Investigating the Effect of Different Autonomy Levels on User Acceptance and User Experience in Self-driving Cars with a VR Driving Simulator

2018 ◽  
pp. 247-256 ◽  
Author(s):  
Jana Helgath ◽  
Philip Braun ◽  
Andreas Pritschet ◽  
Maximilian Schubert ◽  
Patricia Böhm ◽  
...  
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
User Acceptance ◽  
Self Driving Cars

scholarly journals ACCEPTANCE AND USER EXPERIENCE OF WEARABLE ASSISTIVE DEVICES FOR INDUSTRIAL PURPOSES

2020 ◽  
Vol 1 ◽  
pp. 1515-1520
Author(s):  
E. Papp ◽  
C. Wölfel ◽  
J. Krzywinski
Keyword(s):  
Technology Acceptance ◽  
User Experience ◽  
User Acceptance ◽  
Assistive Devices ◽  
Acceptance Criteria ◽  
Development Processes

AbstractThis paper presents experience-oriented aspects of the development of wearable assistive devices (exoskeletons) for industrial purposes, an area which has only begun to be explored. Our research aims to examine user acceptance criteria for assistive devices and understand the meaning of interaction with wearable assistive devices for the users. The resulting models deliver new insights about the importance of user experience for technology acceptance and should be generally considered in development processes of wearable assistive devices.

1A1-G05 Development of a Driving Simulator to Evaluate User Experience for Advanced Intelligent Vehicles

2015 ◽  
Vol 2015 (0) ◽  
pp. _1A1-G05_1-_1A1-G05_4
Author(s):  
Mitsuhiro KAMEZAKI ◽  
Udara MANAWADU ◽  
Masaaki ISHIKAWA ◽  
Shigeki SUGANO
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
Intelligent Vehicles

Elicitation of mental states and user experience factors in a driving simulator

2013 ◽  
Author(s):  
A. Hess ◽  
J. Jung ◽  
A. Maier ◽  
R. Taib ◽  
K. Yu ◽  
...  
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
Mental States

scholarly journals Effect of Robo-Taxi User Experience on User Acceptance: Field Test Data Analysis

2021 ◽  
pp. 036119812110415
Author(s):  
Sunghee Lee ◽  
Soyoung Yoo ◽  
Seongsin Kim ◽  
Eunji Kim ◽  
Namwoo Kang
Keyword(s):  
User Experience ◽  
Statistical Models ◽  
Structural Equation ◽  
User Acceptance ◽  
Equation Modeling ◽  
Perceived Safety ◽  
Taxi Service ◽  
Meaningful Relationships ◽  
Service Experiences ◽  
Depth Interviews

With the advancement of self-driving technology, the commercialization of robot taxi (Robo-taxi) services is expected. However, there is some skepticism as to whether such taxi services will be successfully accepted by real customers because of perceived safety-related concerns; therefore, studies focused on user experience have become more crucial. Although many studies statistically analyze user experience data obtained by surveying individuals’ perceptions of Robo-taxis or indirectly through simulators, there is a lack of research that statistically analyzes data obtained directly from actual Robo-taxi service experiences. Accordingly, based on the user experience data obtained by implementing a Robo-taxi service in the downtown of Seoul and Daejeon in South Korea, this study quantitatively analyzes the effect of user experience on user acceptance through structural equation modeling and path analysis. Balanced and highly valid insights were also obtained by re-analyzing meaningful relationships obtained through statistical models based on the results of in-depth interviews. The results revealed that the experience of the traveling stage had the greatest effect on user acceptance, and the cutting-edge nature of the service and apprehension of technology were emotions that had a significant effect on user acceptance. Based on these findings, guidelines are suggested for the design and marketing of future Robo-taxi services.

Acceptance of Self-driving Cars

2016 ◽  
Vol 60 (1) ◽  
pp. 1449-1453 ◽  
Author(s):  
Michael A. Nees
Keyword(s):  
User Acceptance ◽  
Emerging Technology ◽  
End Users ◽  
Measurement Scale ◽  
Factors Affecting ◽  
Potential Benefits ◽  
Potential Factors ◽  
Self Driving Cars ◽  
Unrealistic Expectations

Despite enthusiastic speculation about the potential benefits of self-driving cars, to date little is known about the factors that will affect drivers’ acceptance or rejection of this emerging technology. Gaining acceptance from end users will be critical to the widespread deployment of self-driving vehicles. Long-term acceptance may be harmed if initial acceptance is built upon unrealistic expectations developed before people interact with these systems. A brief (24-item) measurement scale was created to assess acceptance of self-driving cars. Before completing the scale, participants were randomly assigned to read short vignettes that featured either a realistic or an idealistic description of a friend’s experiences during the first six months of owning a self-driving car. A small but significant effect showed that reading an idealized portrayal in the vignette resulted in higher acceptance of self-driving cars. Potential factors affecting user acceptance of self-driving cars are discussed. Establishing realistic expectations about the performance of automation before users interact with self-driving cars may be important for long-term acceptance.

scholarly journals Improving Passenger Experience and Trust in Automated Vehicles Through User-Adaptive HMIs: “The More the Better” Does Not Apply to Everyone

2021 ◽  
Vol 3 ◽  
Author(s):  
Franziska Hartwich ◽  
Cornelia Hollander ◽  
Daniela Johannmeyer ◽  
Josef F. Krems
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
Individual Characteristics ◽  
User Requirements ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Information Availability ◽  
Related Information ◽  
Expected Benefits

Automated vehicles promise transformational benefits for future mobility systems, but only if they will be used regularly. However, due to the associated loss of control and fundamental change of in-vehicle user experience (shifting from active driver to passive passenger experience), many humans have reservations toward driving automation, which question their sufficient usage and market penetration. These reservations vary based on individual characteristics such as initial attitudes. User-adaptive in-vehicle Human-Machine Interfaces (HMIs) meeting varying user requirements may represent an important component of higher-level automated vehicles providing a pleasant and trustworthy passenger experience despite these barriers. In a driving simulator study, we evaluated the effects of two HMI versions (with permanent vs. context-adaptive information availability) on the passenger experience (perceived safety, understanding of driving behavior, driving comfort, driving enjoyment) and trust in automated vehicles of 50 first-time users with varying initial trust (lower vs. higher trust group). Additionally, we compared the user experience of both HMIs. Presenting driving-related information via HMI during driving improved all assessed aspects of passenger experience and trust. The higher trust group experienced automated driving as safest, most understandable and most comfortable with the context-adaptive HMI, while the lower trust group tended to experience the highest safety, understanding and comfort with the permanent HMI. Both HMIs received positive user experience ratings. The context-adaptive HMI received generally more positive ratings, even though this preference was more pronounced for the higher trust group. The results demonstrate the potential of increasing the system transparency of higher-level automated vehicles through HMIs to enhance users’ passenger experience and trust. They also consolidate previous findings on varying user requirements based on individual characteristics. User group-specific HMI effects on passenger experience support the relevance of user-adaptive HMI concepts addressing varying needs of different users by customizing HMI features, such as information availability. Consequently, providing full information permanently cannot be recommended as a universal standard for HMIs in automated vehicles. These insights represent next steps toward a pleasant and trustworthy passenger experience in higher-level automated vehicles for everyone, and support their market acceptance and thus the realization of their expected benefits for future mobility and society.

scholarly journals Effectiveness and user acceptance of infotainment-lockouts: A driving simulator study

2019 ◽  
Vol 60 ◽  
pp. 643-656 ◽  
Author(s):  
Tirza Jung ◽  
Christina Kaß ◽  
Dieter Zapf ◽  
Heiko Hecht
Keyword(s):  
Driving Simulator ◽  
User Acceptance ◽  
Simulator Study

Fostering User Acceptance and Trust in Fully Automated Vehicles: Evaluating the Potential of Augmented Reality

2019 ◽  
Vol 27 (1) ◽  
pp. 46-62 ◽  
Author(s):  
Philipp Wintersberger ◽  
Anna-Katharina Frison ◽  
Andreas Riener ◽  
Tamara von Sawitzky
Keyword(s):  
Augmented Reality ◽  
Driving Simulator ◽  
Contact Lenses ◽  
User Acceptance ◽  
Automated Driving ◽  
Good Opportunity ◽  
Technological Advances ◽  
Integrated Devices ◽  
Dense Fog ◽  
Quantitative Results

Lack of trust in or acceptance of technology are some of the fundamental problems that might prevent the dissemination of automated driving. Technological advances, such as augmented reality aids like full-sized windshield displays or AR contact lenses, could be of help to provide a better system understanding to the user. In this work, we picked up on the question of whether augmented reality assistance has the potential to increase user acceptance and trust by communicating system decisions (i.e., transparent system behavior). To prove our hypothesis, we conducted two driving simulator studies to investigate the benefit of scenario augmentation in fully automated driving—first in normal ([Formula: see text]) and then in rearward viewing ([Formula: see text]) direction. Quantitative results indicate that the augmentation of traffic objects/participants otherwise invisible (e.g., due to dense fog), or the presentation of upcoming driving maneuvers while sitting backwards, is a feasible approach to increase user acceptance and trust. Results are further backed by qualitative findings from semistructured interviews and UX curves (a method to retrospectively report experience over time). We conclude that the application of augmented reality, in particular with the emergence of more powerful, lightweight, or integrated devices, is a good opportunity with high potential for automated driving.

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