How to Design Valid Simulator Studies for Investigating User Experience in Automated Driving

Hock, Philipp; Kraus, Johannes; Babel, Franziska; Walch, Marcel; Rukzio, Enrico; Baumann, Martin

doi:10.1145/3239060.3239066

Cited by 37 publications

(20 citation statements)

References 73 publications

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“…On the side of the user, personality (e.g., Merritt and Ilgen, 2008;Merritt et al, 2015;Kraus et al, 2019a) as well as emotional states (e.g., Stokes et al, 2010;Merritt, 2011) have been found to influence trust formation and explain inter-individual differences in trust toward specific automated systems. In support of this notion, reviews and meta-analyses in human-machine interaction propose personality to be an important predictor for inter-individual differences in trust in automation (e.g., Lee and See, 2004;Hancock et al, 2011;Hoff and Bashir, 2015;Schaefer et al, 2016;Hock et al, 2018). To extend the understanding of the role of emotional states and personality for the dynamic formation of trust in automation, this research investigates the relationships of depressiveness, negative self-evaluations, and anxiety to learned trust in an automated driving system.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Kraus¹,

Scholz²,

Meßner

et al. 2020

Front. Psychol.

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The advantages of automated driving can only come fully into play if these systems are used in an appropriate way, which means that they are neither used in situations they are not designed for (misuse) nor used in a too restricted manner (disuse). Trust in automation has been found to be an essential psychological basis for appropriate interaction with automated systems. Well-balanced system use requires a calibrated level of trust in correspondence with the actual ability of an automated system. As for these far-reaching implications of trust for safe and efficient system use, the psychological processes, in which trust is dynamically calibrated prior and during the use of automated technology, need to be understood. At this point, only a restricted body of research investigated the role of personality and emotional states for the formation of trust in automated systems. In this research, the role of the personality variables depressiveness, self-efficacy, self-esteem, and locus of control for the experience of anxiety before the first experience with a highly automated driving system were investigated. Additionally, the relationship of the investigated personality variables and anxiety to subsequent formation of trust in automation was investigated. In a driving simulator study, personality variables and anxiety were measured before the interaction with an automated system. Trust in the system was measured after participants drove with the system for a while. Trust in the system was significantly predicted by state anxiety and the personality characteristics self-esteem and selfefficacy. The relationships of self-esteem and self-efficacy were mediated by state anxiety as supported by significant specific indirect effects. While for depression the direct relationship with trust in automation was not found to be significant, an indirect effect through the experience of anxiety was supported. Locus of control did not show a significant association to trust in automation. The reported findings support the importance of considering individual differences in negative self-evaluations and anxiety when being introduced to a new automated system for individual differences in trust in automation. Implications for future research as well as implications for the design of automated technology in general and automated driving systems are discussed.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Kraus¹,

Scholz²,

Meßner

et al. 2020

Front. Psychol.

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“…Relative validity of effects in driving simulator studies can well be assumed [126], but there are still research efforts necessary for validating safety relevant human-automation interaction scenarios. Despite this criticism, we acknowledge that driving simulators are still immersive research tools providing a certain, and, in many cases sufficient, degree of external validity [127,128]. Nevertheless, the present results point towards the need of conducting studies in real vehicles equipped with driving automation technology [50,129,130], or Wizard-of-Oz Settings [48,49,131].…”

Section: Study Setupmentioning

confidence: 71%

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

et al. 2020

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During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety.

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“…Now that we have our identified studies accumulated, we can assess the studies against the background of VR dimensions [2] and simulation criteria [13].…”

Section: Resultsmentioning

confidence: 99%

“…Due to insufficient description, we cannot make a statement regarding the sound environment in the other studies. Questionnaires are administered before and after studies and therefore are not presented during VR ses-sions as suggested by [13]. However, considering the short duration of VR experience for users, we did not find any immersion drawbacks or need to implement within-VR questionnaires.…”

Section: Resultsmentioning

confidence: 99%

“…Hock et al [13] proposed a checklist of eight points to consider in a driver simulation study. Considering our scope immersion and interactivity for eHMI, i.e., the view point of traffic participants, we concentrate on two out of the eight points: 1) simulator sickness (i.e., low motion speed, short VR exposure, for better readability we will refer to this as continuous immersion) and 2) simulator training (some form of familiarization for participants to get accustomed).…”

Section: Automotive Hmi Development In Vrmentioning

confidence: 99%

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Automotive eHMI Development in Virtual Reality: Lessons Learned from Current Studies

Temme

Oehl

2020

Communications in Computer and Information Science

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More and more studies in automotive research and development are conducting user-centered development in the emerging field of external humanmachine interfaces (eHMI) in virtual reality (VR). As time, cost and risk are decreasing with progressively affordable sophisticated VR technologies, researchers have shifted to virtual testing. Within this context, they use a variety of methods and technologies to develop new designs but so far little examination has been done towards validity of virtualization and description of the technical setup. As level of immersion is one of the current pillars in VR and technology evolves rapidly, study setups differ a lot in recent years, resulting in poorer comparability. In this paper, our goal is to review the current generation of VR studies in automotive eHMI development and extract in the sense of a lessonslearned approach best practices with regard to their VR setup. For that, we assessed a total of six current studies published between 2017 and April 2020 in automotive eHMI development to extract lessons learned from study designs and virtualization setups. We took a look at hardware and software used as well as study procedure. The results allow us to find useful conclusions on automotive eHMI development practices in VR.

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How to Design Valid Simulator Studies for Investigating User Experience in Automated Driving

Cited by 37 publications

References 73 publications

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

Automotive eHMI Development in Virtual Reality: Lessons Learned from Current Studies

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