Auditory interfaces in automated driving: an international survey


Submitted 13 May 2015
Accepted 16 July 2015
Published 19 August 2015

Corresponding author
Pavlo Bazilinskyy,
p.bazilinskyy@tudelft.nl

Academic editor
Dan Stowell

Additional Information and
Declarations can be found on
page 22

DOI 10.7717/peerj-cs.13

Copyright
2015 Bazilinskyy and De Winter

Distributed under
Creative Commons CC-BY 4.0

OPEN ACCESS

Auditory interfaces in automated
driving: an international survey
Pavlo Bazilinskyy and Joost de Winter

Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials
Engineering, Delft University of Technology, Delft, The Netherlands

ABSTRACT
This study investigated peoples’ opinion on auditory interfaces in contemporary
cars and their willingness to be exposed to auditory feedback in automated driving.
We used an Internet-based survey to collect 1,205 responses from 91 countries. The
respondents stated their attitudes towards two existing auditory driver assistance
systems, a parking assistant (PA) and a forward collision warning system (FCWS),
as well as towards a futuristic augmented sound system (FS) proposed for fully
automated driving. The respondents were positive towards the PA and FCWS, and
rated the willingness to have automated versions of these systems as 3.87 and 3.77,
respectively (on a scale from 1 = disagree strongly to 5 = agree strongly). The re-
spondents tolerated the FS (the mean willingness to use it was 3.00 on the same
scale). The results showed that among the available response options, the female
voice was the most preferred feedback type for takeover requests in highly automated
driving, regardless of whether the respondents’ country was English speaking or not.
The present results could be useful for designers of automated vehicles and other
stakeholders.

Subjects Human-Computer Interaction, Autonomous Systems, Multimedia
Keywords Driverless car, Crowdsourcing, Survey, Questionnaire, Fully automated driving,
Highly automated driving, Auditory interface, Auditory feedback, Warning

INTRODUCTION
The development of automated driving systems
The development of automated driving technology is a key topic in modern transportation

research. A transition to automated driving may have a large positive influence on society

(European Commision, 2011). Each year more than 1,000,000 fatal accidents occur on roads

worldwide, with the lower-income countries being overrepresented (Gururaj, 2008; World

Health Organization, 2013). If automated driving systems are designed to be fully capable

and reliable, a very large portion of—yet probably not all—road traffic accidents could

be prevented (Goodall, 2014). Furthermore, traffic congestions, gas emissions, and fuel

consumption may reduce considerably thanks to automated driving systems.

The control of vehicles can be represented as a spectrum consisting of five levels:

(1) manual driving, (2) driver assistance, (3) partially automated driving, (4) highly

automated driving, and (5) fully automated driving (Gasser & Westhoff, 2012). The in-

troduction of driver assistance systems to the public took place in the 1990s with the release

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of Adaptive Cruise Control (ACC), a system that automates the longitudinal motion of the

vehicle (Beiker, 2012). Advancements in cameras, radars, lasers, and artificial intelligence

have led to the creation of systems that make partially automated driving possible. Partially

automated driving systems not only control the longitudinal motion of a vehicle, but also

its lateral motion. Examples of such systems are BMW’s Traffic Jam Assistant (BMW, 2013),

Volvo’s ACC with steer assist (Volvo, 2013a), and Mercedes’ Distronic Plus with Steering

Assist (Daimler, 2013). In partially automated driving, drivers are usually required to keep

their eyes focused on the road and intermittently touch the steering wheel.

Highly automated driving (HAD) is a next step. In HAD, the human can release the

hands from the steering wheel and is no longer required to monitor the road permanently

(e.g., Banks, Stanton & Harvey, 2014). However, humans still have an important role in the

control of highly automated vehicles (Alicandri & Moyer, 1992; Dingus, Hulse & Barfield,

1998; Levitan, Golembiewski & Bloomfield, 1998). In HAD, drivers can be asked to take

over control of the vehicle when required, for example, when the vehicle automation

cannot solve a task in a demanding traffic environment. The time between issuing a

‘takeover request’ and the required moment of transition of control from the vehicle to

the human is a critical design parameter (Gold et al., 2013). If the driver spends too much

time on reclaiming the control of the vehicle, or if the driver does not comprehend the

warning signal sent by the vehicle, an accident may result. Clearly, the design of appropriate

feedback is essential for the successful introduction of HAD to the public roads. Indeed,

inappropriate feedback is regarded as a primary cause of automation-induced accidents

(Norman, 1990).

Fully automated driving (FAD) will be the next and final iteration in automated driving.

People have been envisioning this step in the development of transportation for a long

time. Almost half a millennium ago, Leonardo Da Vinci envisioned a pre-programmed

clockwork cart (Weber, 2014). In 1939 during the New York World’s Fair, General Motors

presented their vision of the world 20 years into the future (1959–1960). In their Futurama

exhibition, they introduced a concept of automated highways with trench-like lanes for

separating traffic (Wetmore, 2003). In 1953, the futurist Isaac Asimov wrote a short story

‘Sally’ that pictured a situation where only cars that did not require a human driver were

allowed on the roads.

FAD offers numerous potential benefits. It could reduce stress and allow the operator to

engage in non-driving tasks such as working, using in-vehicle entertainment, or resting

(e.g., Jamson et al., 2011; Llaneras, Salinger & Green, 2013). Furthermore, FAD is a

recommended solution for achieving an optimal traffic flow, for example by means of

platooning on highways (Bergenhem et al., 2012; Varaiya, 1993). The Google Driverless Car

is one of the existing prototypes of FAD (Markoff, 2010). However, this particular vehicle

does not fully comply with the principles of FAD; in reality, the Google Driverless Car relies

on accurate three-dimensional maps of the environment and currently cannot cope with

all dynamic environments of high complexity. It requires considerable advances in sensing

and artificial intelligence before FAD becomes practically feasible on all public roads.

Continental, a leading German manufacturer specialising on components for automotive

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industry, predicts that FAD will be launched in the year 2025 (Continental, 2012), whereas

some voices have argued that FAD will never happen (Gomes, 2014; Underwood, 2014;

Yoshida, 2014).

Although automated driving systems are expected to improve safety, certain side effects

may occur regarding the human factor (e.g., Bainbridge, 1983; Desmond, Hancock &

Monette, 1998; Merat et al., 2012; Brandenburg & Skottke, 2014). A degraded reaction

time to critical events has been found among drivers exposed to ACC (Stanton, Young

& McCaulder, 1997; Stanton et al., 2001; Rudin-Brown & Parker, 2004; Larsson, Kircher

& Hultgren, 2014), and this issue is likely to be aggravated in higher levels of automated

driving (De Winter et al., 2014; Strand et al., 2014). Furthermore, it is expected that

people who will be driving highly and fully automated cars will suffer from a reduction

of their manual control skills, similar to pilots in highly automated airplanes (Ebbatson,

2009; Scallen, Hancock & Duley, 1995). The development of effective feedback systems is

considered important in supporting operator’s sustained attention, also called vigilance

(Heikoop et al., submitted for publication).

Auditory displays
As mentioned above, unless the driving task is fully automated, an appropriate feedback

system is required that warns and/or informs the human when automation mode

changes are required. The present study investigated the potential of auditory feedback

in automated driving. The auditory modality has several important characteristics: (1)

it is omnidirectional. That is, unlike visual cues, auditory cues can be received from any

direction. This is especially important in automated driving, during which the driver may

not be attending to the road and dashboard; (2) the auditory sense can receive information

at almost all times; (3) sound is transient, that is, unlike visual information which can be

continuously available, information passed in the form of sound is only available at that

particular moment; (4) although auditory cues may be masked by other sounds, humans

have the ability to selectively focus on one sound when multiple streams of sound are

available, also known as the cocktail party effect (Bregman, 1990; Cooke & Ellis, 2001;

Hermann, Hunt & Neuhoff, 2011; Wickens & Hollands, 2013).

An advantage of sound is that it is possible to use language, which may be more

informative as compared to the information conveyed with haptic or visual interfaces.

Because of the aforementioned qualities of sound, auditory displays are used in a variety

of applications, especially in those cases where the user needs to be alerted or where

additional visual load has to be avoided. For example, the majority of present route

navigation devices use voice and sound messages to give directions to their users (Holland,

Morse & Gedenryd, 2002), and flight crews use auditory signals to get informed about

proximate aircraft or to obtain directional information (e.g., Begault, 1993; Bronkhorst,

Veltman & Van Breda, 1996). An auditory interface in combination with tactile feedback

was suggested in a driving simulator study (Ho, Reed & Spence, 2007) as an optimal

warning system for collision avoidance. The auditory modality has potential not only as

a warning method, but also for providing input to the machine (e.g., speech interfaces).

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Literature reviews (Stanton & Edworthy, 1999; Barón & Green, 2006) suggest that people

drive ‘better’ (i.e., lower lane variation, steadier speed) when auditory interfaces are

employed in a manually driven car.

Auditory feedback can be delivered as a pre-recorded voice or as an artificial sound

warning/message. The term earcon refers to a brief auditory message (e.g., a tune or a

sound of a bell) that represents a certain event or object. Earcons have been introduced

to desktop computers to complement visual icons (Mynatt, 1990; Belz, Robinson &

Casali, 1999; Hermann, Hunt & Neuhoff, 2011). Previous research has shown that a

female voice is favoured over a male voice in route navigation devices (Large & Burnett,

2013). However, national or cultural differences seem to exist, where in some cases, the

male voice is preferred over the female voice. In 2010, BMW supposedly had to recall its

navigating system in Germany because male drivers disliked the idea of following orders

communicated via a female voice (Takayama & Nass, 2008), and Apple recently added

the option of a male voice to their voice control system Siri (Bosker, 2013). In a driving

simulator study by Jonsson & Dahlbäck (2011), non-native speakers of English responded

more accurately to route instructions provided by a female voice than to route instructions

provided by a male voice.

Auditory systems in current vehicles: parking assistant and
forward collision warning system
Modern vehicles often include systems that assist in driving and increase road safety. Such

systems support drivers by providing auditory/visual/haptic warning messages and by

taking over control of some of the driving tasks. In the present survey, we investigated the

opinion of people on two existing auditory systems: a parking assistant (PA) and a forward

collision warning system (FCWS).

The first generations of PAs were so-called parking sensors, which produce warning

sounds (beeps) when the car gets too close to a nearby object while parking, using

ultrasonic or electromagnetic sensors (BMW, 2013; Toyota, 2014; Volkswagen, 2014). Some

recent PA systems take over the positioning of the vehicle during parking, leaving the

control of acceleration and deceleration to the driver (Volkswagen, 2014). Other PAs take

over control of the parking process entirely, as can be seen in the Toyota Prius 2015 and

BMW X5 (BMW, 2014; Toyota, 2014).

A FCWS is a system that provides a warning sound when a vehicle is rapidly approach-

ing a vehicle in front. FCWSs have the potential to prevent a large portion of rear-end

collisions (Jamson, Lai & Carsten, 2008; Kingsley, 2009; Kessler et al., 2012). If a potential

accident is detected by the FCWS, the system either gives a warning to the driver (Honda,

2014) or engages in emergency braking and/or steers way from the object (Volvo, 2013b).

Most FCWS detect vehicles with the help of computer vision (Srinivasa, 2002; Dagan et al.,

2004), an approach that is used by companies like Honda and BMW (BMW, 2013; Volvo,

2013b; Honda, 2014) and/or radars (Volvo, 2013b; Ford, 2014; Honda, 2014; Mercedes-Benz,

2014). Both approaches have limitations, and the system may not issue warnings or stop

the vehicle in bad weather or in other situations where the sensors are obscured by external

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factors. The introduction of vehicle-to-vehicle (V2V) communication may increase the

efficiency and capabilities of collision warning systems (e.g., Miller & Huang, 2002).

Eighty-eight percent of owners of Volvo cars surveyed by Braitman et al. (2010) reported

always having the FCWS turned on.

It is expected that both PAs and FCWSs will remain in future partially and highly auto-

mated vehicles. However, these technologies will become obsolete with the introduction of

FAD because both parking and collision avoidance will be handled without any input from

the human driver.

‘Augmented/spatial’ sound system for fully automated driving
Auditory warning signals will not be required in FAD, because in FAD the automation

by definition takes care of all possible emergency conditions. This study proposes an

experimental setup aimed at the three-dimensional augmentation of sound surrounding

a vehicle, hereafter referred to as the ‘future system’ (FS), which could be used in FAD for

entertainment and comfort. Three-dimensional sound is being developed as a means for

providing feedback to humans (Lumbreras, Sánchez & Barcia, 1996; Garas, 2000; Rozier,

2000; Godinho, António & Tadeu, 2001; Dobler, Haller & Stampfl, 2002).

Our proposed FS filters out unwanted sounds (e.g., tire/engine noise coming from

vehicles in the vicinity) and amplifies desired sounds (e.g., sound of birds singing in a

park). We envision that such a system could be used in future fully automated vehicles.

Vehicles driving fully automatically have full control of the vehicle and must have reliable

detection capabilities of the environment. Drivers of such vehicles will not be required

to pay attention to the processes that take place in the environment surrounding the car.

Hence, a spatial augmentation of sounds that a driver prefers to hear and simultaneous

cancelation of unwanted sounds may enhance the pleasure of being engaged in FAD. Such

system will probably have to be configurable: drivers must have the option to select which

sounds they want to augment and which sounds they wish to filter out, as well as to adjust

the volume of these sounds.

The aim of the present survey study
As mentioned above, feedback is important in HAD, especially regarding transitions of

control. It is relevant for the development of automated driving systems to know what

types of interfaces people want and need. Because automated cars do not exist yet on the

consumer market, it is impossible to test such research questions in an ecologically valid

environment, except through driving simulator research.

The present study was undertaken from a different point of view. We proceeded on the

basis that respondents were asked to imagine automated driving scenarios. The aim of the

present study was to investigate the opinion of people on two existing auditory displays (PA

& FCWS) as well as the augmented sound system ‘FS’. The respondents were asked to judge

two qualities of the systems—helpfulness and annoyance—and state whether they would

consider using automated versions of such systems in the future. In addition, we asked

people to report their preferred type of feedback for takeover requests in HAD. Statistical

associations between self-reported driving style as measured with the Driver Behaviour

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Questionnaire (DBQ), yearly mileage, number of accidents, and opinions of respondents

on the qualities of the proposed systems were assessed.

The hypothesis that people from non-English speaking countries prefer a female voice

to a male voice in automated driving systems was also tested. Additionally, the respondents

were asked to provide their general thoughts on the concept of automated driving in a

free-response question. Finally, the respondents provided their opinion on the year of

introduction of fully automated driving in their country of residence. Results of these

analyses were compared with findings from two previous surveys that asked questions

related to other aspects of automated vehicles (De Winter et al., 2015; Kyriakidis, Happee &

De Winter, 2015).

METHODS
Survey
A survey containing 31 questions was developed with the online tool CrowdFlower (www.

crowdflower.com). Table 1 shows the questions of the survey as well as the corresponding

coding. The full survey is included in the Supplemental Information 1. The survey was

targeted towards reasonably educated persons without knowledge of automated driving.

A previous survey indicated that people who work on CrowdFlower-based surveys have

mostly undergraduate degrees (Kyriakidis, Happee & De Winter, 2015).

The present survey introduced in plain language three levels of driving: manual driving,

partially automated driving, and fully automated driving. Manual driving was referred to

as “normal (non automated) cars”. The explanation of partially driving was provided as

follows: “Imagine again that you are driving in an automated car (that can perform certain

tasks without any interaction from the humans in the car). However, the automation

cannot handle all possible situations, and you sometimes have to take over control”.

Respondents were asked to imagine fully automated driving as follows: “Imagine a fully

automated car (no steering wheel) that drives completely on its own with no manual

interaction”.

The survey contained questions on the person’s age, gender, driving frequency, mileage,

and accident involvement. The questions asking participants to provide information

on their driving style were based on the violations scale of the DBQ, as used by

De Winter (2013).

The respondents were asked to express their opinion on two currently existing systems

and one proposed setup that could be used during fully automated driving. Specifically,

we asked respondents about (1) a parking assistant (PA) in a manually driven car that

produces warning sounds (beeps) when the car gets too close to a nearby object while

parking, (2) a forward collision warning system (FCWS) in a manually driven car that

provides a warning sound when a car is rapidly approaching another car in front, and (3)

a future augmented surround sound system in a fully automated vehicle (FS). The FS was

described as follows: “Now imagine that this fully automated car records what is happening

outside and plays it via speakers inside the car, informing the occupants about the outside

environment. In other words, those who sit in the car can hear what is happening outside

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Table 1 All survey items.

Variable Question Full question as reported in the survey Used coding

Instr Q1 Have you read and understood the above
instructions?

1 = Yes, 2 = No

Gender Q2 What is your gender? (1 = female, 2 = male) −1 = I prefer not to respond, 1 = Female,
2 = Male

Age Q3 What is your age? Positive integer value

DriveFreq Q4 On average, how often did you drive a vehicle in
the last 12 months?

−1 = I prefer not to respond, 1 = Never,
2 = Less than once a month, 3 = Once a
month to once a week, 4 = 1 to 3 days a
week, 5 = 4 to 6 days a week, 6 = Every
day

KmYear Q5 About how many kilometres (miles) did you
drive in the last 12 months?

−1 = I prefer not to respond, 1 =
0, 2 = 1–1,000, 3 = 1,001–5,000,
4 = 5,001–15,000, 5 = 15,001–20,000,
6 = 20,001–25,000, 7 = 25,001–35,000,
8 = 35,001–50,000, 9 = 50,001–100,000,
10 = more than 100,000

NrAcc Q6 How many accidents were you involved in when
driving a car in the last 3 years? (please include
all accidents, regardless of how they were caused,
how slight they were, or where they happened)?

−1 = I prefer not to respond, 1 = 0,
2 = 1, 3 = 2, 4 = 3, 5 = 4, 6 = 5,
7 = More than 5

Vangered Q7 How often do you do the following?: Becoming
angered by a particular type of driver, and
indicate your hostility by whatever means you
can.

−1 = I prefer not to respond, 1 = 0 times
per month, 2 = 1 to 3 times per month,
3 = 4 to 6 times per month, 4 = 7 to 9
times per month, 5 = 10 or more times per
month

Vmotorway Q8 How often do you do the following? Disregard-
ing the speed limit on a motorway.

−1 = I prefer not to respond, 1 = 0 times
per month, 2 = 1 to 3 times per month,
3 = 4 to 6 times per month, 4 = 7 to 9
times per month, 5 = 10 or more times per
month

Vresident Q9 How often do you do the following? Disregard-
ing the speed limit on a residential road.

−1 = I prefer not to respond, 1 = 0 times
per month, 2 = 1 to 3 times per month,
3 = 4 to 6 times per month, 4 = 7 to 9
times per month, 5 = 10 or more times per
month

Vfollowing Q10 How often do you do the following? Driving so
close to the car in front that it would be difficult
to stop in an emergency.

−1 = I prefer not to respond, 1 = 0 times
per month, 2 = 1 to 3 times per month,
3 = 4 to 6 times per month, 4 = 7 to 9
times per month, 5 = 10 or more times per
month

Vrace Q11 How often do you do the following? Racing away
from traffic lights with the intention of beating
the driver next to you.

−1 = I prefer not to respond, 1 = 0 times
per month, 2 = 1 to 3 times per month,
3 = 4 to 6 times per month, 4 = 7 to 9
times per month, 5 = 10 or more times per
month

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Table 1 (continued)

Variable Question Full question as reported in the survey Used coding

Vhorn Q12 How often do you do the following? Sounding
your horn to indicate your annoyance with
another road user.

−1 = I prefer not to respond, 1 = 0 times
per month, 2 = 1 to 3 times per month,
3 = 4 to 6 times per month, 4 = 7 to 9
times per month, 5 = 10 or more times per
month

Vphone Q13 How often do you do the following? Using a
mobile phone without a hands free kit.

−1 = I prefer not to respond, 1 = 0 times
per month, 2 = 1 to 3 times per month,
3 = 4 to 6 times per month, 4 = 7 to 9
times per month, 5 = 10 or more times per
month

Vmean N/A Mean for Q7–12 Numeric value

PApast Q14 In the past month, did you drive a car with a
parking assistant?

−1 = I prefer not to respond, 1 = I do not
know, 2 = No, 3 = Yes

PAhelp Q15 A parking assistant is helpful. −1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

PAannoy Q16 A parking assistant is annoying. −1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

PAopin Q17 What do you think are the disadvantages of a
parking assistant?

Textual response

PAfut Q18 I would like to have a system in my car that can
park the car automatically, just by pressing a
button.

−1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

FCWSpast Q19 In the past month, did you drive a car with a
forward collision warning system?

−1 = I prefer not to respond, 1 = I do not
know, 2 = No, 3 = Yes

FCWShelp Q20 A forward collision warning system is helpful. −1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

FCWSannoy Q21 A forward collision warning system is annoying. −1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

FCWSfut Q22 I would you like to have a system in my car
that brakes automatically to avoid collisions
(Autonomous Emergency Braking).

−1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

FCWSopin Q23 What do you think are the disadvantages of a
forward collision warning system?

Textual response

FSannoy Q24 I believe that this type of surround sound system
would be annoying.

−1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

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Table 1 (continued)

Variable Question Full question as reported in the survey Used coding

FSfut Q25 I would prefer to use such a sound system
instead of opening the window, when driving
through a scenic place (for example, a national
park).

−1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

FSopin Q26 What would be the advantages and the disad-
vantages of such sound system?

Textual response

TORint Q27 Now imagine again that you are driving in an
automated car (that can perform certain tasks
without any interaction from the humans in the
car). However, the automation cannot handle all
possible situations, and you sometimes have to
take over control. What type of warning signal
would you like to receive in case manual take
over is required?

1 = Warning sound: one beep,
2 = Warning sound: two beeps,
3 = Warning sound: horn sound,
4 = Warning sound: bell sound,
5 = Warning light,
6 = Visual warning message projected
on windscreen ‘Take over please’,
7 = Vibrations in your seat,
8 = Vibrations in your steering wheel,
9 = Vibrations in your seatbelt,
10 = Vibrations in the floor,
11 = Female voice: ‘Take over please’,
12 = Male voice: ‘Take over please’,
13 = Other,
14 = None of the above

TORintot Q28 If you answered ‘Other’ in the previous question,
please specify what type of warning signal you
would like to receive in the described scenario.

Textual response

FACpref Q29 I would prefer to drive in a fully automated car
rather than a normal (non automated) car.

−1 = I prefer not to respond, 1 = Disagree
strongly, 2 = Disagree a little, 3 = Neither
agree nor disagree, 4 = Agree a little,
5 = Agree strongly

YearAuto Q30 In which year do you think that most cars will be
able to drive fully automatically in your country
of residence?

Year

Comm Q31 Please provide any suggestions which could help
engineers to build safe and enjoyable automated
cars.

Textual response

SurvTime Survey time (derived from results generated by
CrowdFlower)

Seconds

even when their windows are closed. Sound volume in such system could be adjusted;

particular noise (for example sound coming from another vehicle) could be filtered out.

Such a system could, for example, be used during a leisure drive through a park on a hot

day”. Illustrations belonging to the three scenarios (i.e., PA, FCWS, FS) were used in the

survey (Fig. 1). No auditory examples were used. The illustrations were uploaded to a

remote site in order to be embedded to the survey. Supplemental Information 1 contains

the XML code used to create the survey. If one wishes to add images to a CrowdFlower

survey, the suggested method could be used.

The respondents were asked to indicate disadvantages of the PA (Q17) and FCWS

(Q23) and to indicate advantages and disadvantages of the FS (Q26) by means of textual

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Figure 1 Illustrations belonging to the three scenarios presented to the respondents. (A) Parking
assistant (PA); (B) Forward collision warning system (FCWS); (C) Future system (FS).

responses. The respondents also had the opportunity to indicate the preferred mode of

feedback for receiving a takeover request (Q27 & Q28). In the last question (Q31), they

were asked to “provide any suggestions, which could help engineers to build safe and

enjoyable automated cars”. Giving a response to this last free-response question was

optional. All examples of given comments shown in this article are direct quotes from

the responses; no grammatical or syntactic errors were corrected. The respondents had

to complete all questions (except Q28 & Q31), and each question had an I prefer not to

respond response option.

Configuration of CrowdFlower
In the instructions, the respondents were informed that they would need approximately

10 min to complete the survey. The task expiration time was set to 30 min. Contributors

from all countries were allowed to participate in the survey, in order to collect data from

an as large and diverse population as possible. Moreover, the lowest level of experience

of contributors, ‘Level 1 contributors’, was selected. This level of experience accounts

for 60% of completed work on CrowdFlower. As a result, the survey was available to a

large number of workers, which allowed reaching a relatively diverse group of users of the

platform. Completing the survey more than once from the same IP address was allowed

(note, however, that multiple responses from the same IP address were filtered out in our

analyses, see results section). For the completion of the survey a payment of $0.15 was

offered, and 2,000 responses were collected. The study was preceded by a pilot test with

10 respondents. The pilot test did not lead to any changes in the survey, and these 10

respondents were not included in the analysis.

Analyses
Descriptive statistics (i.e., mean, median, standard deviation, skewness, and number of

responses) were calculated for each of the variables. The skewness was calculated as

the third central moment divided by the cube of the standard deviation. A Spearman

correlation matrix among the variables was created. The first author manually performed

the analysis of textual responses (Q17, Q23, Q26, Q28, & Q31).

CrowdFlower automatically provides the respondent’s country based on his/her IP

address. We analysed the preferences of people from English speaking countries, as

defined by the UK government (UK Visas & Immigration, 2014: Antigua and Barbuda,

Australia, Bahamas, Barbados, Belize, Canada, Dominica, Grenada, Ireland, Jamaica, New

Zealand, Saint Lucia, Trinidad and Tobago, United Kingdom, and the United States) versus

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non-English speaking countries regarding the use of a male or female voice for supporting

takeover requests during highly automated driving. Supplemental Information 1 contains

the MATLAB script used to analyse the data.

Ethics statement
All data were collected anonymously. The research was approved by the Human Research

Ethics Committee (HREC) of the Delft University of Technology. Documented informed

consent was obtained via a dedicated survey item asking whether the respondent had read

and understood the survey instructions.

RESULTS
Number of respondents and respondent satisfaction
In total, 2,000 surveys were completed. The responses were gathered on 2 September

2014 between 15:00 and 20:15 (CET). The survey received an overall satisfaction rating

of 4.4 out of 5.0. Additionally, the respondents ranked clearness of the instructions as

4.4/5.0, fairness of the questions as 4.2/5.0, easiness of the survey as 4.2/5.0, and the offered

payment as 4.1/5.0.

Data filtering
The respondents who indicated they had not read the instructions (N = 10), who indicated

they were under 18 and thereby did not adhere to the survey instructions (N = 6), who

chose the I prefer not to respond or I do not know options in one or more of the multiple

choice questions (N = 231), who indicated they never drive (N = 193), or who indicated

they drive 0 km per year (N = 191) were excluded from the analyses. Since no limitations

were applied on the number of responses that could be generated per IP address, some

people completed the survey more than once. Such behaviour was seen as an indication

that these persons participated in the survey primarily because of monetary gain. Thus, we

applied a strict filter, and all data generated from non-unique IP addresses were removed

(N = 465). In total, 795 surveys were removed, leaving 1,205 completed surveys for further

analysis.

For the question “In which year do you think that most cars will be able to drive

fully automatically in your country of residence?”, non-numeric responses (e.g., a year

complemented by words such as “maybe 2030”, or “never”) and answers before the year

2014 were excluded, leaving 1,082 numeric responses.

Analyses at the individual level
The 1,205 respondents were from 91 countries (all 2,000 responses were associated with

95 countries). Descriptive statistics for all variables are listed in Table 2. The respondents

took on average 9.2 min to complete the survey (SD = 5.6 min, median = 7.7 min).

The Supplemental Information 1 contains the entire Spearman correlation matrix.

The correlation coefficients between variables that related to questions about the PA,

FCWS, and FS (PApast, PAhelp, PAannoy, PAfut, FCWSpast, FCWShelp, FCWSannoy,

FCWSfut, FSannoy, & FSfut) on the one hand, and Age, DriveFreq, KmYear, NrAcc, the

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Table 2 Descriptive statistics for the survey items (N = 1,205). The response option “I don’t know”
was omitted for the variables PApast and FCWSpast.

Variable Mean Median SD Skewness Min Max

Gender 1.75 2 0.43 −1.17 1 2

Age 31.94 30 10.49 1.04 18 73

DriveFreq 4.72 5 1.21 −0.66 2 6

KmYear 4.09 4 1.78 0.92 2 10

NrAcc 1.47 1 0.94 2.88 1 7

Vangered 1.86 2 0.86 1.46 1 5

Vmotorway 1.85 2 1.05 1.54 1 5

Vresident 1.70 1 1.01 1.79 1 5

Vfollowing 1.45 1 0.77 2.07 1 5

Vrace 1.32 1 0.69 2.62 1 5

Vhorn 1.86 2 1 1.41 1 5

Vphone 1.64 1 1.01 1.84 1 5

Vmean 1.67 1.57 0.57 1.36 1 4.71

PApast 2.27 2 0.45 1.03 2 3

PAhelp 4.33 5 0.88 −1.38 1 5

PAannoy 2.35 2 1.18 0.39 1 5

PAfut 3.87 4 1.24 −0.93 1 5

FCWSpast 2.10 2 0.30 2.65 2 3

FCWShelp 4.11 4 1.04 −1.14 1 5

FCWSannoy 2.56 3 1.26 0.27 1 5

FCWSfut 3.77 4 1.22 −0.80 1 5

FSannoy 3.21 3 1.22 −0.18 1 5

FSfut 3.00 3 1.29 −0.09 1 5

FACpref 3.01 3 1.33 −0.05 1 5

YearAuto 2,078.33 2,030 713.77 30.73 2,014 25,000

SurvTime 553.95 462 338.41 1.34 58 1,810

DBQ variables (Vangered, Vmotorway, Vresident, Vfollowing, Vrace, Vhorn, & Vphone),

YearAuto, and SurvTime, on the other, were overall small, between −0.15 and 0.25.

The respondents’ mean and median age were 31.9 and 30 years, respectively. Figure 2

shows the distribution of the respondents in 5-year wide age groups. 75.2% of the

respondents were male (906 men vs. 299 women). The frequencies of the answers are

provided in Table 3.

Figure 3 shows that the respondents expected most cars to be able to drive in fully

automated mode in their countries of residence around 2,030 (median response), with a

highly skewed distribution.

The respondents were asked to provide their opinion on two characteristics of the PA

and FCWS systems, annoyance and helpfulness, and whether they would be willing to

have automated versions of such systems in their own cars (Q18 for the PA & Q22 for the

FCWS), all questions on a scale from 1 (disagree strongly) to 5 (agree strongly). Figure 4

shows the results for these questions.

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Table 3 Frequencies of answers.

Variable 1 2 3 4 5 6 7 8 9 10

Gender 299 906

DriveFreq 0 79 108 293 313 412

KmYear 0 250 245 337 148 76 77 49 16 7

NrAcc 855 226 79 19 13 6 7

Vangered 417 629 96 33 30

Vmotorway 541 466 97 39 62

Vresident 663 379 78 32 53

Vfollowing 807 295 70 20 13

Vrace 939 182 60 15 9

Vhorn 513 480 123 44 45

Vphone 730 304 89 34 48

PApast 17 865 323

PAhelp 14 39 134 367 651

PAannoy 383 297 290 196 39

PAfut 86 109 175 338 497

FCWSpast 28 1,058 119

FCWShelp 34 68 178 373 552

FCWSannoy 331 254 324 208 88

FCWSfut 82 123 203 377 420

FSannoy 126 218 346 309 206

FSfut 204 222 312 301 166

FACpref 204 252 264 292 193

Figure 2 Distribution of the age of the respondents aged between 18 and 54 years.

Figure 5 shows associations between the opinion of the respondents on annoyance

and helpfulness of the PA and FCWS and their age divided into 5-year wide bins. Figure

5A shows that younger respondents found that both the PA (ρ = −0.05,p = .103) and

the FCWS (ρ = −0.14,p < .001) were more annoying, but these effects were weak. The

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Figure 3 Distribution of responses for the question: “In which year do you think that most cars will
be able to drive fully automatically in your country of residence?” (Q30). Years were divided into
5-year-wide bins.

Figure 4 Opinion of the respondents on whether a PA and FCWS are helpful and annoying, and
whether they would like to have automated versions of such systems in their cars in the future.

Spearman correlation between the respondents’ age and the reported annoyance of the

FS was weak as well (ρ = 0.06,p = .035). Figure 5B shows that the perceived helpfulness

of the FCWS (ρ = 0.12,p < .001) slightly increased with age. People who found the PA

annoying typically indicated that the FCWS was also annoying (ρ = 0.47, p < .001), and

respondents who thought that the PA was helpful, considered the FCWS to be helpful as

well (ρ = 0.34,p < .001).

Figure 6 shows the respondents’ opinion on the proposed future system. The

respondents were asked whether they would find such a system annoying (Q24) and

whether they would prefer to use such a system instead of opening windows while driving

in a fully automated car through a scenic place (Q25). A large portion of the respondents

was neutral in their responses: 346 people chose the option Neither agree nor disagree in

Q24, and 312 persons chose the same option in Q25.

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Figure 5 Opinion on the annoyance and helpfulness of the parking assistant (PA), forward collision
warning system (FCWS), and future system (FS). (A) Opinion on the annoyance of the PA (Q16), FCWS
(Q21), and FS (Q24) as a function of age; (B) Opinion on the helpfulness of the PA (Q15) and FCWS
(Q20) as a function of age. Age was divided into 5-year-wide bins.

Figure 6 Distribution of opinions on whether the proposed future system (FS) would be annoying
(Q24) and whether the respondents would prefer the system to opening windows in fully automated
cars (Q25).

In Q27 the respondents were asked to report on the types of feedback that they would

like to be supported by in case of a takeover request during highly automated driving. The

respondents were allowed to select multiple options. Figure 7 shows that a large number

of people preferred auditory feedback provided by a female voice saying ‘Take over please’

(N = 514). The number of respondents who chose the option with the male voice was

considerably lower (N = 244). Figure 7 makes a distinction between the numbers of female

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Figure 7 Preference for particular takeover requests during highly automated driving. Numbers of
respondents who indicated a preference for a particular takeover request during highly automated driving
in the question: “Now imagine again that you are driving in an automated car (that can perform certain
tasks without any interaction from the humans in the car). However, the automation cannot handle all
possible situations, and you sometimes have to take over control. What type of warning signal would you
like to receive in case manual take over is required?” (Q27). Each bar is supplemented by the corresponding
‘risk ratios’ of female respondents, calculated as the proportion of females who indicated this answer
divided by the proportion of males who indicated this answer. If the risk ratio is greater than 1, females
are overrepresented. Conversely, if the risk ratio is smaller than 1, females are underrepresented. 95%
confidence intervals are shown in parentheses.

and male respondents. It is apparent that both female and male respondents preferred the

female over the male voice.

Other types of auditory feedback were reported in the following order: two beeps

(N = 375), one beep (N = 195), a bell sound (N = 194), and a horn sound (N = 135). The

respondents indicated a high level of support for both visual signals offered in the question:

a warning message projected on the windscreen ‘Take over please’ (N = 429) and a warning

light (N = 406). However, respondents showed a relatively low level of acceptance of the

offered variations of a vibration interface: vibrations in the seat (N = 341), vibrations in

the steering wheel (N = 179), vibrations in the seatbelt (N = 117), and vibrations in the

floor (N = 64). Furthermore, the results seem to suggest that female respondents were less

likely than male respondents to prefer a female voice.

Figure 8 shows the opinion of the respondents on the combinations of warning signals.

The figure shows that most people (N = 188) preferred a sound signal (i.e., one or two

beeps, a horn, or bell) without additional information. A large number of people indicated

that they would like to receive a combination of all four modalities (N = 170) or the

combination of a sound signal, a visual message, and a voice (N = 101).

Cross-national differences in opinion for feedback for takeover
requests
Next, we tested the hypothesis whether peoples’ preference for a female and male voice in

supporting takeover requests in highly automated driving was different between English

and non-English speaking countries. Figure 9 presents a scatter plot, showing the numbers

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Figure 8 Preference to combinations of types of signals for aiding takeover requests during highly
automated driving (Q27). All possible combinations are listed. Hence, the total number of respondents
adds up to 1,205.

Figure 9 Numbers of respondents from English and non-English speaking countries who indicated
a preference for a male and a female voice for a takeover request during highly automated driving
(Q27). The dashed line represents the ratio between the number of respondents who preferred a female
voice and the number of respondents who preferred a male voice. The solid line is the line of unity. No
labels are shown for countries with five or less respondents indicating a male voice, to support the clarity
of the figure. Country abbreviations are listed according to ISO 3166-1 alpha-3.

of respondents per country who indicated that they would like to receive a female or a male

voice. The overall percentage of respondents who expressed preference for a female voice

was 43% (514/1,205), and the overall percentage of people who expressed preference for a

male voice was 20% (244/1,205). The corresponding percentages were 42% (71/168) and

22% (37/168) for English speaking countries, and they were 43% (443/1,037) and 20%

(207/1,037) for non-English speaking countries. The differences between English speaking

countries and non-English speaking countries were not statistically significant (female

voice: RR = 0.99, 95% CI [0.82–1.20]; male voice: RR = 1.10, 95% CI [0.81–1.50]).

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Analyses of textual comments
The respondents provided their feedback on the disadvantages of the PA in Q17. The

responses that were less than five characters long (N = 181) or that were not written in

English (N = 39) were ignored. Comments were processed before data filtering and were

hence based on all 2,000 responses. 12.4% of the respondents (N = 151) provided negative

feedback on the auditory interfaces in parking assistants. Many people (N = 135) indicated

that PA systems were annoying, for example: “Sound should not be too loud and annoying”

and “I think it could be annoying especially when your focusing”. Thirty-seven respondents

pointed out that the PA used overly loud sounds. Several answers to the question contained

comments that the PA sounds can be distracting (N = 21) and inaccurate (N = 48).

Five respondents indicated that they would prefer feedback in other types of modalities,

for example: “annoying, use something else instead of the constant loud beeping sounds”

and “The sound, a voice message would be better”. Five respondents indicated that the PA

systems cannot be used by deaf people.

The respondents indicated their opinion on the disadvantages of the FCWS in Q23. The

responses that were less than five characters long (N = 276) or that were not written in

English (N = 35) were not included in the analysis. Sixteen respondents indicated that the

auditory feedback used in FCWS was annoying, for example: “This situation might come up

too often so the warning sound may get annoying fast” and “The beeps might feel annoying”.

Next, the respondents were asked to comment on possible advantages and disadvantages

of the FS in Q26. The responses that were less than five characters long (N = 138) or that

were not written in English (N = 46) were not included in the analysis. In total, 1,249

comments were analysed. A collection of mixed responses was received. Overall, more

comments were classified as positive (N = 132) than negative (N = 52) to the FS. However,

the respondents also pointed out concerns about a number of characteristics that they as-

sociated with the system: annoyance to both the driver and to other road users in the traffic

(N = 101), distraction (N = 47), and loudness (N = 28). Fifty-five respondents expressed

their concerns that the system would be impractical; however, most of such concerns

could be associated with the lack of understanding of the concept of a fully automated car.

Certain respondents showed a high level of negativity caused by an apparent lack of un-

derstanding the concept of filtering only specific sounds coming from the outside environ-

ment. Examples are: “You can not hear some bells or signal from other cars”, “Main disad-

vantage: makes driver unaware of any dangers”, “If car noises are filtered out how would you

hear if another car is incoming”, and “I feel that filtering other car noise may be dangerous”.

In Q27 the respondents were asked to indicate their preference for types of interfaces to

be used for takeover requests in HAD. One of the options in that question was “Other”. If

respondents selected this option, they were invited to provide further comments in Q28.

The responses that were less than five characters long (N = 32) or that were not written in

English (N = 1) were ignored. In total, 22 responses were analysed. One respondent indi-

cated that he/she would prefer to be aided by continuous beeps until he/she reclaimed con-

trol. Another respondent stated “steering wheel up or down motion to signal steering wheel

usage needed, accompanied by a specific message”. One respondent mentioned that inter-

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faces used in such scenario need to be adaptive depending on the urgency of the request

“It honestly depends on the situation the car needs me to take over for. Does it affect anyone’s

safety at all? Does it actually /need/ to be done straight away? Is it critically important in any

other way? In those cases I’d obviously like a very noticeable signal however ‘annoying’ it may

be. In other situations however I’d prefer a decent text message or a gentle reminder”.

DISCUSSION
The aim of this study was to obtain opinions on preferred feedback types for takeover

requests in HAD from a large number of people coming from all over the globe.

Additionally, the aim was to measure perceived helpfulness and annoyance of auditory

interfaces for three systems. Specifically, the respondents who participated in the survey

were presented with two existing systems used in modern vehicles (a parking assistant [PA]

& a forward collision warning system [FCWS]) and one futuristic setup (FS) envisioned

for FAD. Respondents were asked whether they would consider using the proposed FS in

future automated vehicles. Our survey helped us to gather opinions from people before

technology is actually available.

Previous research suggests that the modality of aiding systems in automated cars should

be chosen carefully to avoid frustration of people who will be using such vehicles and

to increase safety of automation on public roads. Stanton, Young & McCaulder (1997)

expressed concerns that interfaces currently employed in ACC do not support the

understanding of the behaviour and limitations of the system. A driving simulator study

by Adell et al. (2008) provided a comprehensive analysis of combinations of interfaces for

supporting safe driving. Participants in that study were most positive about the haptic

interface, while the auditory warning signals were not highly appreciated, which may be

explained by the nature of the experiment that exposed the participants to a high urgency

scenario of avoiding rear-end collisions (Adell et al., 2008).

Findings from the aviation field show that the female voice is more difficult to

understand in noisy environments (Nixon et al., 1998a). It has also been argued that

the female voice has the advantage that it stands out more in a predominantly male

environment, such as the military (Noyes, Hellier & Edworthy, 2006). Differences in speech

intelligibility and perceived urgency between male and female voices are generally small

and findings have been mixed (e.g., Arrabito, 2009; Edworthy, Hellier & Rivers, 2003; Nixon

et al., 1998b). However, it has been found that most people normally use a female voice

when using their route navigation device (Large & Burnett, 2013). In the present research,

respondents were asked to select the types of interfaces they are willing to be guided by

during a takeover request. The results of our survey further showed that the female voice is

preferred in both English and non-English speaking countries. Thus, our findings reinforce

the idea that the overall most preferred way to support the transition of control is an

auditory instruction performed with a female voice. Note that determining the language

of respondents based on their IP address cannot guarantee accurate results in all cases.

In future surveys adding a question prompting for the participant’s spoken language may

yield more accurate results.

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It was found that the participants showed a relatively low level of appreciation of

vibratory interfaces, which contrasts with the findings in Adell et al. (2008). This could

be due to the fact that only a small number of systems that feature vibratory feedback are

available in modern vehicles. A relatively large number of people indicated that they would

like to be aided by all four proposed modalities. In addition, a large number of respondents

indicated that the combination of a sound signal, a visual message, and a vibration

signal would be preferable during takeover requests in highly automated driving. This

is a surprising finding as such a combination is not common in current cars. A possible

explanation of this finding could be that the respondents misinterpreted the question and

instead of indicating their preference for multimodal feedback, expressed their preferences

for the types of feedback that can be used separately from each other during takeover

requests in highly automated driving. Another limitation of the present study is that we

did not vary possible parameters of the feedback signals, including the quality, intensity,

timing, and speed of delivery of the takeover requests. Future experimental research could

investigate the effects of such parameters.

The existing systems—the PA and FCWS—received favourable ratings, which may not

be surprising, since these systems have already been tested and are already available on

the market. One limitation in this context is that the participants relied on a narrative

description, complemented with a visual illustration; the survey did not contain actual

examples of auditory cues. Before the initiation of the survey, it was believed that the

proposed FS would be seen as a way to enhance the enjoyment of driving a car through

a scenic place. The results showed that the participants were rather sceptical about such

a concept: the system was perceived as somewhat annoying, with a mean score of 3.21 to

question Q24 on the scale from disagree strongly (1) to agree strongly (5). The proposed FS

was not highly rated, possibly because the concept was perceived as a bad idea, because of a

lack of previous experience with such system, or because people could not envision it due

to the lack of a realistic representation (see also the analysis of the textual comments).

It should also be noted that a large proportion of respondents selected the middle

option Neither agree nor disagree, possibly indicating difficulties with understanding the

concept of the proposed system (for studies into middle category endorsement, see Kulas,

Stachowski & Haynes, 2008; Kulas & Stachowski, 2009; Sturgis, Roberts & Smith, 2012).

Small effects of age on the acceptance of FAD were previously reported by Payre, Cestac

& Delhomme (2014). In the present study, we also observed small age effects regarding the

self-reported annoyance of the three proposed systems: younger participants saw the PA

and FCWS as more annoying than older respondents did. However, young respondents

perceived the FS as less annoying than the older respondents. It is known that younger peo-

ple are more likely to accept new technologies (Lee, 2007; Tacken et al., 2005), and thus may

be more successful at envisioning such abstract concepts as the FS. A somewhat stronger

age effect was observed regarding helpfulness: older respondents found the FCWS more

helpful than the younger participants. It is known that young people feel more confident

behind the wheel (Matthews & Moran, 1986; Lee et al., 2002; Lee, 2007; Clarke, Ward &

Truman, 2005), and therefore may think they need less external help than older drivers.

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CrowdFlower offers a platform that supports full anonymity of participants. This

anonymity may have encouraged respondents to express their thoughts freely, without

the fear of being judged by the organizers of the survey. All but the last free-response items

required people to enter at least one character. A large number of respondents did not

provide meaningful comments. However, a substantial portion of respondents did provide

valuable answers, facilitating the understanding of what people think about not only the

use of auditory interfaces in future highly and fully automated cars, but also about the

concept of automated driving in general. Numerous respondents expressed their concerns

about the qualities of current PA and FCWS systems. Some participants suggested that

they want to be aided by visual and vibratory feedback in addition to auditory feedback. A

number of people indicated the inaccessibility of modern PAs and FCWSs to deaf users.

However, current systems also provide haptic and/or visual cues (BMW, 2013; Volvo,

2013b; Ford, 2014; Honda, 2014), and so people with a hearing impairment could still

benefit from such multimodal feedback. Some respondents were sceptical about the

introduction of highly and fully automated vehicles, which may be related to general

consumer scepticism about new technologies. Respondents expected that most cars would

drive fully automatically by the year 2030 (median value), a result that matches findings in

previously published research (Sommer, 2013; De Winter et al., 2015; Kyriakidis, Happee &

De Winter, 2015).

The total cost of the study performed by means of a crowdsourced online-based survey

was lower than what is offered by companies that conduct similar surveys with help of

classic recruitment methods (De Winter et al., 2015). A group of people filled in the

survey more than once, and we reasoned that their responses ought not to be trusted.

Thus, we applied a strict filter, and removed all respondents who filled out the survey

more than once. We also excluded all people who had one or more missing items. With

appropriate data quality control mechanisms, crowdsourcing is known to be a powerful

research tool (Howe, 2006; Kittur, Chi & Suh, 2008; Mason & Suri, 2012; Crump, McDonnell

& Gureckis, 2013). Nonetheless, as with any self-report questionnaire, the validity of the

results is limited to what people can imagine or retrieve from their memory. Furthermore,

CrowdFlower respondents are not representative of the entire population of stakeholders

of future HAD cars. It is likely that highly automated vehicles will initially be purchased

by wealthy people, while projects on CrowdFlower are often completed by people from

low-income countries (Kyriakidis, Happee & De Winter, 2015).

In conclusion, the present survey study showed that the PA and FCWS were well

appreciated by respondents, whereas the proposed future system (FS) was not rated highly.

A second conclusion is that the female voice is the most preferred takeover request among

the offered options. The scientific community and the automotive industry may be able

to use the information gathered in the present survey for the development of automated

driving systems, in particular future iterations of parking assistants and forward collision

warning systems, as well as for the design of human-machine interfaces for automated

driving.

Bazilinskyy and De Winter (2015), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.13 21/28

https://peerj.com/computer-science/
http://dx.doi.org/10.7717/peerj-cs.13


ACKNOWLEDGEMENTS
We would like to express our special gratitude to Daria Nikulina for designing the

illustrations used in the survey.

ADDITIONAL INFORMATION AND DECLARATIONS

Funding
The research presented in this paper is being conducted in the project HFAuto–Human

Factors of Automated Driving (PITN-GA-2013-605817). The funders had no role in study

design, data collection and analysis, decision to publish, or preparation of the manuscript.

Grant Disclosures
The following grant information was disclosed by the authors:

HFAuto–Human Factors of Automated Driving: PITN-GA-2013-605817.

Competing Interests
The authors declare there are no competing interests.

Author Contributions
• Pavlo Bazilinskyy conceived and designed the experiments, performed the experiments,

analyzed the data, contributed reagents/materials/analysis tools, wrote the paper,

prepared figures and/or tables, performed the computation work, reviewed drafts of

the paper, organised outsourcing of creation of illustrations.

• Joost de Winter conceived and designed the experiments, performed the experiments,

analyzed the data, contributed reagents/materials/analysis tools, wrote the paper,

prepared figures and/or tables, performed the computation work, reviewed drafts of

the paper.

Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body

and any reference numbers):

The research was approved by the Human Research Ethics Committee (HREC) of the

Delft University of Technology.

Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/

10.7717/peerj-cs.13#supplemental-information.

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http://dx.doi.org/10.1109/9.250509
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http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
http://www.volkswagen.co.uk/technology/parking-and-manoeuvring/park-assist
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http://dx.doi.org/10.7717/peerj-cs.13

	Auditory interfaces in automated driving: an international survey
	Introduction
	The development of automated driving systems
	Auditory displays
	Auditory systems in current vehicles: parking assistant and  forward collision warning system
	`Augmented/spatial' sound system for fully automated driving
	The aim of the present survey study

	Methods
	Survey
	Configuration of CrowdFlower
	Analyses
	Ethics statement

	Results
	Number of respondents and respondent satisfaction
	Data filtering
	Analyses at the individual level
	Cross-national differences in opinion for feedback for takeover requests
	Analyses of textual comments

	Discussion
	Acknowledgements
	References