Fullpaper Format

12th ITS European Congress, Strasbourg, France, 19-22 June 2017

Paper ID # (TS27)

User Acceptance of Driverless Shuttles Running in an Open and Mixed Traffic Environment

Sina Nordhoff* a,b, Bart van Arem a, Natasha Merat c, Ruth Madiganc, a, Lisa Ruhrort b, Andreas Knieb, and Riender Happeea b

a TU Delft, 2600 GA Delft, The Netherlands Innovation Centre for Mobility and Societal Change – InnoZ, Euref-Campus 16, 10829 Berlin, Germany c University of Leeds, United Kingdom

Abstract User acceptance is a vital requirement for the success of automated vehicles that has been extensively addressed in current research in various acceptance studies. These studies have contributed to our understanding of potential acceptance factors of automated vehicles. However, the main focus of these studies has been on conventional vehicles with steer and pedals that were tested in artificial and simulated environments with a focus on in-vehicle technology. Furthermore, these studies list and identify a number of potential acceptance factors, but without making a systematic use of validated models to systematically present previous research. The current research addresses these gaps in research by investigating user acceptance of driverless shuttles in public transport in an open and mixed traffic environment on real semi-public roads in Berlin-Schöneberg. Results indicate that the acceptance and use of these driverless vehicles in public transport is predominantly influenced by their perceived usefulness, ease of use and social influence. Keywords: automated public transport, user acceptance


1. Introduction Automated driving has received a lot of attention from media and different stakeholders who are involved with the development of automated driving technology. Public exposure to various forms of automated vehicles has slowly started to increase in recent years, with several pilot projects and test runs of automated vehicles occurring across Europe. This makes it possible to examine the user acceptance of automated vehicles in practice, which is defined as key prerequisite to ensure that the investments on automated vehicles are likely to pay off (Milakis, Snelder, Van Arem, Van Wee & De Almeida Correia, 2017). An example for a testfield that has been opened very recently for the trialing of automated driving technology is the EUREF Campus in Berlin Schöneberg. The EUREF Campus Berlin Schöneberg is a 5.5 hectare large semi-public domain that is the host to approximately 100 companies and research facilities with 1,400 employees at the moment. With the agglomeration of sustainable and economic companies, it strives to develop innovations for smart sustainable cities. Since December 19, 2016, the driverless shuttle by the U.S. based startup Local Motors operates on the EUREF-Campus to provide a shuttle transport for the employees of the EUREF campus as well as national and international guests and visitors of the EUREF campus and other interested users. The driverless shuttle, for which the term 4P vehicle was coined in a previous study (Nordhoff, Van Arem & Happee, 2016), which is a SAE level 4 automation vehicle that does not have a steering wheel, brake or gas pedal, and that requires some supervision either from a driver inside the vehicle or from a remote driver in an external control room. P stands for pod-like to clearly distinguish this type of automated vehicle from the classical and traditional automated automobile that is equipped with standard manual controls. The definition of 4P vehicles corresponds with the recent update of the automation taxonomy by the Society of Automotive Engineers (SAE) proposed in the SAE taxonomy J3016 from September 2016. In this taxonomy, 4P’s are SAE level 4 automation Automated-Dedicated Driving Systems-Dedicated Vehicles (ADS-DV’s) that are driverless vehicles without steer and pedals that cannot be driven in automated mode beyond their operational design domain and that can be temporarily operated by a conventional (inside vehicle) or remote (outside vehicle) driver (SAE, 2016). The driverless shuttle that operates on the EUREF campus operates with a steward on board to supervise the vehicle operations and intervene in cases of emergency. The vehicle currently operates at a speed of 8 km/h. 1.1. Research gaps The last four years have seen a high number of research studies investigating the acceptance of driverless vehicles (e.g., AAA, 2016; Abraham, Lee, Brady, Fitzgerald, Mehler, Reimer & Coughlin, 2016; Bansal, Kockelman, & Singh, 2016; Bazilinskyy, Kyriakidis, & De Winter, 2015; Krueger, Rashidi & Rose, 2016; Fraedrich, Cynganski, Wolf & Lenz, 2016; Kyriakidis, Happee & De Winter, 2015; Hohenberger, Sporrle & Welpe, 2016; Madigan et al., 2016; König & Neumayr, 2017; Nees, 2016; Sanaullah, Hussain, Chaudhry, Case & Enoch, 2017; Schoettle & Sivak, 2015; Schoettle & Sivak, 2016; Souders & Charness, 2016; Tennant, Howard, Franks, Bauer, Stares, Pansegrau,

2


Stysko-Kunkowska & Cuevas-Badallo, 2016; Zmud, Sener & Wagner, 2016). These studies contribute to current research on acceptance of automated vehicles by identifying a number of potential acceptance factors that can be mainly summarized as socio-demographic characteristics, mobility characteristics, psychological characteristics, functional-utilitarian and symbolic-affective motives. The next step in research is to make a systematic use of validated models to understand the factors influencing user acceptance of automated vehicles. A second major shortcoming of previous acceptance studies is the lack of knowledge on the factors that drive acceptance of automated and driverless vehicles without steer and pedals that are tested by individuals under real-world environmental conditions. There has been abundant research on the extent to which a driver interacts and accepts an assisted (e.g. Hoedemaker & Brookhuis, 1998; Rudin-Brown & Parker, 2004), partially (e.g. Louw & Merat, 2017; Van den Beukel & Van der Voort, 2017) or highly automated driving system in artificial environments (e.g. driving simulator) (Merat & Jamson, 2009; Merat, Jamson, Lai & Carsten, 2012). These studies have in common that they focus on automated vehicles that can be controlled by standard manual controls that are used by the driver to take back control from the vehicle and be in the loop again. As a result, the majority of respondents that were sampled in these studies have no concrete and real experience with automated vehicles that lack standard manual controls and that radically deviate from the classical and traditional automobile. One possible explanation for the lack of research on these automated vehicles is their low dispersion in consumer markets. The highest level that is currently being commercialized is SAE level 2 automation (Smith, 2016) with the next step being now adding more automated features (e.g. automated lane overtaking) to realize SAE level 3 (conditional automation). The use of driverless vehicles as feeder systems to public transport, which are the focus of this research here, has been mainly restricted to a few pilot projects worldwide (e.g. City Mobil2, WEpods) and thus only a few people have ever experienced them in daily conditions in real and complex environments, such as the EUREF Campus in Berlin Schöneberg. For these reasons, there is a lack of knowledge of the factors which will affect whether or not people will use these vehicles. 1.2. Research objectives To address the first gap in research, the 4P Acceptance Model was built as a synthesis of current acceptance studies on automated vehicles and other domains, to explain, predict and optimize acceptance of driverless vehicles in public transport technology (Nordhoff, Van Arem & Happee, 2016). The model reflects the findings from the literature by identifying the factors whose role has been investigated in current acceptance studies (e.g. perceived usefulness, social influence, trust) and factors that have been proven to be relevant in areas related to the acceptance and use of information technology, but which still remain hypothetical in the context of automated driving (e.g. habit, price value). The 4P Acceptance Model assumes that the acceptance and use of driverless 4P’s is influenced by utilitarian-functional characteristics and symbolic-affective motives, which are both pivotal to make automated vehicles acceptable for a large part of society. The utilitarian-functional

3


characteristics are captured by the Unified Theory of Acceptance and Use of Technology (UTAUT) as one of the most common technology acceptance models. The symbolic-affective motives are represented by the Pleasure-Arousal-Dominance (PAD) framework to address the emotional value of driving, which has been one of the key reasons for the popularity of the private car. It was recently extended and validated (Nordhoff, Kyriakidis, Van Arem, Graff, Ruhrort & Happee, submitted) in the sense that both the UTAUT model and PAD framework are influenced by a range of different lower-level factors inherent to the individual (e.g. innovativeness) or the vehicle itself (e.g. vehicle speed) and environmental conditions that are external and hence beyond the direct control of the individual (e.g. accident with automated vehicle). The 4P Acceptance Model was validated with data from an online questionnaire with 10.001 respondents worldwide, using structural equation modeling analysis (Nordhoff et al., not yet submitted). The effects of the UTAUT constructs – “effort expectancy” and “social influence” on the acceptance and use of driverless vehicles could not be investigated as their underlying items were excluded in the process of the exploratory factor analysis due to their factor loadings. Therefore, one of the research objectives of this study is to examine the effects of all UTAUT constructs, including performance expectancy, effort expectancy and social influence, on the acceptance and use of driverless vehicles in public transport. To tackle the gap in research on the limited knowledge on the factors that influence acceptance given the lack of user experience with these vehicles, the purpose of this study is to capture the first user perceptions of these automated vehicles by questionnaires that are distributed to respondents on tablet computers. This is the second study next to the study by Madigan, Louw, Dziennus, Graindorge, Ortega, Graindorge and Merat (2016) that we are aware of to the best of our knowledge that has studied the perceptions of real users of real vehicles on (semi-)public roads. For this reason, the research findings of current acceptance studies on automated vehicles need to be interpreted taking into account the general lack of real and concrete user experiences among respondents, which could bias and threaten the validity of results. Exposing users to the technology at a very early stage on a small-scale under limited conditions (e.g. limited speed) makes it possible to gradually and slowly expose and familiarize users with automated public transport technology. Additionally, the introduction of automated vehicles can be linked to the creation of realistic expectations which has been defined as key driver of acceptance (Nees, 2016), because people can experience these vehicles themselves in addition to the current technical difficulties and complexities with which an automated driving system is supposed to cope with at the moment. For example, these include sharper braking in front of obstacles in close proximity of the automated shuttle as well as the transition to the manual modus due to some software failures. Also, the driverless vehicle that provides space for up to eight persons (fig. 1) was supervised by a steward or operator on board the vehicle to intervene in the vehicle operations when necessary.

4


Fig. 1. Driverless shuttle on EUREF campus in Berlin-Schöneberg

2. Methodology 2.1. Instrument development The 37-items-questionnaire reported in this study was administered together with a 15-items questionnaire to assess individual perceptions on certain technical features and the attractiveness of driverless vehicles as mobility offer in the city as well as in rural areas that was executed as part of a pilot project involving the testing of driverless shuttles on the EUREF campus in Berlin. The 37-items questionnaire asked respondents about their intended use of driverless shuttle in everyday conditions, about the perceived usefulness (performance expectancy), ease of use (effort expectancy), social influence, level of trust, ecological norms, and perceived enjoyment. The original version of the questionnaire was in German, but was translated into English as the EUREF Campus attracts many international visitors and guests. Some of the items have been double checked before given their use in previous studies (e.g. Nordhoff et al., not yet submitted; Madigan et al., 2016; Yap et al., 2016), which ensures their correct translation. 2.2. Instrument administration The questionnaire was distributed to respondents using tablet computers, after they took a ride with the driverless shuttle. In this way, it was ensured that only respondents who have tested the driverless shuttle filled out the questionnaire. Data collection took place either in close proximity of the vehicle demonstration, or was carried out by the stewards who were supervising the vehicle in case they need to intervene. All questionnaires were self-administered. The stewards provided some assistance when respondents could not handle the technical functionality of the device. Data were collected between December 19th, 2016 and April 12th, 2017 where the vehicle provided daily shuttle operations from 9

5


to 17 hrs. The information was recorded anonymously and no compensation was offered to respondents. 2.3. Operationalization of study variables Pre-validated measures were used for all constructs using multiple-item, four-point and six-point scales. The UTAUT constructs (performance expectancy, effort expectancy, social influence) were measured using at least two items of each construct of Venkatesh et al.’s (2003) measurement scale as well as the scales adopted and refined by Madigan et al. (2016). 4P acceptance and use was measured by behavioral intention, using Venkatesh et al.’s behavioral intention scale and some of the indicators regarding the use of automated vehicles as last mile transport by Yap et al. (2016). The items for the construct of behavioral intention were formulated in such a way to assess respondents’ behavioral intentions of driverless shuttles as an integral element of their daily lives beyond the demonstration. The items used to measure the willingness to share driverless shuttles were constructed on the basis of two different use cases for automated vehicles presented in a study by Krueger et al. (2016). We see the willingness to share driverless vehicles with other passengers having the same destination as indicator of their intended acceptance and use of driverless vehicles in public transport. Also, in Nordhoff et al. (2016) it was argued that acceptance is a multidimensional measure. 2.4. Analyses at the individual level Descriptive statistics (e.g. means, medians, standard deviations, and frequencies) were calculated for each of the variables. In the second step, spearman correlation coefficients were calculated between age, gender, and certain items measuring performance expectancy, effort expectancy and social influence as well as behavioral intention. In a later version when a more sufficient sample size is achieved, hierarchical multiple regression analysis will be carried out to examine more closely the relations between our independent and dependent study variables. 3. Results 3.1. Demographic profile of respondents In total, 326 respondents participated in the questionnaire. Of our current sample, 62.9% of respondents are male and 35.4% are female. The mean and median age of respondents was 35.11 and 32.00 years, respectively (SD= 14.13). 77.1% of respondents did not work on the EUREF Campus, whereas 21% do so. When being asked about their common transport mode on the EUREF Campus, 70.4% of respondents stated to walk, 17.1% cycle, 7.7% use a conventional vehicle and 3.6% an electric vehicle. 95.7% of respondents have not used the driverless shuttle before, whereas 3.1% of respondents indicated to do so. 3.2. Analyses at the individual level: responses

6


In this section, the descriptive statistics and frequencies for the respondents will be reported. Generally, respondents indicated that the driverless shuttle is useful (M=5.12, SD=1.053, on a scale from 1-6) and would be an important part of the public transport system (M=4.77, SD=1.218, on a scale from 1-6). However, respondents do not necessarily think that the driverless shuttle is more efficient/faster than their current form of travel (M=2.47, SD=1.575, on a scale from 1-6), which is not surprising given the limited speed of the vehicle and the operation of the driverless shuttle under restricted conditions. A similar result could be observed when respondents were asked as to whether they think that the driverless shuttle is better and more convenient for their daily travel than their current form of travel (M=3.19, SD=1.615, on a scale from 1-6). Concerning the ease of use, respondents generally stated that using the driverless vehicle is easy for them (M=4.66, SD=1.426, on a scale from 1-6), and that it would not take a lot of time to learn how to use driverless vehicles (M=4.96, SD=1.60, on a scale from 1-6). Furthermore, respondents believe that the use of the driverless shuttle is similar to the use of current transport systems, such as bus or train (M=4.15, SD=1.413, on a scale from 1-6). However, when compared to their current form of travel, respondents generally did not believe that driverless vehicles will be easier to use than their existing form of transport (M=3.92, SD=1.758, on a scale from 1-6) which is probably due to the current restricted operation of the driverless vehicle, whose operation is linked to the ODD as mentioned before. Concerning the role of social influence, respondents might base their adoption decision on the views of people who are important to them. Thus, respondents do prefer that people who are important to them such as family adopt the driverless vehicle before they do (M=2.69, SD=1.791, on a scale from 1-6), and they agree with the statement that people who are important to them like it when they use the driverless shuttle (M=3.81, SD=1.507, on a scale from 1-6). Again, this finding contradicts the findings of a recent study (König & Neumayr, 2007) which found that self-driving cars apparently don’t give people social recognition. When it comes to respondents’ level of trust, respondents generally feel comfortable in a vehicle without steering wheel, gas or brake pedals (M=4.55, SD=1.284, on a scale from 1-6), and believe that that the driverless shuttle will be safe and reliable in severe weather conditions, such as snow, heavy rain or fog (M=4.55, SD=1.284, on a scale from 1-6). Respondents are less convinced that the driverless shuttle can drive safely and reliably without their assistance (M=3.56, SD=1.722, on a scale from 1-6), but generally felt safe throughout the whole trip (M=4.63, SD=1.404, on a scale from 1-6). Considering people’s need to control the vehicle, our study respondents generally do not want to manually steer the vehicle whenever they want (M=3.29, SD=1.898, on a scale from 1-6), but are more willing to press a button inside the vehicle which can be pressed to stop it in cases of emergency (M=5.19, SD=1.211, on a scale from 1-6). These findings overlap with König and Neumayr (2017) who defined the possibility to take over vehicle control as effective strategic option to increase trust in

7


automated vehicles, and Sanaullah et al. (2017) who refer to the psychological need of people to have at least some level of control, which is why people generally prefer some intermediate automation levels to full automation (Schoettle & Sivak, 2015, 2016). They moderately like the idea that the driverless shuttle operates at a low speed (M=3.28, SD=1.533, on a scale from 1-6) and that a steward supervises the vehicle operations throughout the whole trip (M=4.14, SD=1.609, on a scale from 1-6). Respondents believe that the use of driverless vehicles in public transport (M=5.25, SD=1.098, on a scale from 1-6) is the most attractive application scenario, followed by their use in rural areas (M=5.10, SD=1.222, on a scale from 1-6) and in the city (M=5.04, SD=1.234, on a scale from 1-6). As the automated shuttle is 100% electric, respondents were asked about the level of importance of the protection of the environment for their choice of transport. A majority of respondents like it to use a fully-electric driverless shuttle from the train station to their final destination (M=5.05, SD=1.177, on a scale from 1-6) and indicated that the protection of the environment is crucial for their choice of the driverless shuttle (M=4.72, SD=1.366, on a scale from 1-6). To investigate respondents’ behavioral intentions to use automated shuttles, respondents indicated to intend to use driverless shuttles when they are available at the market (M=5.15, SD=1.140, on a scale from 1-6). They agree with the statement to use the driverless shuttle for their daily mobility trips (M=4.25, SD=1.568, on a scale from 1-6), but are rather indecisive when being asked to their current form of travel by an automated shuttle (M=3.55, SD=1.664, on a scale from 1-6). As regards the frequency of use, respondents stated to be willing to use the driverless vehicle on 1-3 days a week or 1-3 days a month (M=2.24, SD=1.218, on a scale from 1=daily or almost daily, 2=on 1-3 days a week, 3=on 1-3 days a month, 4= less than monthly, 5=never or almost never). Respondents generally display a high willingness to use shared driverless 4P’s in public transport. Thus, respondents indicated to like the idea to share the driverless shuttle together with a few unknown travelers having the same destination like them (M=4.74, SD=0.1.383, on a scale from 1-6), which corresponds with their general agreement to use a shared automated vehicle that is used with other 6-8 passengers at the same time (M=5.35, SD=0.940, on a scale from 1-6). 3.3. Correlational analyses at the individual level The calculation of Spearman correlation coefficients found no statistical significant effects were found between gender and performance expectancy, effort expectancy, social influence on the one hand and between the willingness to use driverless vehicles (in public transport) on the other (behavioral intention). This is in line with prior research (Krueger et al., 2016; Zmud et al., 2016). A negative statistical significant correlation was found between age and the extent to which driverless vehicles are perceived to be easy to use (r=-0.147, p=0.01). Furthermore, the correlation between age and social influence is negative and statistical significant, indicating that for younger people the influence of other people important to them matters less for their adoption decisions of automated vehicles (r=-0.146, p=0.01)

8


The extent to which respondents consider the driverless shuttle to be useful and an important part of the traffic system as measurements for the UTAUT construct performance expectancy is positively and statistically significant correlated to individuals’ behavioral intentions to use a driverless shuttle on their daily trips, respectively (correlation coefficient r=0.440, p=0.01; r=0.510, p=0.01). This means that individuals who consider driverless shuttles to be useful and an important part of the traffic system are more likely to consider the use of driverless shuttles as transport mode on their daily trips. Additionally, individuals who consider driverless vehicles to be more useful consider the use of driverless shuttles from the train station or some other public transport stop to the final destination and vice versa (r=0.443, p=0.01), or as mobility offer in the city (r=0.257, p=0.01). The perceived usefulness of driverless shuttles does not predict its use as mobility offer in rural areas, which will be further discussed in the remaining section. Individuals who believe that the use of the driverless shuttle is easier than their current form of transport as an indicator for the construct effort expectancy are more likely to consider the choice of driverless shuttles on their daily trips (r=0.312, p=0.01). Positive and statistical significant relations were found between the extent to which individuals would like it that people who are important to them would like it when they adopt the driverless shuttle before they do it (social influence) and the use of driverless shuttles in rural areas (r=0.244, p=0.01), whilst no statistical significant effects were found on the use of driverless shuttles in the city. 4. Conclusions and discussion This is the second study to the best of our knowledge next to the study of Madigan et al. (2016) that investigates the acceptance of driverless vehicles in public transport that were tested by real users on semi-public roads. Our study respondents very generally very positive towards the use of driverless vehicles in public transport and can also imagine their use in the city as well as in rural areas. Also, the willingness to share driverless vehicles with other fellow travelers is very high among respondents, where the willingness to share driverless vehicles is seen as additional indicator for the acceptance and use of driverless vehicles in general. Additionally, our study respondents believed that driverless vehicles will be useful and easy to use, but when comparing them to their current mode of transport, the perceived usefulness and ease of use declines. This means that our research findings should also be interpreted with the current restrictions under which driverless vehicles currently operate (e.g. limited speed). Future research is needed to re-visit individuals’ perceptions as regards the perceived usefulness and ease of use of automated vehicles when their use is more widespread, and when they operate under less restrictive conditions on public roads. The desire of individuals to take over control from the vehicle needs to be considered with care as the human intervention in the vehicle’s trajectory may seriously jeopardize traffic safety and nullify the effects of vehicle automation altogether. Therefore, a safety button inside the driverless vehicle may be a good way to increase trust among respondents and their acceptance, but only if it’s safe and proper use can be warranted under all conditions. Its misuse can have serious counter effects and may hamper acceptance in the end. Thus, the present study sheds

9


light on the design implications to make driverless vehicles a success. Furthermore, the use of driverless vehicles in rural areas was not predicted by perceived usefulness, which may be due to the higher car dependency in these areas. Therefore, to make driverless vehicles a success, it would be of utmost important to increase the popularity of new mobility services in rural areas in the first step and then integrate automated vehicles in this mobility offer. These findings are in line with a recent study (König & Neumayr, 2017), which investigated the attitudes of 489 respondents from 33 countries by an online survey and found that urban residents tended to be more open towards automated driving than rural residents, probably because of the higher car dependency in rural areas as mentioned before (König & Neumayr, 2017). Thus, the same study also found that car use is negatively correlated to a positive attitude towards automated vehicles, meaning that the more people make use of the car, the less likely they are positive towards it. This finding corresponds with the result of our previous study (Nordhoff, Madigan, Happee, Van Arem, Schönduwe, & Merat, not yet submitted), which found that active car users may be less likely to adopt automated vehicles in public transport. The full benefits of vehicle automation are likely to pay off in the long-run when rural areas see a higher diffusion of new mobility concepts such as ride sharing schemes and a lower number of privately owned cars per household. However, our results should be interpreted with care since our sample of study respondents is likely to be skewed towards urban residents as the pilot project takes place in Berlin. Moreover, this study confirms the importance of the UTAUT constructs performance, effort expectancy and social influence and our dependent variables. Generally, no statistical significant gender-specific effects were found, which mirrors current literature. A negative statistical significant correlation was found between age and the extent to which driverless vehicles are perceived to be easy to use, which shows that younger people find automated vehicles less easy to use. This finding is surprising because and should be revisited by future studies. However, more sophisticated analyses will be performed in a consecutive study to model the correlations of the key constructs in this study by applying a more powerful multivariate statistical analysis. The limitations of the study need to be taken into account when analyzing the results. First, the vehicle currently operates under restricted conditions (e.g. limited speed) that are likely to influence individual perceptions of the vehicle itself and may make it more difficult to envision its use as daily transport mode. Second, the sample of respondents may be skewed towards individuals that are more innovative and open towards sustainable innovations in the mobility sector as the EUREF campus assembles many companies whose business model is centered on the conceptualization and implementation of sustainable mobility innovations. Third, the views of rural residents are likely to be underrepresented in this study sample as the pilot project takes place in Berlin.

10


References 1.

AAA.

(2016).

Fact

Sheet:

Vehicle

Technology

Survey.

http://publicaffairsresources.aaa.biz/wpcontent/uploads/2016/02/Automotive-Engineering-ADAS-Survey-Fact-S heet-FINAL-3.pdf. Accessed April 11, 2016. 2. Abraham, Lee, Brady, Fitzgerald, Mehler, Reimer, and Coughlin. (2016). Autonomous Vehicles, Trust, and Driving

Alternatives:

A

survey

of

consumer

preferences.

http://agelab.mit.edu/files/publications/2016_6_Autonomous_Vehicles_Consumer_Preferences.pdf.

Accessed

September 12, 2016. 3. Bansal, P., Kockelman, K.M., & Singh, A. (2016). Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C, 67, 1-14. 4. Bazilinskyy, P., Kyriakidis, M., and De Winter, J. (2015). An internal crowdsourcing study into people’s statements on fully automated driving. Procedia Manufacturing, 3, 2534-2542. 5.

Continental

(2013).

Mobility

Study

2013.

http://www.continentalcorporation.com/www/download/pressportal_jp_jp/general/ov_automated_driving_jp/ov_ mobility_ study_jp/download_channel/pres_mobility_study_jp.pdf. Accessed September 22, 2016. 6.

Continental

(2015).

Continental

Mobility

Study

2015.

https://www.contionline.com/www/download/pressportal_com_en/themes/initiatives/chanel_mobility_study_en/ ov_mobility_study2015_en/dow. Accessed September 22, 2016. 7. Danise, A. (2015). Women Say No Thanks to Driverless Cars, Survey Finds; Men Say Tell MeMore. https://www.nerdwallet.com/blog/insurance/survey-consumer-fears-self-driving-cars/. Accessed April 11, 2016. 8. Detecon International GmbH. (2016). Autonomes Fahren: Wenn das Lenkrad zur Sonderausstattung wird: Eine

empirische

Untersuchung

der

Akzeptanz

autonom

fahrender

Fahrzeuge.

https://www.detecon.com/sites/default/files/gladbach_-_studie_autonomes_fahren_final.pdf. Accessed April 21, 2016. 9. Fraedrich, E., Cynganski, R., Wolf, I., and Lenz, B. (2016). User Perspectives on Autonomous Driving: A Use-Case-Driven

Study

in

Germany.

https://www.geographie.huberlin.de/de/institut/publikationsreihen/arbeitsberichte/download/Arbeitsberichte_Hef t_187.pdf. Accessed August 02, 2016. 10. Hoedemaeker, M., & Brookhuis, K. A. (1998). Behavioural adaptation to driving with an adaptive cruise control (ACC). Transportation Research Part F: Traffic Psychology and Behaviour, 1(2), 95-106. 11. Hohenberger, C., Sporrle, M., Welpe, I.M. (2016). How and why do men and women differ in their

11

User Acceptance of Driverless Shuttles Running in an Open and Mixed Traffic Environment willingness to use automated cars? The influence of emotions across different age groups. Transportation Research Part A, 94, 374-385. 12. Krueger, R., Rashidi, T.H., and Rose, J.M. (2016). Preferences for shared autonomous vehicles. Transportation Research Part C, 69, 343- 355. 13. Kyriakidis, M., Happee, R., and De Winter, J.C.F. (2015). Public opinion on automated driving: Results of an international questionnaire among 5.000 respondents. Transportation Research Part F: Traffic Psychology and Behavior, 32, 127-140. 14. König, M. & Neumayr, L. (2017). Users’ resistance towards radical innovations: The case of the self-driving car. Transportation Research Part F, 44, 47-52. 15. Louw, T., & Merat, N. (2017). Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation. Transportation Research Part C: Emerging Technologies, 76, 35-50. 16. Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., and Merat, N. (2016). Acceptance of Automated Road Transport Systems (ARTS): An adaptation of the UTAUT model. Proceedings of the 6th Transport Research Arena, April 18-21, Warsaw, Poland. 17. Merat, N., & Jamson, A. H. (2009). How do drivers behave in a highly automated car. In Proceedings of the 5th international driving symposium on human factors in driver assessment, Training and Vehicle Design (pp. 514-521). 18. Merat, N., Jamson, A. H., Lai, F. C., & Carsten, O. (2012). Highly automated driving, secondary task performance, and driver state. Human factors, 54(5), 762-771. 19. Milakis, D., Snelder, M., Van Arem, B., Van Wee, B., Correira, G.H.D.A. (2017). Development and transport implications of automated vehicles in the Netherlands: scenarios for 2030 and 2050. EJTIR 17, 1, 63-85. 20. Nordhoff, S., Van Arem, B., and Happee, R. (2017). A Conceptual Model to Explain, Predict, and Improve User Acceptance of Driverless 4P Vehicles. Transportation Research Records, 2602-2608. 21. Nordhoff, S., Kyriakidis, M., Van Arem, B., Ruhrort, L., Graff, A., & Happee, R. (not yet submitted). Modelling Acceptance of Driverless Vehicles in Public Transport: Results of an International Survey with 10.001 Respondents Worldwide. 22. Nordhoff, S., Madigan, R., Happee, R., Van Arem, B., Schönduwe, R., & Merat, N. (submitted). Using the 4P model to understand why people choose to use automated vehicles in public transport. Transportation Research Procedia. 23. Nees, M.A. (2016). Acceptance of Self-Driving Cars: An Examination of Idealized versus Realistic Portrayals with a Self-Driving Car Acceptance Scale. Proceedings of the Human Factors and Ergonomics

12

User Acceptance of Driverless Shuttles Running in an Open and Mixed Traffic Environment Society 2016 Annual Meeting, pp. 1449-1453. 24. Rudin-Brown, C. M., & Parker, H. A. (2004). Behavioural adaptation to adaptive cruise control (ACC): implications for preventive strategies. Transportation Research Part F: Traffic Psychology and Behaviour, 7(2), 59-76. 25. Payre, W., Cestac, J., and Delhomme, P. (in press). Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation Research Part F: Traffic Psychology and Behavior, 27, 252-263. 26. Payre, W., Cestac, J., and Delhomme, P. (2016). Fully Automated Driving: Impact of Trust and Practice on Manual Control Recovery. Human Factors, 58 (2), 229-241. 27. Sanaullah, I., Hussain, A., Chaudhry, A., Case, K., & Enoch, M. Autonomous Vehicles in Developing Countries: A Case Study on User’s View Point in Pakistan. In: Advances in Human Aspects of Transportation, Advances in Intelligent Systems and Computing 484. Springer International Publishing Switzerland, 2017, pp. 561-569. 28. Schoettle, B., & Sivak, M. (2014). A Survey of Public Opinion about 26 Autonomous and Self-Driving Vehicles

in

the

U.S.,

the

U.K.,

and

Australia.

27

Michigan,

USA.

http://deepblue.lib.umich.edu/bitstream/handle/2027.42/108384/103024.pdf?sequence=1&isAllowed=y. Accessed January 2, 2015. 29. Schoettle, B., and Sivak, M. (2015). Motorists’ Preferences for Different Levels of Vehicle Automation. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/114386/103217.pdf?sequence=1&isAllowed=y. Accessed July 22, 2015. 30. Schoettle, B., and Sivak, M. (2016). Motorists’ Preferences for Different Levels of Vehicle Automation: 2016. http://www.umich.edu/~umtriswt/PDF/SWT-2016-8.pdf. Accessed July 22, 2015. 31. Schaefer, K.E., and Straub, E.R. (2016). Will Passengers Trust Driverless Vehicles? Removing the steering wheel and pedals. 2016 IEEE International Multidisciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA). 32. Souders, D., and Charness, N. Challenges of Older Drivers’ Adoption of Advanced Driver Assistance Systems and Autonomous Vehicles. In: Human Aspects of IT for the Aged Population. Healthy and Active Aging. Lecture Notes in Computer Science. Springer International Publishing Switzerland, 2016, 9755, pp. 428-440. 33. Tennant, C., Howard, S., Franks, B. Bauer, M.W., Stares, S., Pansegrau, P., Stysko-Kunkowska, M. and Cuevas-Badallo, A. (2016). Autonomous Vehicles – Negotiating a Place on the Road: A study on how drivers feel

about

Interacting

with

Autonomous

Vehicles

on

the

Road.

Executive

Summary.

www.lse.ac.uk/newsAndMedia/PDF/AVs-negotiating-a-place-on-the-road-1110.pdf. Accessed October, 31, 2016.

13

User Acceptance of Driverless Shuttles Running in an Open and Mixed Traffic Environment 34.

TNS

Opinion

&

Social.

(2015).

Special

Eurobarometer

427

„Autonomous

Systems”.

http://ec.europa.eu/public_opinion/archives/ebs/ebs_427_en.pdf. Accessed September 30, 2016 35. Wintersberger, P., Frison, A.-K., Riener, A. & Boyle, L.N. (2016). Towards a Personalized Trust Model for Highly Automated Driving. In: Weyers, B. & Dittmar, A., Mensch und Computer 2016 –Workshopband. Aachen: Gesellschaft für Informatik e.V. 36. Van den Beukel, A. P., & Van der Voort, M. C. (2017). How to assess driver's interaction with partially automated driving systems–A framework for early concept assessment. Applied ergonomics, 59, 302-312. 37. Yap, M.D., Correira, G., and Van Arem, B. (2015). Valuation of travel attributes for using automated vehicles as egress transport of multimodal train trips. Transportation Research Part A: Policy and Practice, 94, 1-16. 38. Zmud, J., Sener, I., and Wagner, J. (2016). Consumer Acceptance and Travel Behaviour: Impacts of Automated Vehicles. http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/PRC-15-49-F.pdf. Accessed April 13, 2016.4

14