐Meng Chen
1
|Shan‐Shan Xing
1
|Rui‐Zhi Qiu
1
1
Department of Automobile Engineering,
School of Automobile and Transportation,
Xihua University, Chengdu, China
2
Department of Vehicle Engineering, School
of Mechanical engineering, Southwest
Jiaotong University, Chengdu, China
3
Vehicle Measurement, Control and Safety
Key Laboratory of Sichuan Province, Chengdu,
China
Correspondence
Jian‐Jun Yang, Department of Automobile
Engineering, School of Automobile and
Transportation, Xihua University, 610039
Chengdu, China.
Email: 332898021@qq.com
Funding information
The Open Research Fund of Sichuan Key
Laboratory of Vehicle Measurement, Control
and Safety (szjj2018‐130); Sichuan Province
Innovation Training Project (S202110650026
and S202110650028)
Abstract
With the rapid development of automobiles, car cockpits are becoming more and
more intelligent and advanced, and the intelligent requirements of automobile
cockpits are gradually increasing. However, the real value of intelligence can only be
realized when it makes passengers in a cockpit feel comfortable. In this study,
seven factors that affect passenger comfort in intelligent cockpits are defined. Under
these factors, a total of 33 evaluation indicators were developed. The core of the
method was to determine the dissatisfaction indicators and degree of dissatisfaction
in the intelligent cockpit by analyzing the relationship between people's perceived
performance and their expectations. This method was used to evaluate the Tesla
Model 3, and it was found in the results that the higher the degree of dissatisfaction
with the indicator, the more subjective feedback it had, which in turn proved the
effectiveness of the model. According to the degree of dissatisfaction, the indicators
affecting comfort were also divided into three levels. This hierarchical division helps
clarify which indicators should be prioritized for improvement. Generally, this
method has a certain feasibility, which is helpful for the development and redesign of
an intelligent car cockpit, and provides some reference strategies for other
transportation fields.
KEYWORDS
comfort, evaluation method, expectation, intelligent cockpit, the degree of dissatisfaction
1|INTRODUCTION
1.1 |Background
With the rapid development of technology, the number of intelligent cars
has also increased in recent years (Arena et al., 2020). Related research
describes the intelligent car of the future as not only improving traffic
safety like a robot but also further meeting people's demands by
connecting to the Internet (Huang et al., 2016;James,2012). Intelligent
vehicleshavebecomeatrendnow.Companies such as Tesla, Jaguar, and
Google are now launching more intelligent vehicles from innovative
materials and innovative technologies (Sun et al., 2021).
However, for the vast majority of consumers, it is not the
technology itself that cares about them, but how they feel in the
cockpit (Park et al., 2019). The benefits of intelligent cars include
increased safety, reduced need for infrastructure investment,
improved fuel economy, and reduced congestion. But most impor-
tantly, they make passengers more relaxed and comfortable (Jorlöv
et al., 2017). The real benefits of intelligent cars can only be realized
when the human driver is comfortable in the intelligent cockpit and
the interaction between the driver and the automated system is at a
reasonable level (Helldin et al., 2013). Beggiato et al. (2019)
believe that comfort plays an important role in the wide acceptance
of intelligent vehicles. For some products such as smart cars,
consumer satisfaction is a factor for business success (Park et al.,
2019; Salomo et al., 2003). Industry and research are working to
develop systems that can automatically adapt to maximize well‐being
(Olugbade et al., 2021). Therefore, making people feel comfortable is
Hum Factors Ergon Manuf. 2022;1–14. wileyonlinelibrary.com/journal/hfm © 2022 Wiley Periodicals LLC.
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very important in many industries, because the essence of
intelligence is to realize the convenience and comfort of users.
The car cockpit will gradually become a room‐like interior
environment. In the near future, there will be more advanced
intelligent cockpits, people will spend more time in the car cockpit,
and do endless things other than driving such as learning, entertain-
ment, and so on (Sun et al., 2021). The cockpit of an intelligent car is
the key place for direct contact with people to generate feelings. It is
certain that the comfort requirements of people in the car cockpit will
become higher and higher, and should be the primary consideration
in intelligent automobiles. Therefore, the main content of this article
is to explore and analyze the comfort of people in the intelligent
cockpit.
First of all, for the analysis of comfort, there are many
contributions from experts in various fields. In the study, Andargie
et al. (2019) showed that in addition to indoor environmental
conditions, occupant characteristics, building‐related characteristics,
and outdoor environment can significantly affect passenger comfort
in multiunit residential buildings. Jin et al. (2020) found that gender is
also an important factor affecting thermal comfort in outdoor spaces.
Z. S. Chen et al. (2020) proposed a fuzzy language‐based approach to
discover passengers’most important needs to improve comfort while
riding high‐speed trains. Maki et al. (2022) utilize the PDF Line
Integral Method to analyze motion sickness or ride comfort in ships.
It can be seen that comfort is very important in different fields, and
the intelligent car industry is no exception.
1.2 |Evaluation methods for intelligent cars for
passenger comfort
In traditional automobiles, the most common method is to use the car
seat as the object to measure the vehicle vibration, acceleration, and
other indicators to determine comfort (Du et al., 2021; Sezgin &
Arslan, 2012). Castellanos and Fruett (2014) used vehicle accelera-
tion to evaluate passenger comfort, and Li and Bi (2010) analyzed
comfort through vehicle vibration. Silva (2002) collected thermal
comfort, air quality, noise, vibration, and other indices in the cabin
through sensors, and uses objective algorithms to evaluate comfort.
Asua et al. (2022) also evaluated comfort and motion sickness by the
vibration acceleration experienced by passengers in the car cockpit.
These methods have achieved the comfort evaluation of the car
cockpit to a certain extent, but most methods are still too objective
and cannot fully represent the real feelings of passengers. For the
increasingly developed intelligent cockpit, the indicators selected by
these methods are also insufficient, for example, they cannot reflect
the feeling of using some intelligent devices.
Su and Jia (2021) used wearable sensors to analyze passenger
comfort in autonomous vehicles. This approach is difficult to
implement, and it cannot be widely implemented effectively. For
the intelligent car cockpits, Yang et al. (2022) proposed a comfort
model with sound, light, heat, and human–computer interaction as
evaluation indicators. This method is very novel and has a certain
reference value, but the comfort of the human–computer interaction
indicator is defined by the car price, which has certain limitations. In
general, there are still relatively few passenger comfort evaluation
methods based on intelligent cockpits. Especially from the perspec-
tive of human preference, there is relatively little research and effort
on intelligent cars (Park et al., 2019). Based on existing comfort
research and understanding of driver characteristics, there is still a
lack of corresponding inspiration for the evaluation and design of
current and future human–computer interaction cockpits (Sun
et al., 2021).
There are studies that combine people's perceived performance
and expectations to analyze their comfort (Naddeo et al., 2015;
Parasuraman et al., 1985; J. Wang et al., 2021). In these studies, it is
also proved that using the relationship between the two can more
accurately obtain the real comfort of the person. This is a subjective
rather than an objective analysis of comfortable feelings, which can
better reflect real emotions. Therefore, this article will use the
relationship between passengers’perceived performance and ex-
pectations to score some indicators to achieve the evaluation of the
intelligent car cockpit. With the intelligent cockpit trend in full swing,
the comfort evaluation method proposed in this article is also an
exploration in this field. An effective intelligent cockpit comfort
evaluation method can help find problems, improve design, and
improve industrial competitiveness.
1.3 |Influencing factors of passenger comfort in
the intelligent car cockpits
In the exploration and research of the car, some reference factors
that influence comfort can be found for the intelligent cockpit.
Whether it is a traditional car cockpit or an intelligent cockpit, the
comfort of the seat is always an indispensable factor. Fiorillo et al.
(2021) pointed out in the study that legroom and seat spacing have a
great impact on comfort in the possible future interactive car
cockpits. Naddeo et al. (2015) studied various postures of the upper
part of the human body and explained that the different subtle
postures caused by the seats placed in the cockpit are also key
factors affecting comfort. And prolonged sitting in the cockpit can
lead to disccomfort and even health damage (Mastrigt et al., 2015).
At present, there are more and more human–computer interac-
tion displays in intelligent cars, such as the central display in Tesla
Model S, which can control almost all functions of a car. But is the
design of the display screen completely comfortable for passengers?
In response to this, Choi et al. (2018) put forward necessary
functional requirements for the human–machine interface of the
intelligent cockpit. Liu et al. (2022) focused on the driver's gesture
operation on the large in‐vehicle screen and found areas on the
screen that were easy and difficult for the driver to operate. With the
trend of large in‐vehicle displays, displays must continually improve
their design for passenger comfort.
Good information interaction is essential in an intelligent car,
which is also an aspect of brand competition. Guo et al. (2021)
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YANG ET AL.
studied the driving condition prompt systems in the intelligent
cockpit and found that if the system increases the subjective comfort
of passengers, it can reduce motion sickness and unstable posture.
Lin et al. (2005) found that when the driver was appropriately
presented with visual on‐screen information about the driving path,
the driver's symptoms of motion sickness were significantly reduced.
It can be seen that the way of prompting information in the intelligent
cockpit is also a key factor affecting comfort.
In the intelligent cockpit, passengers need less driving tasks, and
the time saved will be doing a lot of entertainment (Sun et al., 2021).
In‐car entertainment devices are also gradually becoming the fastest‐
growing segment in the automotive field (Dimitrakopoulos &
Panagiotopoulos, 2021). These make the entertainment system in
the cockpit more demanding. While the field of in‐cockpit entertain-
ment may still be a relatively new field, there is little research into
whether passengers like it and whether it actually makes them happy.
However, as a development trend of the intelligent cockpit, the
entertainment system must also be an important factor affecting
comfort.
As time in the car gradually increases, the enclosed physical
environment of the intelligent cockpit also requires attention. Human
comfort in indoor environments can usually be assessed from four
aspects: thermal comfort, visual comfort, acoustic comfort, and
respiratory comfort (Song et al., 2019). For the interior environment
of the car, Silva (2002) considers temperature, air quality, noise,
vibration, light, and ergonomics as the main influencing aspects of
comfort in the car.
Based on all the above research and analysis, the comfort of
passengers in the intelligent car cockpit is defined as the compre-
hensive feeling under seven influencing factors in the cockpit. These
influencing factors have been drawn in Figure 1, including physical
environment, seats, human–computer interaction display screen,
entertainment system, navigation system, voice assistant, and early
warning system.
Under the seven influencing factors, the respective evaluation
indicators are divided, which is easy for people to fill in the evaluation
scores during the experiment and it is also convenient to analyze the
specific impact. Five experts, including three experts in the
automotive field and two vehicle engineering designers, discussed
and verified the proposed indicators, and some details were adjusted.
Finally, after feasibility analysis, a total of 33 evaluation indicators for
the intelligent cockpit were determined.
2|RELATIONSHIP BETWEEN
EXPECTATIONS AND ACTUAL
PERFORMANCE
Consumers will compare the service they expect to what they
actually get, and the gap between the two has a huge impact on how
they feel (Grönroos, 1988; Parasuraman et al., 1985). Parasuraman
et al. (1985) developed a SERVQUAL model that defines service
quality through customer expectations and actual perceived effects.
J. Wang et al. (2021) developed a method for assessing aircraft cabin
comfort by comparing the actual experience of passengers with their
expectations.
Understanding the relationship between expectations and
performance will lead to more realistic and accurate results in the
process of evaluating products. Regardless of how expectations are
developed and formed, they are a prerequisite for assessing quality
(O'Connor et al., 2000). In terms of expectations, customer wishes
should be incorporated into the experimental design, and an accurate
understanding of customer expectations is the most critical step in
defining and improving product quality (Pakdil & Aydın, 2007).
Therefore, regarding the comfort of the intelligent cockpit,
evaluating it in combination with expectations will make the results
more accurate and reflect the real situation. J. Wang et al. (2021)
explained that expectations are based on the judgment of the
importance of indicators, and are the desire to reach a certain state in
the future, and usually the importance is proportional to expecta-
tions. This study also uses the importance to reflect the expectations
of passengers in the evaluation.
3|METHODS
To evaluate the comfort of passengers in the intelligent cockpit, some
questionnaires and evaluation models are designed. There are three
types of questionnaires. The first is to explore the satisfaction of
the indicators in the cockpit of the intelligent car, the second is to
explore the importance of the indicators, and the third is to collect
some subjective opinions on the cockpit. The evaluation model
provided a method for analyzing comfort that involved processing
FIGURE 1 Factors affecting comfort in intelligent car cockpits
YANG ET AL.
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the data, screening the indicators of comfort dissatisfaction, and
calculating the degree of dissatisfaction.
3.1 |Questionnaire design
In many studies on comfort, questionnaires were often used to obtain
the comfort of people (Caballero‐Bruno et al., 2022; Goujard et al.,
2005; Terroir et al., 2022). Questionnaires are a good way to collect
people's subjective feelings in an experiment. In the past decades,
researchers developed many types of questionnaires, and they were
proven to be useful instruments in evaluating the subjective feeling
of people in comfort studies applied to diverse fields (Anjani et al.,
2021). Consequently, this article used questionnaires to collect the
subjective comfort data of passengers.
As mentioned in the introduction section, after feasibility
analysis, a total of 33 evaluation indicators for intelligent cockpits
have been formulated. The questionnaires were mainly to evaluate
these indicators. The specific questionnaire details are as follows.
3.1.1 |Satisfaction questionnaire
According to the divided seven influencing factors, the method of
scoring is used to evaluate the satisfaction of the cockpit. The 7‐point
scale may perform better than the 5‐point scale given the reliability
of survey participants’responses (Joshi et al., 2015). Fiorillo et al.
(2021) also used a 7‐point scale when evaluating the comfort of body
parts in the cockpit. To make the evaluation results more accurate,
the indicators of the satisfaction questionnaire use a 7‐point scale
(1 = extremely dissatisfied;7=extremely satisfied). The satisfaction
questionnaire contains a total of 33 evaluation indicators for
personnel to score. The satisfaction scores of the corresponding
indicators can roughly represent their psychological acceptance.
3.1.2 |Importance questionnaire
Similar to the Satisfaction questionnaire design method, a 7‐point
scale (1 = extremely unimportant;7=extremely important) is also used.
It represents the importance of the comfort of a certain indicator, and
it can also refer to the expected effect of people on a certain
indicator. The aim was to collect how important these indicators are
to people in terms of comfort.
3.1.3 |Open‐ended questionnaire
Open‐ended questionnaires are different from satisfaction and
importance questionnaires. It uses open‐ended questions instead of
objective scores. The open‐ended questions in the questionnaire
include: (1) Write down the factors that make you feel uncomfortable
and their corresponding indicators. (2) Explain the reasons for feeling
uncomfortable or dissatisfied. (3) What do you think needs to be
improved the most?
The purpose is to collect some subjective feedback questions and
use the subjective feedback to compare the results of objective
scores.
3.2 |Procedure
To verify the validity of the evaluation method, the cockpit of an
intelligent car was evaluated. A total of 35 Chinese participants were
invited to the evaluation experiment. The basic information statistics
of the participants were shown in Figure 2. According to statistics,
there are 18 males and 17 females among the participants. In
addition, there are 13 people engaged in the automotive field,
10 smart car scholars, 8 intelligent car users, and 4 others. The largest
numbers were 31–40 years old, and the least were those over
60 years.
Based on the intelligent cockpit, this evaluation experiment
selected Tesla Model 3. To exclude the interference of different
cockpit structures of different vehicles on the results, the participants
conducted experiments in the same vehicle in batches. The specific
experimental process is as follows:
Step 1. Fill in personal information and introduce evaluation
indicators.
After the participants arrived at the experimental site, they first
filled in their personal information and were introduced to the factors
and corresponding evaluation indicators in the cockpit.
Step 2. Complete the importance questionnaire.
Before entering the car cockpit, each participant was asked to fill
out a materiality questionnaire within 10 min. This represented each
participant's expected value of the indicators in the cockpit.
Step 3. Complete the satisfaction questionnaire.
After completing the materiality questionnaire, participants
entered the car cockpit for satisfaction evaluation. The road
experiment was carried out on the experimental road selected as
shown in Figure 3, and the road conditions included constant speed
and acceleration sections. Participants drive or ride a car for 20 min
on this route map to fully experience the influencing factors
FIGURE 2 Basic statistics of participants in the experiment
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YANG ET AL.
(Figure 4). Participants completed the satisfaction questionnaire
within 10 min after taking a 5‐min rest after getting off the car.
Step 4. Complete the open‐ended questionnaire.
After completing the satisfaction questionnaire, each participant
completed an open‐ended questionnaire within 15 min. Then, the
entire evaluation experiment ends.
3.3 |Comfort evaluation models
The comfort evaluation model provided a way to analyze comfort,
including taking the geometric mean of participant evaluation scores,
using the relationship between expectations and performance to
determine dissatisfaction indicators, and calculating the degree of
dissatisfaction. The specific steps of the evaluation model are shown
in Figure 5.
3.3.1 |Geometric mean
When participants scored indicators, there were likely to be a few
extreme scores due to differences in perception. This has an impact
on getting an accurate final value of the evaluation. The geometric
mean is more stable than the arithmetic mean for extreme values and
has a smaller coefficient of variation (Gasnier et al., 2021). Singh et al.
(2014) mentioned that the geometric mean is a good measure of the
central tendency of a distribution with a power law. Baskar and
Arockiaraj (2015) showed in their study that the per‐street traffic
growth rate is more accurate when considering the geometric mean
rather than the arithmetic mean across a large population in a city.
Therefore, to make the final result more accurate, the geometric
mean is used to calculate the average score for each indicator.
S
i
represents the geometric mean of satisfaction of the ith indicator
obtained by this method, and
I
i
represents the geometric mean of the
FIGURE 3 Driving route map for the evaluation cockpit
FIGURE 4 Participants driving in the cockpit
FIGURE 5 Model steps for the evaluation of comfort
YANG ET AL.
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importance of the ith indicator. Because it is stated in the above
analysis that the importance is proportional to the expectation,
I
i
also
represents the expectation of the corresponding indicator. A total of
33 indicators are divided in this experiment, i=1, …, 33. The specific
calculation process of
S
i
and
I
i
is shown in Equations (1) and (2)
∏
S
x=,
i
j
n
ij
=1
1
n(1)
∏
Ix=,
i
j
n
ij
=1
2
n(2)
where Jrepresents the jth participant, j=1, …,n, there are a total of
35 participants in this experiment, so n= 35.
x
ij1represents the
satisfaction value given by the jth participant under the ith indicator.
x
ij2represents the importance value given by the jth participant
under the ith indicator.
3.3.2 |Dissatisfaction and degree of dissatisfaction
Parasuraman et al. (1985) found in their research that service quality
or product is satisfactory when it meets or exceeds expectations.
When the service quality or product is lower than expected, it means
that it does not meet consumer expectations, and the larger the
difference is, the more likely it is to be incompletely accepted
(Parasuraman et al., 1985; O'Connor et al., 2000). In general, it is to
compare the performance with the expected effect to judge whether
people are really satisfied and comfortable with the product.
To compare the performance and expectations of the participants,
the evaluation criteria for the two must be unified. In this experimental
process, both satisfaction and importance evaluations are scored
according to the 7‐point scale method, so they can be compared. In
this article, importance represents people's expectations and satisfaction
represents people's perceived performance. Therefore, the gap between
the expectation and performance of the ith indicator is represented by Zi,
andiscomputedasshowninEquation(3)
ZIS=−
.
ii i (3)
According to Parasuraman et al. (1985), Zeithaml et al. (1993),
Pakdil and Aydın(2007), and J. Wang et al. (2021) on the relationship
between human perceived performance and expectation, it can be
sorted out in the following conclusions:
When
Z<
0
i
, it means that the satisfaction of the ith indicator in
the cockpit is greater than the importance. This shows that the
indicator meets the expectations of the participants, indicating that
the participants are satisfied and comfortable with the indicator.
When
Z>
0
i
, it means that the satisfaction of the ith indicator in
the cockpit is less than the importance that the participants think.
This shows that the indicator failed to meet the expectations of the
participants, who were uncomfortable with the indicator.
J. Wang et al. (2021) used an algorithm to calculate the degree of
dissatisfaction when evaluating the comfort of an aircraft cabin. The
algorithm is also applicable for the evaluation of this study, that is, the
degree of dissatisfaction is the ratio of Zito expectation (
I
i
). Z′
iin
Equation (4) is defined as the degree of dissatisfaction
ZZI ISI
′=/=(−)/
.
iii i i i (4)
As mentioned above,
Z<
0
i
represents satisfaction, and it is
meaningless for the degree of dissatisfaction. Therefore, only when
Z′>
0
i
represents the degree of dissatisfaction, and the greater the
Z′
i, the more significant the discomfort component.
4|RESULTS
4.1 |Experimental data statistics
The evaluation data of the participants were collected through the
experiment, and the geometric mean of the importance and
satisfaction of each indicator was calculated by Equations (1) and
(2). The importance and satisfaction of the indicators under each
influencing factor are clearly represented by a line chart.
4.1.1 |Physical environment
The importance score of each evaluation indicator in the physical
environment is compared with the satisfaction score, and Figure 6
was drawn. Among them, the satisfaction scores of noise and
vibration are lower than the importance scores, indicating that these
two indicators make passengers uncomfortable and dissatisfied in the
intelligent cockpit of this evaluation.
4.1.2 |Seats environment
Figure 7is plotted comparing the importance value of each indicator
with the satisfaction value under the seats influencing factors. It was
FIGURE 6 Importance score and satisfaction score of the
indicators under the influence factors of the physical environment
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YANG ET AL.
found that the scores of legroom and texture of seats satisfaction
were lower than the importance, indicating that these two points
were unsatisfactory and uncomfortable for the participants.
4.1.3 |Human–computer interaction display screen
Figure 8is plotted comparing the importance value with the satisfaction
value of each indicator under the human–computer interaction display
screen influencing factors. It was found that the satisfaction value of
screen size and operation method were lower than the importance value,
indicating that the participants were dissatisfied with these indicators and
presented an uncomfortable attitude.
4.1.4 |Entertainment system
Compare the importance score of each evaluation indicator in the
entertainment system with the corresponding satisfaction score, and
Figure 9was drawn. It is found that in the entertainment system,
satisfaction scores for all indicators are higher than importance scores,
indicating that participants are satisfied and comfortable with this section.
It shows that the entertainment system of the Tesla Model 3 tested by
the experiment is so far satisfactory to most people.
4.1.5 |Navigation system
Compare the importance score of each evaluation indicator in the
navigation system with the corresponding satisfaction score, and
Figure 10 was drawn. It was found that the satisfaction scores of
route planning, navigation accuracy, and information prompt ability
were lower than the importance scores, indicating that participants
are dissatisfied and uncomfortable with these indicators.
4.1.6 |Voice assistant
Compare the importance score of each evaluation indicator in the voice
assistant with the corresponding satisfaction score, and Figure 11 was
drawn. It was found that the satisfaction scores of the humanization,
voice recognition accuracy, and the response rate of the voice assistant
were lower than the importance, indicating that the participants showed
dissatisfaction with these indicators and were not very comfortable.
FIGURE 7 Importance score and satisfaction score of the
indicators under the influence factors of seats
FIGURE 8 Importance score and satisfaction score of the
indicators under the influence factors of human–computer
interaction display screen
FIGURE 9 Importance score and satisfaction score of the
indicators under the influence factors of the entertainment system
FIGURE 10 Importance score and satisfaction score of the
indicators under the influence factors of the navigation system
YANG ET AL.
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4.1.7 |Early warning system
Compare the importance score of each evaluation indicator in the
early warning system with the corresponding satisfaction score,
and Figure 12 was drawn. It was found that the satisfaction of all
indicators under the influence factors of the early warning system
was greater than the importance, indicating that the participants
were satisfied with all indicators and presented a comfortable
state. It can be seen that the cockpit early warning system
basically meets the comfort requirements, which makes almost all
participants satisfied.
4.2 |Degree of dissatisfaction and open‐ended
questionnaire data statistics
In the open‐ended questionnaire, some participants' reasons for their
dissatisfaction were collected, and the number of times each dis-
satisfaction indicator was mentioned was counted. Ziand Z′
iare
calculated using Equations (3)and(4). The specific results are as follows:
Among the evaluation indicators of the physical environment
of the cockpit, noise and vibration are indicators that are
calculated to be uncomfortable. In addition, the dissatisfaction
degree of these two indicators is relatively large, and noise and
vibration are mentioned more frequently in the open‐ended
questionnaire (as shown in Table 1). Although the temperature is
mentioned twice, the number of times is relatively few and it is
also a satisfactory indicator after calculation. Overall, the
temperature indicator is still comfortable.
In the evaluation indicators of seats, legroom and texture of seat
are uncomfortable. The dissatisfaction degree with legroom and
texture are slightly higher, and the corresponding mentions are only
six and eight times (as shown in Table 2). Lumbar support feeling, as a
satisfaction indicator, is mentioned once by dissatisfaction and can be
ignored.
In the evaluation indicators of human–computer interaction
display screen, the dissatisfaction degree of screen placement is very
high, and the number of mentions is also high (as shown in Table 3).
The operation method has a relatively low degree of dissatisfaction
and fewer mentions. Fluency of use is a satisfactory indicator, but is
still mentioned three times.
All the evaluation indicators of the entertainment system are
satisfactory. Only the fun indicator is mentioned once (as shown in
FIGURE 11 Importance score and satisfaction score of the
indicators under the influence factors of voice assistant
FIGURE 12 Importance score and satisfaction score of the
indicators under the influence factors of the early warning system
TABLE 1 Degree of dissatisfaction Z
(
′)
iand the number of
mentions of indicators under the influence of physical environment
Physical environment
Zi
Z
i
Feeling Mentions
Noise 2.45 0.40 Dissatisfaction 18
Temperature −0.4 ‐Satisfaction 2
Odor −0.47 ‐Satisfaction 0
Vibration 2.71 0.42 Dissatisfaction 20
Brightness −1.48 ‐Satisfaction 0
Note:“‐” means that the indicator is satisfactory without the degree of
dissatisfaction
Z
(
′)
i
;“Mentions”means the number of times the indicator
was mentioned unsatisfactorily in the open‐ended questionnaire.
TABLE 2 Degree of dissatisfaction Z
(
′)
iand the number of
mentions of indicators under the influence of seats
Seats
Zi
Z
i
Feeling Mentions
Leg room 0.99 0.16 Dissatisfaction 6
Arm bending angle −0.31 ‐Satisfaction 0
Texture of seat 0.97 0.15 Dissatisfaction 8
Lumbar support feeling −0.2 ‐Satisfaction 1
Neck support feeling −0.23 ‐Satisfaction 0
Note:“‐” means that the indicator is satisfactory without the degree of
dissatisfaction
Z
(
′)
i
;“Mentions”means the number of times the indicator
was mentioned unsatisfactorily in the open‐ended questionnaire.
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YANG ET AL.
Table 4). Overall, all aspects of the entertainment system were
generally comfortable for the participants.
Among the evaluation indicators of the navigation system, route
planning, guidance accuracy, information prompting ability, and
speed of response were all mentioned as dissatisfied. The first three
indicators have a higher degree of dissatisfaction and a higher
number of mentions (as shown in Table 5). Speed of response was
mentioned less frequently only twice.
Among the evaluation indicators of voice assistants, human-
ization, response accuracy, and system response speed are men-
tioned many times, and the degree of dissatisfaction calculated by
these three indicators is also high (as shown in Table 6). Way to
control is only mentioned twice.
The evaluation indicators of the early warning system were all
satisfactory, and only early warning prompt efficiency was mentioned
once in the open‐ended questionnaires (as shown in Table 7). Overall,
all evaluation indicators of the early warning system were satisfactory
to the general participants.
According to the statistical results, there are 19 dissatisfaction
indicators mentioned in the open‐ended questionnaire, and 12
dissatisfaction indicators are calculated by the evaluation model.
The indicators that were calculated as dissatisfaction were all
mentioned in the open‐ended questionnaire. It was found that the
higher the degree of dissatisfaction with the indicator, the more it
was mentioned by the participants in the open‐ended questionnaire.
The degree of dissatisfaction was closely proportional to the number
of mentions in the open‐ended questionnaire. The above results
show that the intelligent cockpit evaluation model can reflect the
actual subjective feelings of passengers and has a certain effect on
comfort analysis, which demonstrate that the evaluation model is
effective.
TABLE 3 Degree of dissatisfaction Z
(
′)
iand the number of
mentions of indicators under the influence of Human‐computer
interaction display screen
Human–computer
interaction display
screen
Zi
Z
i
Feeling Mentions
Screen placement 1.71 0.28 Dissatisfaction 14
Screen size −0.34 ‐Satisfaction 0
Screen image quality −0.30 ‐Satisfaction 0
Fluency of use −0.37 ‐Satisfaction 3
Operation method 1.21 0.19 Dissatisfaction 7
Note:“‐” means that the indicator is satisfactory without the degree of
dissatisfaction
Z
(
′)
i
;“Mentions”means the number of times the indicator
was mentioned unsatisfactorily in the open‐ended questionnaire.
TABLE 4 Degree of dissatisfaction Z
(
′)
iand the number of
mentions of indicators under the influence of entertainment system
Entertainment system
Zi
Z
i
Feeling Mentions
Security guarantee −0.29 ‐Satisfaction 0
Speed of response −0.50 ‐Satisfaction 0
Interest −0.49 ‐Satisfaction 1
Operation mode −0.27 ‐Satisfaction 0
Fluency of the system −0.38 ‐Satisfaction 0
Note:“‐” means that the indicator is satisfactory without the degree of
dissatisfaction
Z
(
′)
i
;“Mentions”means the number of times the indicator
was mentioned unsatisfactorily in the open‐ended questionnaire.
TABLE 5 Degree of dissatisfaction Z
(
′)
iand the number of
mentions of indicators under the influence of the navigation system
Navigation system
Zi
Z
i
Feeling Mentions
Route planning 2.24 0.37 Dissatisfaction 16
Navigation accuracy 1.90 0.31 Dissatisfaction 15
Map clarity −0.35 ‐Satisfaction 0
Information prompt 0.89 0.15 Dissatisfaction 10
Speed of response −0.31 ‐Satisfaction 2
Note:“‐” means that the indicator is satisfactory without the degree of
dissatisfaction
Z
(
′)
i
;“Mentions”means the number of times the indicator
was mentioned unsatisfactorily in the open‐ended questionnaire.
TABLE 6 Degree of dissatisfaction Z
(
′)
iand the number of
mentions of indicators under the influence of voice assistant
Voice assistant
Zi
Z
i
Feeling Mentions
Way to control −0.27 ‐Satisfaction 2
Humanization 1.49 0.24 Dissatisfaction 14
Voice recognition
accuracy
2.41 0.39 Dissatisfaction 17
Speed of response 2.05 0.34 Dissatisfaction 18
Note:“‐” means that the indicator is satisfactory without the degree of
dissatisfaction
Z
(
′)
i
;“Mentions”means the number of times the indicator
was mentioned unsatisfactorily in the open‐ended questionnaire.
TABLE 7 Degree of dissatisfaction Z
(
′)
iand the number of
mentions of indicators under the influence of the early warning
system
Early warning system
Zi
Z
i
Feeling Mentions
Scope of early warning −0.25 ‐Satisfaction 0
Early warning accuracy −0.21 ‐Satisfaction 0
Early warning prompts
efficiency
−0.57 ‐Satisfaction 1
Speed of response −0.31 ‐Satisfaction 0
Early warning system −0.38 ‐Satisfaction 0
Note:“‐” means that the indicator is satisfactory without the degree of
dissatisfaction
Z
(
′)
i
;“Mentions”means the number of times the indicator
was mentioned unsatisfactorily in the open‐ended questionnaire.
YANG ET AL.
|
9
5|DISCUSSION
5.1 |Levels of affecting on comfort in the
intelligent cockpit
In the application of comfort grading, Menegon et al. (2019) divided
the indicators affecting comfort into five levels, from the lowest
discomfort to the highest discomfort. J. Wang et al. (2021) divided
the indicators affecting comfort in the aircraft cabin into three levels
according to the degree of dissatisfaction and explained that these
levels also reflect the priority of improving the comfort indicators.
Combined with the experimental data, three levels that affect
comfort in the car intelligent cockpit of this evaluation are divided.
The indicators of
Z′≥0.3
5
i
was classified as the level of significantly
affecting comfort, the indicators of
Z0.25 ≤′<0.3
5
i
was classified as
the level of generally affecting comfort, and the indicators of
Z′≤0.2
5
i
was classified as the level of slightly affecting comfort.
5.1.1 |Level 1: Significantly affects comfort
The indicators of
Z′≥0.3
5
i
include noise, vibration, route planning,
and voice recognition accuracy. The degree of dissatisfaction with
these indicators is extremely high, which makes passengers feel less
comfortable, and there are many mentions of dissatisfaction in the
open‐ended questionnaire. These indicators are extremely important
and should be taken as a priority to improve passengers' comfort.
Combined with the reasons for the feedback of the participants,
it was found that the wind noise and tire noise in the evaluation
cockpit was loud, which made the participants dissatisfied and
uncomfortable. Participants mentioned in the open‐ended question-
naire that they also heard a distinct buzzing sound while driving.
Noise can cause discomfort in many ways, and even affect people's
health (Mellert et al., 2008). Minimizing interior noise has always
been a key research topic in the automotive industry. However, as
automakers strive for more economical and lighter designs, the
resulting car interiors have instead become noisier due to increased
structural vibration (H. Chen et al., 2015). In the future, it is necessary
to design an intelligent cockpit with better noise reduction capability
to improve passengers' comfort.
For the comfort of passengers in the car, the vibration should be
kept within a certain range (Wen et al., 2019). The shock absorption
of the Tesla Model 3 seems to be unsatisfactory, and the participants
reported that the sense of shock was strong when they encountered
the speed bump, which seriously affected comfort. The performance
of the vehicle suspension system plays a decisive role in the ride
comfort and driving stability of the cockpit (Kalaivani & Lakshmi,
2013). Due to the limitation of dampers and stiffness, the traditional
passive suspension is difficult to meet the needs of vehicle
performance improvement (Fan et al., 2017). The design and analysis
of seat suspension systems always consider the effective vertical
spring rates and damping characteristics, ignoring the effects due to
the kinematics of the widely used cross‐linking mechanism
(Shangguan et al., 2017). To improve ride comfort and the driver's
ability to perform specific tasks, a systematic comparative analysis of
various active suspension types and associated controls is necessary.
At the same time, the corresponding control system design tools
should be developed for specialized laboratory tests (Cvok
et al., 2019).
In terms of route planning for navigation, participants responded
that their route planning was unreasonable, and the system may not
have selected the optimal route. Some owners of Tesla Model 3 said
in the survey that the planning of navigation routes seems to be
designed based on charging stations, resulting in unreasonable routes
to some destinations. Spatial cognition research shows that wayfind-
ing is a complex, highly adaptive process, and that route planning is
incremental rather than prescriptive (Hölscher et al., 2011). In fact,
people can also deviate from their initial chosen route for a variety of
reasons, including navigation errors, especially when the environment
is unfamiliar (Amores et al., 2021). But for the electric vehicle
evaluated this time, the battery power issue should not be considered
too much, otherwise, the good planning of the route will be ignored.
For more and more intelligent vehicles in the future, its navigation
system should also be optimized with deep learning methods (Ni
et al., 2020).
The accuracy of speech recognition of Tesla Model 3 does need
to be improved. During the experiment, some participants com-
plained about the inaccuracy of speech recognition, such as saying
the command to play music, but the weather forecast system was
turned on. In human–computer interaction, voice communication
with devices is an important content (Seaborn et al., 2021). With the
development of intelligent technology, the accuracy of the voice
assistant's response will inevitably affect the driving experience and
even safety. Faster and more accurate recognition can meet people's
needs for efficient, simple, and fast interaction (Tao, 2019).
Therefore, designing a better voice assistant in the intelligent cockpit
has a great effect on improving comfort.
5.1.2 |Level 2: Generally affects comfort
The indicators of Z0.25 ≤′<0.3
5
iinclude the navigation guidance
accuracy, the screen placement position and the response speed of
the voice assistant. The degree of dissatisfaction with these
indicators is relatively low. These indicators need to be considered
and improved only after the indicators at level 1 have been improved.
During the experiment, several passengers reported that the
navigation route was wrong and the route had problems. The reason
may be related to the route planning of the navigation system, and
some analysis has also been made above. It is necessary to provide
multiple optional routes and reasonable path algorithms to ensure the
accuracy of navigation guidance (Amores et al., 2021; Jeong et al.,
2019). Although the degree of dissatisfaction with this indicator is
not very serious, it is not negligible.
The dashboard is eliminated in this intelligent cockpit, and almost
all information is displayed on the display screen placed in the middle
10
|
YANG ET AL.
of the cockpit. Participants reported that they needed to deflect their
heads to pay attention to the information on the screen when
driving and that they would experience neck pain when driving for a
long time. This may be due to the fact that the driver is not familiar
with this type of cockpit and is not used to its driving style.
Participants suspected that it might be unsafe to operate the display
while driving to control performance in the cockpit. Obviously, one‐
handed operation is bad for vehicle control and reduces driving
stability (Le et al., 2016). Ma et al. (2018) also explored the optimal
display size and structure to reduce driver distraction. Although
diverse rollable display device concepts have been suggested, little is
known regarding ergonomic forms for comfortable screen unrolling
(S. Lee et al., 2020). Because this is a multidimensional and
multifactor problem, which is affected by the luminous characteristics
of the screen, the user's physiological factors, and some other
environmental factors (K. Wang et al., 2020). Human–computer
interaction display screen is a trend, and passengers will definitely
become more demanding. Therefore, we must pay attention to the
problem of the display screen in the intelligent cockpit.
Regarding the response speed of the voice assistant, the participants
reported that the response speed was very slow. The possible reason is
that people have adapted to the fast voice function response of mobile
phones, which has led to increased expectations for voice assistants, and
then dissatisfaction has appeared. However, it cannot be ignored that
improving the recognition speed of voice assistants is also meeting
people's needs for efficient, simple, and fast interaction (Tao, 2019).
Therefore, it is necessary to speed up the response speed of voice
through a software upgrade and hardware redesign.
5.1.3 |Level 3: Slightly affects comfort
The indicators of
Z′≤0.2
5
i
include leg room, texture of seat,
information prompting ability of the navigation system and human-
ization of voice assistant. Although these indicators are also
indicators of dissatisfaction, the degree of dissatisfaction is low, so
the impact on passengers is not large. After perfecting the level 1 and
level 2 indicators, it is not too late to consider these indicators.
Regarding the leg room and seat softness and comfort, there will
be dissatisfaction in almost any car cockpit. These indicators are also
closely related to the passenger's own body structure and personal
preferences (Stanglmeier et al., 2021; Vink & Lips, 2017). As long as
the degree of dissatisfaction is not too high, there is no need to deal
with it immediately.
The prompts for navigation information are indeed relatively few
in the evaluation of the intelligent cockpit. For some participants who
are not very familiar with the route, they will not be very satisfied, so
some information prompts can be added appropriately, such as speed
limit, one‐way street reminder, and so on.
Dissatisfaction with the humanization of voice assistants reflects
passengers' expectations for higher intelligence in the future. If the
degree of dissatisfaction is not too high, they can be ignored for the
time being.
5.2 |Analysis of the comfort evaluation model
First of all, this article proposed a comfort evaluation model based on
the relationship between expectations and performance. In the actual
evaluation, the satisfaction of the indicator is used to represent the
passenger's perceived performance, and the importance of the
indicator is used to represent the expectation. In the Tesla Model 3
evaluation experiment, we can see that the higher the degree of
dissatisfaction with the indicator, the more it is mentioned in the
open‐ended questionnaire. Some indicators calculated in the evalua-
tion model are satisfaction indicators, which are also mentioned in
the public questionnaire. However, these satisfaction indicators were
so rarely mentioned that they were negligible, suggesting that
participants as a whole were still satisfied with them. Overall, the
results show that the evaluation model and subjective feedback are
the same to a certain extent, which proved that the comfort
evaluation model can truly reflect the participants' feelings in the
intelligent cockpit and verified the validity of the model.
Recently, there have been many studies on intelligent cars, but
the research on the comfort of their intelligent cockpits is still
relatively lacking. With the application of intelligent driving technol-
ogy and the transformation of the driver's identity, the interior of the
car cockpit will be redesigned, and the comfort research based on the
passenger's riding experience will be particularly important
(Elbanhawi et al., 2015). In our previous research, we created an
evaluation model including noise decibels, illuminance, temperature,
and vehicle price to evaluate the comfort of intelligent cockpits (Yang
et al., 2022). This evaluation model still has many limitations; a reason
is that there are too few indicators set to fully represent whether the
passenger is really comfortable. To improve our understanding of
comfort and reduce variability in measurements, more dimensions
should be considered (Terroir et al., 2021). The vehicle price was used
in the model to define the quality of human–computer interaction,
which had great defects and is not well represented.
In this article, combined with relevant literature and previous
research, a new method for evaluating intelligent car cockpits is
pioneered. In the beginning, after analysis, seven factors that affect
passenger comfort in intelligent cockpits are divided. These influen-
cing factors include physical environment, seats, human–computer
interaction display screen, entertainment system, navigation system,
voice assistant, and early warning system. To make the evaluation
model more comprehensive, corresponding evaluation indicators are
also divided under different influencing factors. The proposed
influencing factors and 33 evaluation indicators also served as
references for future research on human factors. In terms of
passenger perception, the relationship between expectations and
performance is used to define comfort, which is also a more accurate
evaluation result. The three levels that affect the comfort proposed
above help to quickly find the areas that need to be improved in the
intelligent cockpit, so as to maximize comfort. Just like the analysis of
the evaluation results above, many indicators affecting comfort in
Tesla Model 3 were found and some improvement strategies were
proposed, which also contributed to the ergonomic design.
YANG ET AL.
|
11
6|CONCLUSION
In the increasingly intelligent automotive industry, the comfort
requirements of the corresponding intelligent cockpit are also
gradually increasing. Based on this, this article proposes a comfort
evaluation method for intelligent car cockpit comfort. After discus-
sion and research, 33 evaluation indicators for the current intelligent
cockpit were determined. The core of the evaluation model was to
define comfort by comparing people's perceived performance and
expectations on these indicators. It is also through the algorithm in
the model to get the indicators of dissatisfaction and the degree of
dissatisfaction. Finally, it is also possible to classify the level of impact
on comfort, making it easy to prioritize improvements.
The effectiveness of the method is verified in the evaluation of
the Tesla Model 3. According to its evaluation results, three levels
that affect comfort in this cockpit are divided, and the reasons for
discomfort are analyzed. The results obtained from the evaluation of
the cockpit in this article can also serve as a reference for other smart
car industries. Effective evaluation of this method can help designers
find problems, improve deficiencies, and improve industrial competi-
tiveness. Furthermore, it helps to promote the development of
intelligent cockpits to maximize passenger comfort. For other
industries with high comfort requirements, it also provides a strategy.
J. H. Lee et al. (2009) found that cultural environment and
cultural psychological factors also have an impact on personnel
comfort. Gender differences can also have an impact on comfort (Jin
et al., 2020). Although the basic information of the participants was
counted in the experiment, the influence of these factors on the
comfort evaluation was not considered.
In addition, to rule out different errors in the interior of the
intelligent cockpits of different cars, only the same style of cockpit
can be tested at a time, and the evaluation data obtained are only for
the cockpit of the evaluation. However, the evaluation results still
have certain enlightenment and suggestions for the cockpit design
and improvement of other intelligent cars. Thus, our future research
work will focus on some of the limitations mentioned above.
ACKNOWLEDGMENTS
This study was supported by the Open Research Fund of Sichuan Key
Laboratory of Vehicle Measurement, Control and Safety (szjj2018‐
130) and the Sichuan Province Innovation Training Project
(S202110650026 and S202110650028).
DATA AVAILABILITY STATEMENT
Data are available on request from the authors.
ORCID
Jian‐Jun Yang http://orcid.org/0000-0002-4760-5184
Yi‐Meng Chen http://orcid.org/0000-0002-9455-0286
REFERENCES
Amores, D., Tanin, E., & Vasardani, M. (2021). A proactive route planning
approach to navigation errors. International Journal of Geographical
Information Science,35(6), 1094–1130. https://doi.org/10.1080/
13658816.2020.1820508
Andargie, M. S., Touchie, M., & O'Brien, W. (2019). A review of factors
affecting occupant comfort in multi‐unit residential buildings.
Building and Environment,160, 106182. https://doi.org/10.1016/j.
buildenv.2019.106182
Anjani,S.,Kühne,M.,Naddeo,A.,Frohriep,S.,Mansfield,N.,Song,Y.,&
Vink, P. (2021). PCQ: Preferred Comfort Questionnaires for product
design. Work,68(s1), S19–S28. https://doi.org/10.3233/WOR-208002
Arena, F., Pau, G., & Severino, A. (2020). An overview on the current
status and future perspectives of smart cars. Infrastructures,5(7), 53.
https://doi.org/10.3390/infrastructures5070053
Asua, E., Gutiérrez‐Zaballa, J., Mata‐Carballeira, O., Ruiz, J. A., &
del Campo, I. (2022). Analysis of the motion sickness and the lack
of comfort in car passengers. Applied Sciences,12(8):3717. https://
doi.org/10.3390/app12083717
Baskar, A. D., & Arockiaraj, S. (2015). F‐geometric mean graphs.
Applications and Applied Mathematics: An International Journal
(AAM),10(2), 20–25.
Beggiato, M., Hartwich, F., & Krems, J. (2019). Physiological correlates of
discomfort in automated driving. Transportation Research Part F:
Traffic Psychology and Behaviour,66, 445–458. https://doi.org/10.
1016/j.trf.2019.09.018
Caballero‐Bruno, I., Töpfer, D., Wohllebe, T., & Hernández‐Castellano, P.
M. (2022). Assessing car seat posture through comfort and user
experience. Applied Sciences,12(7), 3376. https://doi.org/10.3390/
app12073376
Castellanos, J. C., & Fruett, F. (2014). Embedded system to evaluate the
passenger comfort in public transportation based on dynamical
vehicle behavior with user's feedback. Measurement,47, 442–451.
https://doi.org/10.1016/j.measurement.2013.08.068
Chen, H., Samarasinghe, P., & Abhayapala, T. D. (2015). In‐car noise field
analysis and multi‐zone noise cancellation quality estimation.Asia‐
Pacific Signal and Information Processing Association annual summit
and conference, APSIPA 2015, 773–778. https://doi.org/10.1109/
APSIPA.2015.7415377
Chen, Z. S., Liu, X. L., Rodríguez, R. M., Wang, X. J., Chin, K. S., Tsui, K. L., &
Martínez, L. (2020). Identifying and prioritizing factors affecting in‐
cabin passenger comfort on high‐speed rail in China: A fuzzy‐based
linguistic approach. Applied Soft Computing,95, 106558. https://doi.
org/10.1016/j.asoc.2020.106558
Choi, J.‐K., Kwon, Y.‐J., Jeon, J., Kim, K., Choi, H., & Jang, B. (2018).
Conceptual design of driver‐adaptive human–machine interface for
digital cockpit.International conference on information and communi-
cation technology convergence, ICTC 2018, 1005–1007. https://doi.
org/10.1109/ICTC.2018.8539644
Cvok, I., Deur, J., Eric Tseng, H., & Hrovat, D. (2019). Comparative
performance analysis of active and semi‐active suspensions with road
preview control.InThe IAVSD international symposium on dynamics of
vehicles on roads and tracks, 2019‐August, 1808–1818. https://doi.
org/10.1007/978-3-030-38077-9_206
Dimitrakopoulos, G. J., & Panagiotopoulos, I. E. (2021). In‐vehicle
infotainment systems: Using Bayesian networks to model cognitive
selection of music genres. IEEE Transactions on Intelligent
Transportation Systems,22(11), 6900–6909. https://doi.org/10.
1109/tits.2020.2997003
Du, X., Sun, C., Zheng, Y., Feng, X., & Li, N. (2021). Evaluation of vehicle
vibration comfort using deep learning. Measurement,173, 108634.
https://doi.org/10.1016/j.measurement.2020.108634
Elbanhawi, M., Simic, M., & Jazar, R. (2015). In the passenger seat:
Investigating ride comfort measures in autonomous cars. IEEE
Intelligent Transportation Systems Magazine,7(3), 4–17. https://doi.
org/10.1109/MITS.2015.2405571
Fan, J. Y., Xue, W. P., & Liu, G. H. (2017). Vibration control for vehicle active
suspension based on ANFIS method.36th Chinese control conference,
12
|
YANG ET AL.
CCC 2017, 9602–9606. https://doi.org/10.23919/ChiCC.2017.
8028889
Fiorillo, I., Nasti, M., & Naddeo, A. (2021). Design for comfort and social
interaction in future vehicles: A study on the leg space between
facing‐seats configuration. International Journal of Industrial
Ergonomics,83, 103131. https://doi.org/10.1016/j.ergon.2021.
103131
Gasnier, N., Denis, L., & Tupin, F. (2021). On the use and denoising of the
temporal geometric mean for SAR time series. IEEE Geoscience and
Remote Sensing Letters,19,1–5. https://doi.org/10.1109/LGRS.
2021.3051936
Goujard, B., Sakout, A., & Valeau, V. (2005). Acoustic comfort on board
ships: An evaluation based on a questionnaire. Applied Acoustics,
66(9), 1063–1073. https://doi.org/10.1016/j.apacoust.2005.01.001
Grönroos, C. (1988). Service quality: The six criteria of good perceived
service quality. Review of Business,9(3), 10–13. https://www.
proquest.com/scholarly-journals/service-quality-six-criteria-good-
perceived/docview/220949893/se-2
Guo, Y., Sun, Q., Su, Y., Guo, Y., & Wang, C. (2021). Can driving condition
prompt systems improve passenger comfort of intelligent vehicles?
A driving simulator study. Transportation Research Part F: Traffic
Psychology and Behaviour,81, 240–250. https://doi.org/10.1016/j.
trf.2021.06.007
Helldin, T., Falkman, G., Riveiro, M., & Davidsson, S. (2013). Presenting
system uncertainty in automotive UIs for supporting trust calibration
in autonomous driving. AutomotiveUI'13: Proceedings of the 5th
international conference on automotive user interfaces and interactive
vehicular applications, 210‐217. https://doi.org/10.1145/2516540.
2516554
Hiemstra‐van Mastrigt, S., Kamp, I., van Veen, S. A. T., Vink, P., & Bosch, T.
(2015). The influence of active seating on car passengers' perceived
comfort and activity levels. Applied Ergonomics,47, 211–219.
https://doi.org/10.1016/j.apergo.2014.10.004
Hölscher, C., Tenbrink, T., & Wiener, J. M. (2011). Would you follow your
own route description? Cognitive strategies in urban route planning.
Cognition,121(2), 228–247. https://doi.org/10.1016/j.cognition.
2011.06.005
Huang, S.‐C., Chen, B.‐H., Chou, S.‐K., Hwang, J.‐N., & Lee, K.‐H. (2016).
Smart car [application notes]. IEEE Computational Intelligence
Magazine,11(4), 46–58. https://doi.org/10.1109/MCI.2016.
2601758
James, T. (2012). Smart cars. Engineering & Technology,7(6), 50–51.
https://doi.org/10.1049/et.2012.0606
Jeong, M. G., Lee, E. B., Lee, M., & Jung, J. Y. (2019). Multi‐criteria route
planning with risk contour map for smart navigation. Ocean
Engineering,172,72–85. https://doi.org/10.1016/j.oceaneng.2018.
11.050
Jin, H., Liu, S., & Kang, J. (2020). Gender differences in thermal comfort on
pedestrian streets in cold and transitional seasons in severe cold
regions in China. Building and Environment,168, 106488. https://doi.
org/10.1016/j.buildenv.2019.106488
Jorlöv, S., Bohman, K., & Larsson, A. (2017). Seating positions and activities
in highly automated cars—a qualitative study of future automated
driving scenarios. Proceedings of International research conference on
the biomechanics of impact, IRCOBI 2017, 13–22. https://trid.trb.org/
view/1485922
Joshi, A., Kale, S., Chandel, S., & Pal, D. (2015). Likert scale: Explored and
explained. British Journal of Applied Science & Technology,7(4),
396–403. https://eclass.aspete.gr/modules/document/file.php/
EPPAIK269/5a7cc366dd963113c6923ac4a73c3286ab22.pdf
Kalaivani, R., & Lakshmi, P. (2013). Adaptive neuro‐fuzzy controller for
vehicle suspension system.2013 Fifth international conference on
advanced computing, ICoAC 2013, 236–240. https://doi.org/10.
1109/ICoAC.2013.6921956
Le, T. H. N., Zheng, Y., Zhu, C., Luu, K., & Savvides, M. (2016). Multiple
scale faster‐rcnn approach to driver's cell‐phone usage and hands on
steering wheel detection.InProceedings of the IEEE conference on
computer vision and pattern recognition workshops, CVPRW 2016,
46–53. https://doi.org/10.1109/CVPRW.2016.13
Lee, J. H., Jin, B. S., Ji, Y. G., & Yun, M. H. (2009). Cultural differences in
conceptual models of ride comfort for high‐speed trains. Human
Factors and Ergonomics in Manufacturing & Service Industries,19(2),
128–144. https://doi.org/10.1002/hfm.20141
Lee, S., Kyung, G., Kim, M., Choi, D., Choi, H., Hwang, K., Park, S.,
Kim, S. Y., & Lee, S. (2020). Shaping rollable display devices: Effects
of gripping condition, device thickness, and hand length on bimanual
perceived grip comfort. Human Factors,62(5), 770–786. https://doi.
org/10.1177/0018720819855225
Li, Y., & Bi, H. T. (2010). Vehicle driving comfort analysis for a cable‐stayed
bridge considering vehicle bridge coupled vibration. Proceedings
of 2010 WASE international conference on information engineering,
422–425. https://doi.org/10.1109/ICIE.2010.278
Lin, J. J. W., Parker, D. E., Lahav, M., & Furness, T. A. (2005). Unobtrusive
vehicle motion prediction cues reduced simulator sickness during
passive travel in a driving simulator. Ergonomics,48(6), 608–624.
https://doi.org/10.1080/00140130400029100
Liu, X., Sun, H., Gao, Y., Zhang, W., Ge, Y., & Qu, W. (2022). Exploring the
performance of click and slide gestures on large in‐vehicle touch
screens. Applied Ergonomics,99, 103613. https://doi.org/10.1016/j.
apergo.2021.103613
Ma, J., Ding, Y., & Gong, Z. (2018). Impact of screen size and structure of
IVIS on driving distraction. Proceedings of the IOP conference series:
Materials science and engineering, 2018, 042154. https://doi.org/10.
1088/1757-899X/452/4/042154
Maki, A., Dostal, L., Maruyama, Y., Sakai, M., Sugimoto, K., & Fukumoto, Y.
(2022). Theoretical determination of roll angular jerk of ships in
irregular beam seas using PDF Line Integral Method. Journal of
Marine Science and Technology,27(1), 163–172. https://doi.org/10.
1007/s00773‐021-00823-z
Mellert, V., Baumann, I., Freese, N., & Weber, R. (2008). Impact of sound
and vibration on health, travel comfort and performance of flight
attendants and pilots. Aerospace Science and Technology,12(1),
18–25. https://doi.org/10.1016/j.ast.2007.10.009
Menegon, L. D. S., Vincenzi, S. L., de Andrade, D. F., Barbetta, P. A.,
Vink, P., & Merino, E. A. D. (2019). An aircraft seat discomfort scale
using item response theory. Applied Ergonomics,77,1–8. https://doi.
org/10.1016/j.apergo.2018.12.015
Naddeo, A., Cappetti, N., & D'Oria, C. (2015). Proposal of a new
quantitative method for postural comfort evaluation. International
Journal of Industrial Ergonomics,48,25–35. https://doi.org/10.1016/
j.ergon.2015.03.008
Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., & Cao, W. (2020). A survey on
theories and applications for self‐driving cars based on deep learning
methods. Applied Sciences,10(8), 2749–2777. https://doi.org/10.
3390/app10082749
O'Connor, S. J., Trinh, H. Q., & Shewchuk, R. M. (2000). Perceptual gaps in
understanding patient expectations for health care service quality.
Quality Management in Health Care,9(2), 26–42. http://www.jstor.
org/stable/44950966
O'Connor, S. J., Trinh, H. Q., & Shewchuk, R. M. (2000). Perceptual gaps in
understanding patient expectations for health care service quality.
Health Care Management Review,25(2), 7–23. https://doi.org/10.
1097/00019514-200109020-00007
Olugbade, T., Cho, Y., Morgan, Z., El Ghani, M. A., & Bianchi‐Berthouze,
N. (2021). Toward intelligent car comfort sensing: New dataset and
analysis of annotated physiological metrics.9th international confer-
ence on affective computing and intelligent interaction, ACII 2021,
1–8. https://doi.org/10.1109/ACII52823.2021.9597393
YANG ET AL.
|
13
Pakdil, F., & Aydın, Ö. (2007). Expectations and perceptions in airline
services: An analysis using weighted SERVQUAL scores. Journal of
Air Transport Management,13(4), 229–237. https://doi.org/10.
1016/j.jairtraman.2007.04.001
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model
of service quality and its implications for future research. Journal of
Marketing,49(4), 41–50. https://doi.org/10.2307/1251430?uid
Park, J., Nam, C., & Kim, H.‐j (2019). Exploring the key services and players
in the smart car market. Telecommunications Policy,43(10), 101819.
https://doi.org/10.1016/j.telpol.2019.04.003
Salomo, S., Steinhoff, F., & Trommsdorff, V. (2003). Customer orientation
in innovation projects and new product development success—the
moderating effect of product innovativeness. International Journal of
Technology Management,26(5–6), 442–463. https://doi.org/10.
1504/IJTM.2003.003417
Seaborn, K., Miyake, N. P., Pennefather, P., & Otake‐Matsuura, M. (2021).
Voice in human–agent interaction: A survey. ACM Computing Surveys
(CSUR),54(4), 1–43. https://doi.org/10.1145/3386867
Sezgin, A., & Arslan, Y. Z. (2012). Analysis of the vertical vibration effects
on ride comfort of vehicle driver. Journal of Vibroengineering,14(2),
1392–8716.
Shangguan, W. B., Shui, Y., & Rakheja, S. (2017). Kineto‐dynamic design
optimisation for vehicle‐specific seat‐suspension systems. Vehicle
System Dynamics,55(11), 1643–1664. https://doi.org/10.1080/
00423114.2017.1317102
Silva, M. G. D. (2002). Measurements of comfort in vehicles. Measurement
Science and Technology,13(6), R41–R60. https://doi.org/10.1088/
0957-0233/13/6/201/meta
Singh, A. K., Singh, H. P., & Karmeshu (2014). Analysis of finite buffer queue:
Maximum entropy probability distribution with shifted fractional
geometric and arithmetic means. IEEE Communications Letters,19(2),
163–166. https://doi.org/10.1109/LCOMM.2014.2377236
Song, Y., Mao, F., & Liu, Q. (2019). Human comfort in indoor environment:
A review on assessment criteria, data collection and data analysis
methods. IEEE Access,7, 119774–119786. https://doi.org/10.1109/
access.2019.2937320
Stanglmeier, M. J., Schulte, F., Schauberger, G., Bichler, R. J., Schwirtz, A.,
& Paternoster, F. K. (2021). Effect of legroom proportions and
individual factors for sitting with crossed legs: Implications on the
interior design of automated driving vehicles. Ergonomics,64(11),
1393–1404. https://doi.org/10.1080/00140139.2021.1933201
Su, H., & Jia, Y. (2021). Study of human comfort in autonomous vehicles using
wearable sensors. IEEE Transactions on Intelligent Transportation Systems,
23, 11490–11504. https://doi.org/10.1109/TITS.2021.3104827
Sun, X., Cao, S., & Tang, P. (2021). Shaping driver‐vehicle interaction in
autonomous vehicles: How the new in‐vehicle systems match the
human needs. Ap plied Ergonomics,90, 103238. https://doi.org/10.
1016/j.apergo.2020.103238
Tao, Y. (2019, January). An intelligent voice interaction model based on
mobile teaching environment. Proceedings of the 2019 International
conference on intelligent transportation, big data & smart city, ICITBS
2019, 377–380. https://doi.org/10.1109/ICITBS.2019.00099
Terroir, J., Perrin, N., & Wild, P. (2022). Comfort of earplugs: Results of a
field survey based on the COPROD questionnaire. Ergonomics,65,
1–21. https://doi.org/10.1080/00140139.2021.1990415
Terroir, J., Perrin, N., Wild, P., Doutres, O., Sgard, F., Gauvin, C., &
Negrini, A. (2021). Assessing the comfort of earplugs: Development
and validation of the French version of the COPROD questionnaire.
Ergonomics,64(7), 912–925. https://doi.org/10.1080/00140139.
2021.1880027
Vink, P., & Lips, D. (2017). Sensitivity of the human back and buttocks: The
missing link in comfort seat design. Applied Ergonomics,58, 287–292.
https://doi.org/10.1016/j.apergo.2016.07.004
Wang, J., Xiang, Z.‐R., Zhi, J.‐Y., Chen, J.‐P., He, S.‐J., & Du, Y. (2021).
Assessment method for civil aircraft cabin comfort: Contributing
factors, dissatisfaction indicators, and degrees of influence.
International Journal of Industrial Ergonomics,81, 81. https://doi.
org/10.1016/j.ergon.2020.103045
Wang, K., Ho, C. H., Tian, C., & Zong, Y. (2020). Optical health analysis of
visual comfort for bright screen display based on back propagation
neural network. Computer Methods and Programs in Biomedicine,196,
105600. https://doi.org/10.1016/j.cmpb.2020.105600
Wen, T. W. X., Nor, N. S. B. M., Darus, I. Z. M., Zawawi, F. M., Wen, T. K.,
& Yamin, A. H. M. (2019). Arduino‐based data logger for driving
comfort analysis. Proceedings of the 2019 2nd International
conference on applied engineering, ICAE 2019, 1–4. https://doi.org/
10.1109/ICAE47758.2019.9221725
Yang, J., Xing, S., Chen, Y., Qiu, R., Hua, C., & Dong, D. (2022). An
evaluation model for the comfort of vehicle intelligent cockpits
based on passenger experience. Sustainability,14(11), 6827. https://
doi.org/10.3390/su14116827
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1993). The nature and
determinants of customer expectations of service. Journal of the
Academy of Marketing Science,21(1), 1–12. https://www.
researchgate.net/publication/343219652_The_Nature_and_
Determinants_of_Customer_Expectations_of_Luxury_Service
How to cite this article: Yang, J.‐J., Chen, Y.‐M., Xing, S.‐S., &
Qiu, R.‐Z. (2022). A comfort evaluation method based on an
intelligent car cockpit. Human Factors and Ergonomics in
Manufacturing & Service Industries,1–14.
https://doi.org/10.1002/hfm.20973
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