Using the wizard of Oz paradigm to prototype automated vehicles

Müller, Andrea Isabell; Weinbeer, Veronika; Bengler, Klaus

doi:10.1145/3349263.3351526

Cited by 13 publications

(8 citation statements)

References 12 publications

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“…Firstly, the method is not dependent on participants believing in the illusion of an AV to create a valid simulation. Secondly, the driving style of an AV is more consistent than a Wizard-of-Oz-simulated automated driving style because the variance of the human driving behavior does not have to be accounted for [77].…”

Section: Discussion and Future Researchmentioning

confidence: 99%

Evaluation of a Human–Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

et al. 2021

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Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs.

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Section: Discussion and Future Researchmentioning

confidence: 99%

Evaluation of a Human–Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

et al. 2021

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“…We applied the method of Wizard-of-Oz to provide a realistic driving setting. According to the study by Müller, Weinbeer and Bengler (2019), a Wizard-of-Oz vehicle study can be interpreted as a psychological test in which "the test manual defines the studyspecific testing procedure for examiners and the automation behavior simulated by the driving wizard" (Müller et al, 2019, p. 183). Based on this, the main test quality criteria of psychological tests (i.e., objectivity, reliability, and validity) can be applied to the Wizard-of-Oz method.…”

Section: Methodological Discussion On Our Wizard-of-oz Approachmentioning

confidence: 99%

“…Third, validity in the context of Wizard-of-Oz vehicle studies addresses whether participants actually believed in the cover story (i.e., vehicle being equipped with a technical Level 3 driving automation system) and whether the simulated Level 3 driving automation truly behaves like a technical Level 3 driving automation system. Müller et al (2019) suggest asking participants "Do you trust the vehicle?" after the ride.…”

Section: Methodological Discussion On Our Wizard-of-oz Approachmentioning

confidence: 99%

“…As trust values increased numerically after experiencing the simulated Level 3 driving automation, it can be assumed that participants trusted the driving automation system. Following the study by Müller et al (2019), we can infer that participants believed in the cover story. In addition, after debriefing participants, their reactions revealed great surprise.…”

Section: Methodological Discussion On Our Wizard-of-oz Approachmentioning

confidence: 99%

“…For example, one participant of Experiment 1 compared it to the situation when a child learns that Santa Claus is not real. In summary, the first validity aspect by Müller et al (2019) can be assumed to be given. The second aspect cannot be estimated, yet.…”

Section: Methodological Discussion On Our Wizard-of-oz Approachmentioning

confidence: 99%

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Non-driving-related tasks during level 3 automated driving phases–measuring what users will be likely to do.

Shi¹,

Frey²

2021

Technology, Mind, and Behavior

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After activation of a Society of Automotive Engineers (SAE) Level 3 automated driving function (SAE International, 2021), the function takes over the driving task and the human user may engage in other, non-driving-related tasks (NDRTs). Meanwhile, the user needs to remain receptive to requests by the function because s/he needs to reengage in the driving task, when the function approaches a system limit and requests the user to take over. Hence, the effects of NDRT engagement on driver state and takeover behavior have been investigated closely. However, concerning relevance to traffic safety, it is important to take a step back and investigate what NDRTs users are likely to engage in, and what methods are suitable to collect respective data. Two experiments were conducted to answer these questions. In Experiment 1, participants experienced Level 3 automated driving in a Wizard-of-Oz vehicle on German motorways. After the ride, participants were asked to name NDRTs that they would engage in, if the function was available in their own vehicle. In Experiment 2, participants were asked to bring their own NDRTs along and experienced Level 3 automated driving in the Wizard-of-Oz vehicle. Comparison of results shows preferences of similar NDRTs (e.g., smartphone usage and reading). Moreover, we found that different methods provide different insights into NDRT engagement (i.e., engagement rate, total duration rate of engagement, and naming rate). Integrating our results in current literature landscape highlights the strong dependence of resulting NDRTs from the investigation method. Results, strengths, and weaknesses of the employed methods are discussed.

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Vorreiter: Manoeuvre-Based Steering Gestures for Partially and Highly Automated Driving

Flemisch

Diederichs

Meyer

et al. 2020

Human–Computer Interaction Series

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Using the wizard of Oz paradigm to prototype automated vehicles

Cited by 13 publications

References 12 publications

Evaluation of a Human–Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

Evaluation of a Human–Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

Non-driving-related tasks during level 3 automated driving phases–measuring what users will be likely to do.

Vorreiter: Manoeuvre-Based Steering Gestures for Partially and Highly Automated Driving

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