Conditional Privacy: Users’ Perception of Data Privacy in Autonomous

Driving

Teresa Brell, Hannah Biermann, Ralf Philipsen and Martina Zieﬂe

Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen Germany

Keywords:

Mobility Acceptance, Autonomous Driving, Privacy, User Diversity, Data Security.

Abstract:

Connected autonomous driving can be a key for safety enhancement in road trafﬁc and long-term reduction

of driver-induced accidents with personal injury. Though, its acceptance is challenging, because of perceived

restrictions on data security and privacy concerns. Hence, in this study, the focus was on users’ perception

of data privacy in autonomous and connected driving. For this purpose, a two-tiered research approach was

conducted, based on semi-structured interviews (N=7) and an online questionnaire (N=100). Special attention

was given to data storage and processing, data distribution, as well as personal usage purposes as predictors

for the use intention. Results showed that the driver was most likely accepted as data decision maker, whereas

the own car was preferred as data receiver. Besides, evaluation proﬁles indicated user diverse attitudes con-

cerning the willingness to use autonomous driving regularly. These study outcomes contribute to a deeper

understanding of user requirements in the context of mobility acceptance.

1 INTRODUCTION

Due to increasing trafﬁc on the streets today, with the

human as accident reason number one, road safety

poses a global challenge. Regarding Germany, for

example, 3,180 people were killed in road trafﬁc ac-

cidents in 2017, which is about nine people a day

(Destatis, 2018b). Although the total number of casu-

alties dropped to its lowest level in more than 60 years

(Destatis, 2018d), there is still a need for progress

in road safety, in particular, to prevent driver-related

causes of accidents, like insufﬁcient distant, inappro-

priate speed, and improper road use (Destatis, 2018c).

To this, smart mobility systems and technologies,

such as in autonomous driving, have the potential

to reduce trafﬁc accidents and improve road safety

in the long-term (Bishop, 2000; Farmer, 2004; Liu

et al., 2008). Automated vehicle systems, for in-

stance, adaptive cruise control (Vahidi and Eskandar-

ian, 2003) and autonomous brake assist (Breuer et al.,

2007; Kusano and Gabler, 2012), support drivers in

their responsibility under certain driving conditions

to provide a high level of safety in speciﬁc trafﬁc sit-

uations. Concerning the automation and intelligent

connection of transportation systems and road trafﬁc,

Vehicle2X-technologies offer innovative communica-

tion possibilities for data sharing and interaction be-

tween vehicles and their environment (e.g., other ve-

hicles, transport infrastructure, and pedestrians) (Pi-

cone et al., 2015). To increase overall road safety, spe-

cial attention is given to the technical development,

e.g., sensor models (Eichberger et al., 2017), data pro-

cessing (Scherping et al., 2013), and trafﬁc manage-

ment as well as efﬁciency (Wedel et al., 2009) in this

context.

With regard to the users’ perspective on au-

tonomous driving, previous research revealed that

users are aware of the advantages of automated driv-

ing, but also, that perceived barriers, e.g., the fear

of hacker attacks, limit its acceptance (K

onig and

Neumayr, 2017). Hence, research focus was on,

for instance, predictors for trust (Haeuslschmid and

Buelow, 2017), safety perceptions (Schmidt et al.,

2016b), driverless car ethics (Bonnefon et al., 2016),

and, in particular, user diverse privacy and data se-

curity requirements (Schmidt et al., 2016c). To this,

different approaches were applied, such as qualita-

tive and quantitative methods (Schmidt et al., 2016a)

as well as experimental study designs (Waytz et al.,

2014). Key ﬁndings indicate a general distrust when

it comes to transfer data, provided that the willingness

to share personal data with an intelligent transport

system is particularly low. Against the background

of fast-developing technologies, further research is

needed to clarify why and to what extent users ac-

cept or reject data processing and storage as well as

352

Brell, T., Biermann, H., Philipsen, R. and Zieﬂe, M.

Conditional Privacy: Users’ Perception of Data Privacy in Autonomous Driving.

DOI: 10.5220/0007693803520359

In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 352-359

ISBN: 978-989-758-374-2

Table 1: Statements about data storage and data processing.

General evaluation:

Do you agree with the following statements?

Personal data recorded by the car should be identiﬁed with my person.

Each driver should have an ID chip to log & in the vehicle and store personal data.

I want to know where my personal data is & stored and processed.

I want to know how long my data will be stored.

I want to be able to delete the history or & individual entries of the recorded data individually.

I want to know who stores and processes my & personal data.

I want to decide for myself which personal & data I want to release.

I want to know which of my data is stored & processed.

its distribution in autonomous driving.

The aim of this study was to explore users’ per-

ception of data privacy in autonomous and connected

driving.

For this purpose, a two-tiered research approach

was chosen including preceding interviews (N=7) and

a subsequent online questionnaire study (N=100).

The identiﬁcation of inﬂuential acceptance factors for

autonomous driving is an essential step towards a

user-centered technology development. Not only ma-

jor acceptance-relevant factors like perceived bene-

ﬁts and barriers of the technology itself, but rather a

deep dive into an understanding of users’ privacy and

data security appraisals is necessary at this stage of

the mobility shift. In particular, we focused on user

diverse evaluation patterns, for instance, concerning

questions about the data receiver (Who can access the

data?), decision maker (Who can decide on the data

distribution?) and personal usage purposes as contri-

bution to deeper insights into mobility acceptance.

2 METHOD

With a two-tiered research approach, we addressed

the questions of data security and privacy perception

in autonomous driving via a preceding interview

study. Including these ﬁrst ﬁndings about users’

perception of data distribution and handling, a

questionnaire was developed to validate the results.

As can be seen in Figure 1, the usage intention was

assessed to form evaluation groups. All evaluation

differences on privacy in autonomous driving will

be laid out, focusing on data storage and processing,

data distribution and purpose of use.

Data Acquisition and Analysis. The following em-

pirical work was designed as an online questionnaire.

The participants were acquired in the university envi-

ronment or in thematic forums. The quality of the re-

sponses was checked with regard to processing time

and contradictory response behavior. The resulting

data were analyzed by descriptive analysis and, with

respect to the effects of user diversity. The level of

signiﬁcance was set to α = .05.

2.1 The Survey

The survey was divided into three main parts.

Demographics. The demographic data was ad-

dressed in the ﬁrst section. Further, information

about monthly income, possession of driving licence,

and vehicle owner status was questioned. Also, the

willingness to use technology (Neyer et al., 2016)

and the need for control was measured.

Mobility and Digital Proﬁle. In the second section

the participants had to evaluate their mobility and

digital proﬁle. Here, the previous experience with

driver assistance systems (brake assistant, lane

assistant, automatic parking, distance control and

cruise control) as well as usage-patterns and attitudes

toward smartphone applications were questioned.

Concerning the prior experience with driver as-

sistance systems, we focused on advanced speed

regulation systems (adaptive cruise control (ACC))

with automation level 2 (SAE, 2016), as this is the

system with the highest automation level that is

currently available.

Data Security and Privacy Evaluation. The last

section addressed an overall evaluation of data se-

curity and the users’ perception of privacy in an au-

tonomous driving context. Here, several sets of items

(6-point Likert scale, 5=full agreement) questioned

data storage and processing (see Table 1), the distri-

bution of data and different reasons to share data.

Conditional Privacy: Users’ Perception of Data Privacy in Autonomous Driving

353

Participants (N = 100)

Purpose of use

Data

distribution

Data storage

and

processing

Evaluation groups:

usage intention

Privacy in

autonomous

driving

Figure 1: Methodological concept of research model.

2.2 Sample

A total of 134 people participated in the questionnaire

study, of which 34 were excluded from statistical

analyses because of incomplete data sets. Hence, the

sample consisted of N=100 participants, divided into

36 women and 64 men. The participants’ age ranged

from 19 to 68 years, with an average of 34.5 years

(SD=12.5). With 59% holding a university degree

(n=59), the sample was of above average education

(Destatis, 2018a). Besides, 22% had an university en-

trance qualiﬁcation (n=22), followed by 11% with a

vocational baccalaureate diploma (n=11), 7% with a

secondary school diploma (n=7), and 1% with a pri-

mary or secondary school leaving certiﬁcate (n=1).

The majority’s (62%) monthly net household income

was below 3000 Euro (n=62).

Most of the participants (96%) hold a driving

licence (n=96) and owned a motor vehicle (69%,

n=69). In general, the experience with driver assis-

tance systems was rather low (M=2.42, SD=0.70).

In detail, navigation systems were commonly used

(M=4.53, SD=0.80), next to cruise control (CC)

(M=3.67, SD=1.20), whereas parking assistant

(M=2.85, SD=1.11), adaptive cruise control (ACC)

(M=2.49, SD=0.98), and lane keeping assistant

(M=2.40, SD=0.85) were only little used and known,

just as assistants for trafﬁc signs (M=2.20, SD=1.01),

emergency brake (M=2.18, SD=0.82), and lane

change (M=2.16, SD=0.93). Besides, the willingness

to use technology was rather positive (M=3.54,

SD=0.73), whereas the need for control was more

average (M=2.74, SD=0.59).

User Group Allocation. In order to classify the

groups, the willingness to use autonomous vehicles

regularly in the future was surveyed as part of an item

set. According to the participants’ intention to use

a connected autonomous vehicles, two groups were

formed, comprising potential users (n=37), who were

more likely to agree autonomous driving in future

(M=4.62, SD=0.49) and non-users (n=63), who rather

rejected autonomous driving (M=2.16, SD=0.95).

Descriptive proﬁles of speciﬁc user group character-

istics are presented in Table 2.

3 RESULTS

In the following section the obtained results will be

presented in detail. First, an overview of the evalua-

tion based on the complete sample, the results on data

storage and processing, data distribution and purpose

of use will be introduced extensively. Following each

evaluation, the group-speciﬁc differences will be laid

out.

3.1 Data Storage and Processing

Overall, the results show that obtaining information

about data storage and processing is favored. The

highest approval rates could be found in statements

on receiving information about the data type, which

is stored (M=4.33/5 points max., SD= 0.87), the pos-

sibility to decide for oneself, which personal data is

released (M=4.32, SD=0.88) and receiving informa-

tion about who stores and processes the data (M=4.31,

SD=0.91). In addition, it was agreed on the possi-

bility to delete the history or individual entries of the

recorded data (M=4.25, SD=1.07), receiving informa-

tion about how long (M=4.04, SD=0.99) and where

data will be stored and processed (M=3.68, SD=1.23).

The strongest disagreement was found regarding a

possible ID chip, which is used to log on to the vehi-

cle containing personal data (M=2.07,SD=1.55) and

the identiﬁcation of a person via the personal data

recorded by the vehicle (M=1.72, SD=1.36).

User Groups. Addressing the data storage and pro-

cessing, group differences showed up. To be pre-

cise, the non-user group (M= 3.87, SD=1.13) evalu-

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

354

Table 2: Characteristics of user groups.

User Groups User (n=37) Non-user (n=63)

Age M=29.51, SD=10.17 M=37.49, SD=12.92

Male 83.8% (n=31) 52.4% (n=33)

Female 16.2% (n=6) 47.6% (n=30)

Net household income <3000 Euro 70.2% (n=26) 57.1% (n=36)

Driving licence holders 64.9% (n=24) 71.4% (n=45)

Willingness to use technology in general M=3.95, SD=0.53 M=3.30, SD=0.73

Perceived need for control M=2.63, SD=0.44 M=2.80, SD=0.66

Prior experience with driver assistance systems M=2.65, SD=0.80 M=2.28, SD=0.60

ated the possibility to know where the data is stored

signiﬁcantly higher (F(1,98)=4,34), p<.05) than the

user group (M=3.35, SD=1.34). Also, the information

about who stores the data showed a similar outcome:

the non-user group (M=4.46,SD=0.67) urged that

information signiﬁcantly stronger (F(1,98)=4,86),

p<.05) than the user group (M=4.05, SD=1.18).

3.2 Data Distribution: Data Receiver

and Decision Maker

With regard to data distribution, two thematic cate-

gories were measured: First, the question of who is

entitled to have the recorded data was addressed (see

Figure 2) and second, who should decide who may

use the data by the connected autonomous vehicle.

3.2.1 Data Receiver

The strongest consent of all the possible receivers

were found for the own car (M=3.52, SD=1.43) fol-

lowed by the police and emergency services (M=3.43,

SD=1.38), and by an addition of the infrastructure

(M=3.02, SD=1.50). In contrast, the vehicle manu-

facturer (M=1.17, SD=1.17) and the insurance com-

pany (M=1.50, SD=1.37) matched the least average

agreement rates.

User Groups. Concerning the user groups, almost all

statements according the data distribution were per-

ceived signiﬁcantly different (see Figure 2). On av-

erage, potential users show a stronger agreement to

most of the given possible data receivers.

3.2.2 Decision Maker

Addressing the responsibility to decide who uses the

data, the driver reached the highest average approval

with 4.33 out of 5 points (SD=1.03) followed by the

vehicle owner (M=3.46, SD=1.59). The vehicle man-

ufacturer however has received the strongest rejec-

tion as a decision-maker with an average agreement

of 0.79 (SD=0.97).

User Groups. Again, the groups revealed to signiﬁ-

cantly impact the evaluations. While users had a small

tendency towards neutral agreement about the manu-

facturer as decision maker (M=1.05, SD=1.05), non-

users tend to disagree strongly (M=0.63, SD=0.89)

(F(1,98)=4,53, p<.05). Further, a similar difference

can be found in the agreement of the legislator as de-

cision maker. Here, the willingness to use the tech-

nology had a signiﬁcant effect (F(1,98)=6,83, p=.01).

On average, the users agreed more on the legislator

as decision-maker (M=2.70, SD=1.76) than non-users

(M=1.84, SD=1.48).

3.3 Purpose of Use

The sample has agreed on average to all possible rea-

sons to share data (see Figure 3). Here, safety for

oneself and other trafﬁc participants was of highest

interest: to report own accidents to police and rescue

services (M=4.07, SD=1.02) and for accident clari-

ﬁcation (M=3.88, SD=1.03). Thematically, comfort

issues followed the safety-related reasons to use data:

the data may be used for personal beneﬁts (e.g. trafﬁc

information or reservation of parking spots) and for

the improvement of street and trafﬁc ﬂow. Hence, to

enable the police to immobilize the vehicle in case of

emergency received the least approval with an aver-

age of 3.06 (SD=1.61).

User Groups. Here, the intention to use the con-

nected and autonomous technology showed an impact

on the evaluations of the purposes of use. All re-

sults were signiﬁcantly different, except the reasons

inspection of vehicles and to immobilize the vehicle.

In all cases the group with potential users had higher

agreement rates.

Conditional Privacy: Users’ Perception of Data Privacy in Autonomous Driving

355

1,05

2,70

3,46

4,27

1,70

1,51

1,78

3,51

3,73

3,97

4,05

0,63

1,84

3,46

4,37

0,86

1,49

1,43

2,17

2,60

3,11

3,21

0,00 1,00 2,00 3,00 4,00 5,00

Vehicle Manufacturer *

Legisl ator *

Vehicle owner

Dri ver

Vehicle manufacturer *

Insurance company

Service provider of rental vehicles

The own car and cars nearby *

The own car and the infrastructure *

Poli ce and em ergenc y services *

The own car *

Data distribution: Decision maker and data receiver

Non-user

User

Decision maker

Data receiver

Figure 2: Overall results of data distribution: who decides who may receive the data (decision maker) and who may access

the data (data receiver). 0=full disagreement, 5=full agreement. * indicates signiﬁcant group differences.

4 DISCUSSION

The present study aimed at an understanding of users’

perceptions towards data privacy in autonomous and

connected driving. Methodologically, we focused in

a ﬁrst investigation on understanding general insights

of possible technology user about autonomous driv-

ing, in particular their fears and doubts according data

security (interview study). In a second step, we as-

sessed the desire for information on data processing,

storage, and distribution in a western European sam-

ple (survey study). Additionally, we analyzed evalua-

tions of user groups with respect to their willingness

to use the technology in future. The discussion will

mirror the result structure and will furthermore ques-

tion the empirical procedure as well as needed future

research topics.

Concerning data storage and processing, the

most valued information was which type of data is

shared (e.g. own position, speed) – for both user

groups. This result goes in line with the research ﬁnd-

ings of Schmidt et al. (2016a). Further, it was shown

that there are different types of data, which are shared

more willingly than others (the more personal data

the data is, the less willing users are to share them).

A closer look into the user groups showed, that non-

users would like to know the data type with a signiﬁ-

cantly higher consent.

The wish to maintain these information shows also

a possible distrust to the system, not necessarily a dis-

like or distrust for the technological idea. Hence, only

less than half of the sample agreed to use the technol-

ogy in future. Given the fact that the technology was

only described and could not be tested, the number of

potential users was rather high.

A closer look onto the groups shows, that the user

group in this study is mainly male and rather young,

given the fact that the ”early adopters” of mobility

technology (e.g. electromobility) are about 51 years

of age (Trommer et al., 2015). Whereas the non-user

group is almost symmetrical in gender distribution.

In fact, this is particularly noteworthy since the en-

tire sample had a high male proportion and therefore

women may be more likely to be theoretical non-users

(at least in this study). The group of users has a

higher willingness to use technology in general and

they have also more experience with driver assistance

systems. Interestingly, their average value for per-

ceived need for control was lower compared to the

non-user group.

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

356

3,43

3,41

4,32

4,27

4,41

4,59

2,84

3,24

3,25

3,37

3,57

3,76

0 1 2 3 4 5

By the police to immobilize the vehicle in the case of emergency.

For the inspection of vehicles used for business purposes.

To improve roads and traffic f low. *

My personal bene fit (e.g. traffic information). *

For accident clarification. *

To report own accidents or emergencies to police and res cue services . *

Purpose of data usage

Non-user

User

Figure 3: Overview of the agreement to the use of the recorded data. 0=full disagreement, 5=full agreement. * indicates

signiﬁcant group differences.

The group of users is almost neutral to need for

control, which might mirror the lower evaluation rates

on information about data storage and processing.

Now concerning the data distribution, the group

of users has signiﬁcantly higher rates on all possible

data receivers, almost showing a disinterest in who

receives the data at all. And further, they also have

rather high average agreement rates on who decides

about the data distribution.

Interestingly, no group is overall agreeing to the vehi-

cle manufacturer as data receiver or distributor. This

is an astonishing result, given the fact that the manu-

facturer is right now the main receiver of all vehicle-

related data. Here, a potential distrust to authorities

can be identiﬁed, given the recent negative events and

press about the liability on car manufacturers, e.g.

VW Diesel scandal (Jung and “Alison” Park, 2017;

Sharpe, 2017). However, one has to consider the

western European sample. A comparison study in

other European regions would deﬁnitely reveal pos-

sible cultural differences regarding the trust given to

authorities in a mobility context. Trust also seems

to be a main inﬂuencing factor in data privacy of au-

tonomous vehicles. Hence, the survey did not ques-

tion trust as user factor, the result that the own car has

the highest approval values as data receiver and the

driver him- or herself as decision maker for the data

distribution shows a clear and needed focus on that

topic in future work.

An average agreement on all purposes of use was

identiﬁed. Safety is, however, still the most promi-

nent purpose. This result validates several studies on

beneﬁts of autonomous driving (Brell et al., 2018).

The group of users agreed signiﬁcantly higher on all

given purposes. Here, possibly a better understand-

ing of the technology’s beneﬁts may be uncovered.

This could be connected to the groups overall higher

use of technology altogether. Also, personal beneﬁts

like trafﬁc information or the improvement of road

and trafﬁc ﬂow had very high acceptance rates – here,

the general understanding of potential possibilities to

enhance and develop trafﬁc can be identiﬁed. How-

ever, further purposes of use need to be discussed.

The general wish to improve the current situation does

not automatically improve the acceptance or usage of

a technology or infrastructure change.

5 CONCLUSION AND

LIMITATIONS

Autonomous driving and connected vehicles in Ger-

many are perceived as useful and although without

practical experience, the western European sample

showed interest in the topic and communicated a will-

ingness to use the technology in future. Although data

protection is a very critical issue and needs to be fur-

ther explored in order to ensure up-front acceptance

of the technology and a possibility to participate in its

development, a group of users could be identiﬁed who

agree to use the technology without practical experi-

ence. Therefore, data privacy is a highly important

topic as well as the distrust or trust of data receivers,

which showed that these topics are not handled well

up until today. Clearly, the beneﬁts must be commu-

nicated more transparent in order to pick up the more

skeptical people. We found a high approval on po-

lice and emergency services as data receiver, but still

not the highest agreement on using it actively. To en-

able the police to immobilize the vehicle in case of an

emergency was an agreed upon reason, but still over-

all not the highest approval rates. The ﬁndings call

Conditional Privacy: Users’ Perception of Data Privacy in Autonomous Driving

357

for action to help transform the conditional perceived

privacy into a fully perceived one.

ACKNOWLEDGEMENTS

This work has been funded by the Federal Ministry of

Transport and Digital Infrastructure (BMVI) within

the funding guideline ”Automated and Connected

Driving” under the grant number 16AVF2134B.

Many thanks go to Adam Michalik for research assis-

tance and the participants for sharing their opinions

on this matter. Also we like to thank the reviewer for

their feedback on this work.

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