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Technology readiness affects the acceptance of and intention to use an automated
driving system after repeated experience
System evaluation & technology readiness
Barbara Metz
WIVW GmbH, metz@wivw.de
Johanna Wörle
WIVW GmbH, woerle@wivw.de
User acceptance is the key to success of automated driving. The user’s technology readiness is one important factor in their
behavioral intention to use automated driving. User acceptance changes with actual experience of the technology. The effect of
user’s technology readiness, automation level and experience with the technology on the acceptance of automated driving are
assessed in a driving simulator study. N=60 drivers tested an L3 or L4 motorway automated driving system during six drives
taking place at six different days. They evaluated the tested systems for a variety of relevant aspects. The results show an impact
of technology readiness on higher level concepts like usefulness, satisfaction and behavioural intention but not on direct
evaluation of the functionality or on drivers’ immediate experience of driving with the system. The meaning of the results for
future research is discussed.
CCS CONCEPTS • Empirical studies in HCI • User studies • User characteristics
Additional Keywords and Phrases: automated driving, technology readiness, driving simulator, user acceptance
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1 INTRODUCTION
Automated Driving (AD) technology promises to make travelling more efficient, more convenient and safer; and to
make mobility more accessible [1]. To realize this vision, AD technology not only has to prove to work safely and
reliably, it also has to be accepted by potential users. In other words, not only technology needs to be ready for use
but also the user needs to be ready for usage. Technology readiness describes how willing people are to use new
technologies. It is defined as “people’s propensity to embrace and use new technologies for accomplishing goals in
home life and work” (p. 308) and is described as a person’s predisposition to use new technologies [2]. Technology
readiness is positively related to the actual usage of a technology. It is influenced by the age, education and
experience of the user, the type of technology and by the actual usage of a particular technology [3].
2
Research in the field of behavioural adaptation shows, that evaluation and usage of technical systems changes
with growing experience with a technology. With repeated usage, users change their perception, behaviour and
attitudes towards the technology [4]. Changes in all of these categories are interrelated. Especially for AD
technology, users report higher trust and a higher intention to use AD when gaining more experience with it [5, 6].
It is not known how technology readiness affects the development of acceptance of AD and of its usage. The aim of
the presented analysis is to investigate the interplay between technology readiness and growing experience with
AD and the impact of both factors on acceptance as well as usage of ADS.
The presented results come from a study conducted as part of the European L3Pilot project. The main focus of
the project is to test higher level AD functions on open roads in real traffic. The aim is to evaluate “technical aspects,
user acceptance, driving and travel behaviour, as well as the impact on traffic and safety” [7]. User acceptance is a
key for market success of AD technology. Since higher level AD functions are not available yet, peoples’ opinions
and attitudes towards AD technology do not reflect personal experiences, but rather basic attitudes and opinions
formed by media and social influences. A large-scale Europe-wide survey was conducted as part of the L3Pilot
project to investigate the public opinion about AD, specifically about conditionally AD (level 3 according to the
Society of Automobile Engineers taxonomy of driving automation [8]) and high automation (level 4 according to
[8]). N = 9,118 car drivers across Europe were questioned in an online survey. A basic description of a conditionally
AD system was provided to participants clarifying that such a system would operate under limited conditions and
that the driver would be enabled to engage in other activities but remain available to resume control in case the
system reached a limit. About half of the sample stated that such a system would be useful and that they would use
it during everyday trips. 42% of respondents could imagine using the time in which the system is active to engage
in other activities, like talking to fellow travellers, surf the internet or watch videos. Only 28% were willing to buy
such a system. The respondents of the survey had not experienced AD before.
From research in the field of behavioural adaptation, it can be expected, that peoples’ opinions and attitudes
change when they use a certain technology. Drivers’ initial attitudes and behavioural expectations – like assessed
in surveys - might change when they actually get in contact with the technology [4].
The relation between evaluation of technical systems, the intention to use a technique and the actual usage of a
technology has been widely studied and described in various models. For instance, UTAUT [9] describes the relation
between performance expectancy (which is also referred to as usefulness when talking about AD [10]), effort
expectancy and other e.g. social factors on behavioural intention, which again predict usage. In the presented paper,
the impact of technology readiness and the effect of growing experience with an AD on system evaluation,
behavioural intention and system usage is studied; the relation between the different components is not assessed.
We conducted a study on driver behaviour and acceptance of an automated driving system (ADS) and potential
changes with repeated usage in a driving simulator (see, e.g., [11]) also as part of the L3Pilot project. Regular drivers
were invited for six test sessions where they were provided with an ADS during simulated motorway drives. Drivers
were free to bring to the sessions whatever they would like to engage in during driving with ADS active. Before and
after each drive, they filled in questionnaires asking about their experience during the drive and attitudes towards
the ADS. In contrast to the findings from the online survey [12] where only half of the sample stated that they would
use an ADS, all participants of the driving simulator study used the system resulting in an overall activation of 90%
of the time it was available [11]. The proportion of driving time drivers engaged in other activities increased after
the first driving session and the attention to the road decreased. With increasing usage, trust, perceived safety and
willingness to use the ADS increased. The discrepancy between drivers’ initial attitudes towards ADS questioned in
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the survey and drivers’ attitudes after using a (simulated) ADS raises the question of how initial attitudes may affect
drivers’ acceptance of and behaviour toward ADS. The general attitudes towards technology or the “technology
readiness” is hypothesized to have an impact on drivers’ acceptance and usage of AD.
The questionnaire used in the experiment allows to study the impact of technology readiness on a more objective
evaluation of the tested functionality, with items measuring the immediate user experience while driving with the
ADS as well as on more abstract concepts like usefulness and satisfaction. According to [8] usefulness refers to the
subjective probability that using a technology will improve the way a user completes a given task. Satisfaction has
been defined as an affective state that is an emotional reaction to a technology experience [8]. Furthermore, the
relation to behavioural intention like willingness to buy and actual system usage during the experimental drives is
investigated.
2 METHODS
For the study, a high fidelity moving-base driving simulator was used that has a 240° surround view displaying
realistic dynamics. The driver was seated in a mockup that consists of a chassis of a production type BMW 520i. The
motion system used six degrees of freedom and could display a linear acceleration up to 5 m/s². All vehicle dynamics
and noises were displayed realistically. The simulator runs with the simulation software SILAB 6.0® (WIVW GmbH,
Veitshöchheim). This software provides a realistic simulation of vehicle characteristics including L3 / L4 AD
systems, of the driving environment (road layout, infrastructure, weather conditions) and of the surrounding traffic.
N = 60 drivers participated in the study and were randomly assigned to one of two conditions: the L3 condition
(N = 30 drivers) and the L4 condition (N = 30 drivers). For an overview of the sample description, see Table 1.
Table 1: Study sample: Means and standard deviations (in brackets) for the description of the two sub-samples
L3 condition
L4 condition
N
30
30
Age
38.4 (sd=12.0)
40.1 (sd=11.9)
Gender
13 female, 17 male
16 female, 14 male
Technology readiness
Among last: N=7
Middle: N=17
Among first: N=6
Among last: N=5
Middle: N=17
Among first: N=8
All drivers experienced six drives on a motorway in six different driving sessions which took place on different
days. During the drives, they were free to use a motorway chauffeur of L3 resp. L4. Drives in experimental session
1, 2, 4 and 6 had a duration of 30 min and drives in session 3 and 5 had a duration of 90 min. In all drives, standard
situations of motorway driving were simulated, including sections with dense traffic, sections with little traffic,
changing speed limits, construction sites, highway intersections, highway entries and exits. The aim was to allow
participants to experience the tested ADS under everyday conditions; unusual or critical situations were not part
of the experimental setup. During the two longer sessions, the drives were specifically designed to promote fatigue
and consisted of monotonous highway sections with low traffic density. Table 2 gives an overview over the
experimental sessions. Both groups (L3 and L4) were instructed in all drives that they were free to use the ADS as
they would like to use it in real life.
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Table 2: Overview over experimental sessions.
Session
Content
Session 1 – introductory session
90 min
Information on experiment & planned schedule
Informed Consent
Handing out of Pilot site questionnaire part 1
Introductory drive (10 min)
30 min drive
Post drive questionnaire (full version)
Session 2
45 min
Short pre-drive questionnaire
30 min drive
Post drive questionnaire (short version)
Session 3
2 hours 30 min
Short pre-drive questionnaire
90 min drive
Post drive questionnaire (short version)
Session 4
45 min
Short pre-drive questionnaire
30 min drive
Post drive questionnaire (short version)
Session 5
2 hours 30 min
Short pre-drive questionnaire
90 min drive
Post drive questionnaire (short version)
Session 6
90 min
Short pre-drive questionnaire
30 min drive
Post drive questionnaire (full version)
Both ADS implementations were able to execute longitudinal and lateral guidance as well as automated lane
changes in case the ADS detected a slower lead vehicle. The ADS had a speed range of 0-130 km/h and issued a take-
over request (TOR) in case system limits were reached. Differences between the L3 and L4 ADS were:
• System limits: The L3-ADS issued a TOR before motorway exits and entrances, construction sites,
missing lane markings and heavy rain. The L4-ADS had the same system limits except that it was able
to operate when lane markings were missing.
• Take-over times: The L3-ADS had a take-over time of 15 sec while the L4-ADS had a take-over time of
45 sec.
• Instruction: Participants in the L3 condition were instructed with the actual wording of §1b of the
German Road Transport Law [13] that regulates the role of the driver while using an ADS. The
paragraph obliges the driver to be receptive to TORs at any time during the drive and to respond
appropriately in case the ADS issues a TOR. In contrast, in the L4 condition, the instruction said that
the vehicle was able to solve all situations and that they were not responsible for the drive.
5
Figure 1: The high-fidelity driving simulator.
In the experiment, questionnaires developed in the L3Pilot were used [14]. They were developed to specifically
address the research questions of the project, which cover a wide range of user related topics. To be able to address
all research questions while keeping the questionnaire at a reasonable length, it was not possible to integrate more
elaborate standardized tools. The L3 Pilot questionnaire is planned to be used within all studies of L3Pilot to allow
a pooling of the data later in the project.
We administered the L3Pilot pre-questionnaire before the 1st drive and the full L3 Pilot post-drive questionnaire
after the 1st and 6th drive. The L3Pilot questionnaire consists of standardized scales (like, e.g., the Acceptance scale
of [15]) and customized items that were specifically designed for the purposes of L3Pilot (for a full description of
the L3Pilot questionnaire see [14], Annex 3). The specifically developed items all consist of a statement with which
the drivers can agree or disagree on a 5-point-Likert-scale.
Before and after every other drive, participants filled in a short version of the questionnaire. For this paper, we
only evaluate the pre-questionnaire and post-questionnaire of the 1st session and the post-questionnaire of the 6th
session. After the 6th drive, participants had the possibility to experience the systems during a total of about 5
hours. Since drivers were free to use the ADS as they liked, actual usage might deviate from the total time of system
availability.
Technology readiness was assessed via one item answered once during the 1st session:
• ‘When it comes to trying a new technology product, I am generally
among the last – in the middle – among the first
This item is used to split the sample into three groups based on their stated technology readiness. As can be seen
in table 1, the distribution of the three levels of technology readiness is similar for the two experimental groups.
From the questionnaire, the following items will be investigated:
• Items where the drivers evaluate the functionality of the ADS (items ‘The system acted appropriately
in all situations.’ and ‘The system worked as it should work.’ answered on 5-point-scale).
• Items where the drivers judge their immediate user experience while driving with the ADS:
experienced comfort (‘Driving with the system active was comfortable.’) and experienced stress
(‘Driving with the system was stressful.’) both answered on 5-point-scale.
• Items where drivers evaluate the ADS based on higher level, more abstract concepts: for this the 9-item
Acceptance scale [15] is used. For the analysis, the nine original scales are summarized to the two sub-
scales usefulness and satisfaction. Furthermore, drivers’ trust in the ADS is investigated (‘I trust the
system to drive.’, answered on 5-point-scale)
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• Items measuring behavioural intentions, i.e., willingness to use (‘I would use the system during my
everyday trips.’) and willingness to buy (‘I would buy the system.’) both answered on 5-point-scale.
For all questionnaire items and the two subscales the answers are linearly transformed to values ranging
between -2 and 2, with positive values corresponding to a positive evaluation of the ADS. This will allow better
comparison between the different scales presented in the results section.
Besides questionnaire data, data from the driving simulation (e.g. status of the ADS) were logged continuously
during the drives. To measure actual usage of the ADS during the experimental drives, two objective indicators are
derived from those data logs:
• proportion of time driving with activated ADS
• proportion of time spent on non-driving related activities that involve the hands. Engagement in non-
driving related activities was coded based on the video.
For all questionnaire items and for the two indicators assessing actual usage, the impact of the within-factor
time (1st vs. 6th drive), between-factor ADS-level (L3 vs. L4) and between-factor technology readiness (among last,
middle, among first) are assessed with three-way multi-factorial ANOVAs.
3 RESULTS
For items assessing a subjective evaluation of the system’s functionality there are significant main effects of ADS-
level (“The system acted appropriately in all situations”: F(1,53) = 10.95, p < 0.01 & “The system worked as it should
work.”: F(1,53) = 10.36, p < 0.01) but neither effects of time nor of technology readiness. The L3-ADS is evaluated
as less reliable and as working less properly (see figure 2).
Figure 2: Effect of time, technology readiness and ADS level on items evaluating the functionality of the ADS.
For items describing the immediate user experience while driving with the ADS, the factor time becomes
relevant: with increasing experience with the ADS, the experienced stress decreases significantly (F(1,53) = 4.23,
p < 0.05, there is also a significant main effect of ADS-level (F(1,53) = 6.66, p < 0.05) and the experienced comfort
increases (F(2,53) = 6.8, p < 0.05). For trust, there are significant effects of time (F(1,53) = 10.3, p < 0.01) and of
technology readiness (F(2,53) = 6.1, p < 0.01). With repeated usage, trust increases and drivers with higher
technology readiness state higher trust in the ADS (see figure 3).
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Figure 3: Effect of time, technology readiness and ADS level on experienced comfort and trust.
For the subscale usefulness of the Acceptance scale [15], there is a significant main effect of ADS level
(F(1,53) = 6.64, p < 0.05), a nearly significant main effect of technology readiness (F(2,53) = 3.03, p = 0.057) and a
significant interaction between time and technology readiness (F(2,53) = 3.32, p < 0.05), see figure 4. The L4-ADS
is evaluated more positively than the L3-ADS. For participants with medium and high technology readiness, the
usefulness increases with repeated usage, for participants with low readiness it decreases. For the subscale
satisfaction, there is a significant main effect of ADS level (F(1,53) = 12.98, p < 0.001), a significant main effect of
technology readiness (F(2.53) = 5.17, p < 0.01) and a nearly significant interaction between time and technology
readiness (F(2,53) = 2.86, p = 0.066). The L4-ADS is experienced as more satisfying and the experienced satisfaction
is higher the higher the technology readiness is.
Figure 4: Effect of time, technology readiness and ADS level on the sub-scales usefulness and satisfaction.
For willingness to buy, there are significant effects of ADS-level (F(1,53) = 7.90, p < 0.01) and technology
readiness (F(2,53) = 5.55, p < 0.01). Drivers are more willing to buy the more mature L4-ADS and - independent of
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the experienced ADS-level - drivers with high technology readiness are more willing to buy an ADS (figure 5, right
graph). For willingness to use, there are a significant main effect of technology readiness (F(2,53) = 8.26, p < 0.001),
a significant main effect of time (F(1,53) = 6.88, p < 0.05) as well as a significant 2-way interaction between ADS-
level and technology readiness (F(2,53) = 7.17, p < 0.01) and a significant 3-way interaction (F(2,53) = 3.38,
p < 0.05). As can be seen in figure 5 left hand side, there is an effect of ADS-level and of time only for drivers with
medium technology readiness. For that subgroup, willingness to use is higher for L4-ADS and it increases with
increasing experience with the ADS. Drivers with high technology readiness are more willing to use the system
independent of ADS-level and this willingness increases with increasing experience with the system. Drivers with
low technology readiness are less willing to use the system. For them, there is neither a significant effect of time nor
of ADS-level.
Figure 5: Effect of time, technology readiness and ADS-level on willingness to use and willingness to buy.
For the proportion of time driving with activated function during the experimental drives, there are no
significant effects. This is due to a high usage of the ADS already during the 1st session (on average 90% of time
during which the ADS was available) leaving little room for change. For the proportion of time spent on manual side
tasks, there are a significant effect of time (F(1,55) = 16.2, p < 0.01) and a significant interaction of ADS-level and
technology readiness (F(2,55) = 3.3, p < 0.05). Unexpectedly and not in line with all other results, drivers with low
technology readiness spent more time on side tasks when using the L3-ADS. Independent of ADS-level and
technology readiness, time spent on side tasks increases over time.
9
Figure 6: Effect of time, technology readiness and ADS-level on measured system usage and time spent on side tasks while
driving with the ADS active.
4 SUMMARY & CONCLUSION
The technology readiness of a person affects their actual usage of a technology [3]. The aim of the presented analysis
was to assess in more detail the relation of technology readiness and the attitudes towards AD technology after
repeated experience. For the analyses, participants were divided into three groups based on their self-reported
technology readiness. Besides the effect of technology readiness, the effects of system level and time of repeatedly
using an ADS were investigated.
Results show that the factors influencing the evaluation of the ADS seem to systematically vary with the assessed
concepts (see also table 3). Questionnaire items that relate closely to the tested ADS, i.e., that directly evaluate the
functionality, are only influenced by the ADS-level, that is the maturity of the function. These evaluations do neither
change with growing experience with the ADS nor are they dependent on technology readiness.
When the immediate user experience of driving with the ADS is assessed, the factor time plays an important role.
With growing experience with the ADS, drivers get used to it and have a more positive experience during the drive.
At the same time, immediate user experience (i.e. stress or comfort) during the drives is independent of the ADS
level, at least for the tested ADS implementations.
Technology readiness comes into play as the ADS is evaluated on more complex concepts like trust, satisfaction
and usefulness. Here, a higher technology readiness is related to a more positive evaluation of the ADS. For the
usefulness scale, there is an unexpected interaction between technology readiness and time using the ADS: with
increasing experience of the ADS, drivers with low technology readiness evaluate the usefulness of the ADS more
negatively than after the 1st test drive. In all other cases, growing experience with the ADS was always linked to a
more favourable evaluation of the function.
Items measuring behavioural intention are again not influenced by growing experience with the ADS.
Willingness to use and willingness to buy depend on the maturity of the tested ADS and on technology readiness.
The strongest impact of the factor ADS-level can be seen for the medium level of technology readiness. For that
group, there is a substantial increase of willingness to use with increasing experience with the ADS and there is also
a pronounced difference between ADS levels.
10
Looking at the effects of the investigated factors, actual handling of the ADS during the experimental drives
(assessed via the time spent on side tasks) seems to be most closely related to items assessing the drivers’
experience during the experimental trips. However, there is this unexpected interaction between ADS level and
technology readiness: drivers with low technology readiness spent more time on side tasks with a L3-ADS then the
same group of drivers with an L4-ADS. None of the other results is in line with this, on all subjective items L3-ADS
was rated less positively than the L4-ADS. Actual system usage i.e., the driving time with the system activated is
independent of all investigated factors. This is probably due to the extremely high usage already during the 1st
drive which leaves little room for improvement (see also [5]). Overall, system usage during the experiment seems
to be independent of technology readiness, instead it is more strongly influenced by time. With growing experience
drivers engage more frequently in side tasks.
Table 3: Summary of results. For significant interactions, both factors are marked with an X.
Indicator group
Indicator
Influencing factor
ADS level
time
Tech. read.
Evaluation of functionality
Acted appropriately
X
Worked as it should
X
Evaluation of experience
Experienced stress
X
X
Experienced comfort
X
Higher evaluation concepts
Trust
X
X
Usefulness
X
X
X
Satisfying
X
X
Behavioural intention
Willingness to use
X
X
Willingness to buy
X
X
Measured system usage
% system usage
% side tasks
X
X
X
Taking all presented results into account, it seems that technology readiness becomes a relevant factor as soon
as the evaluation of an ADS goes beyond immediate experience of the system. Although the direct system experience
expressed in an evaluation of the functionality and of drivers’ immediate experience while using the ADS is similar
for all participants independent of their technology readiness, there is a significant difference in higher level
evaluations like trust, usefulness or satisfaction between drivers with low and high technological readiness.
Furthermore, the impact of other factors like ADS level or growing experience with the ADS seems to be most
pronounced for drivers with medium technology readiness. Drivers that state high technology readiness are
positive about the ADS right from the beginning and that evaluation remains stable with growing experience with
the ADS. On the other side, drivers with low technology readiness are not too much thrilled by the ADS and this
stays independent of the functionality of the ADS or the experience while using the function.
Technology readiness seems to moderate how drivers move from their experience with a system to higher level
evaluations like usefulness, satisfaction or resulting behavioural intentions. The immediate impression of the
system is independent from technology readiness.
Actual system usage during the experimental drives seems to be more closely linked to the immediate
experience of the ADS than to stated behavioural intentions. This result might be relevant for the interpretation of
data collected in surveys (e.g. [6]). Here, intention to use is frequently assessed based on questionnaire items from
participants that know ADS only from the descriptions provided in the survey. The aim is to predict future ADS
usage as soon as such systems will be available on the market. Based on our results, it seems that individual
technology readiness is an important factor to be taken into account e.g., for planning a successful market
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introduction and promotion of new systems. To reach a high acceptance and usage amongst the whole population,
it is necessary to introduce the new technology also to those with medium and low technological readiness.
Especially the large group of people stating medium technology readiness seem to benefit from actually
experiencing the system. For that subgroup, system evaluation became more positively with repeated usage of the
ADS. Furthermore, technology readiness might be a relevant factor to be included in research on usage and
acceptance of new technologies. Results obtained with a sample for instance of users with high technology readiness
might lead to misleading conclusion on the acceptability of a technology and the impact of influencing factors.
Based on our result, the link between stated intention to use and actual usage of an ADS that is available to the
driver might not be straightforward (see also [16]). It has to be kept in mind that it was probably easier for
participants to actually use the ADS in the experiment than it would be for everyday trips. The experiment was
designed in a way that the ADS was available during most of the time. In reality, usage would be linked to many pre-
conditions like having a vehicle available that is fitted with the ADS, driving on the motorway etc. that are dependent
on choices made by the driver. For the prediction of still to come usage of ADS during everyday trips (which is
needed for instance to judge the potential benefit of an ADS) many more factors will come into play that will
moderate the relation between stated intention to use and real usage.
ACKNOWLEDGMENTS
The research leading to these results has received funding from the European Commission Horizon 2020 program
under the project L3Pilot, grant agreement number 723051. Responsibility for the information and views set out in
this publication lies entirely with the authors. The authors would like to thank all partners within L3Pilot for their
cooperation and valuable contribution. Funding: The study is part of the research project L3Pilot
(https://www.l3pilot.eu/), which receives funding from the European Union's Horizon 2020 research and
innovation programme under grant agreement No 723051. Conflicts of Interest: The authors declare no conflict of
interest.
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