Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat

Petermeijer, Sebastiaan M.; Cieler, Stephan; Winter, Joost de

doi:10.1016/j.aap.2016.12.001

Cited by 99 publications

(47 citation statements)

References 37 publications

(60 reference statements)

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“…That is, the steer turn reaction times do not show any significant differences. Previous studies [10], [18], using the same measures, found similar 'diluting effects'. In order to improve the driver's decision making in the later stages of take-over process, more informative displays (e.g., augmented feedback) may be needed.…”

Section: Discussionsupporting

confidence: 59%

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Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants

Petermeijer

Doubek

Winter

2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Section: Discussionsupporting

confidence: 59%

“…The independent variable for the betweensubjects design was the non-driving task (Video, Call, Reading). Dependent variables, as used in earlier studies [8], [10], [18], were:…”

Section: Variablesmentioning

confidence: 99%

Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants

Petermeijer

Doubek

Winter

2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…6). The level of significance was visualized as the negative base-10 logarithm of the p-value, where large values represent small p-values in a similar fashion to the 'Manhattan' plot [53], [69]- [71]. Our use of multiple Wilcoxon signed-rank tests allows for a high temporal resolution (as opposed to using larger bin sizes and fewer tests).…”

Section: G Statistical Analysesmentioning

confidence: 99%

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Eriksson

Petermeijer

Zimmermann

et al. 2019

IEEE Trans. Human-Mach. Syst.

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This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. ["A model for types and levels of human interaction with automation," IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisitionlow), sphere (information acquisition-high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

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“…Telpaz et al and Petermeijer et al suggested that tactile feedback leads to a faster response time compared to no tactile feedback [29,30]. Naujoks et al found that visual-auditory warnings were more efficient to reduce drivers' reaction times compared to a visual-only [31].…”

Section: Resultsmentioning

confidence: 99%

Situation Awareness and Transitions in Highly Automated Driving: A Framework and Mini Review

Son¹,

Park²

2017

J Ergonomics

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The human factors of transition in highly automated driving is becoming crucial, because transitions between the driver and the automation will remain a key element of automated driving until fully automated driving replace manual operation. This paper aims to suggest the framework to investigate human factors in transition of highly automated driving. The framework classifies transitions according to the transition initiator, control after transition, and situation awareness. Based on the framework, we retrieved previous studies and categorized their experimental designs. The inclusion criteria for the review were transitions which involved SAE level 3 automated driving and the research topics on driver behavior and/or performance during a transition. Finally, we interpreted the empirical studies on transitions using the proposed framework, and suggested areas for future research.

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Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat

Cited by 99 publications

References 37 publications

Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants

Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Situation Awareness and Transitions in Highly Automated Driving: A Framework and Mini Review

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