Die SAGAT-Methode zur Erfassung von Situationsbewusstsein im Fahrkontext

Rauch, Nadja; Gradenegger, B.; Krüger, Hans‐Peter

doi:10.1007/978-3-531-90949-3_10

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Central to that measurement technique is the optional execution of the secondary task. When something unusual happens, a declined task and the duration of the processing phase have proven to be valid measures for situation awareness [6], [28]. In the current study, only the last tasks offered just before the dangerous situations are used for measuring the situation awareness.…”

Section: Dependent Variablesmentioning

confidence: 99%

“…Rauch [6] developed a measurement technique that uses the subject's situation-adaptive usage of a secondary task in order to implicitly measure situation awareness. Central to that measurement technique is the optional execution of the secondary task.…”

Section: Dependent Variablesmentioning

confidence: 99%

“…Should the driver fail to navigate to the correct menu-element within 15 seconds, the current task is declared as "cancelled", otherwise as "finished". Corresponding to the method of Rauch [6], the participants were told to focus on their main task of supervising the traffic situation and the automation system.…”

Section: A Test Designmentioning

confidence: 99%

“…It follows the scheme developed by Rauch [6] in order to measure situation awareness. The menu is displayed in the left part of the central display and is operated via a touch-screen.…”

Section: A Test Designmentioning

confidence: 99%

“…Consequently, one can speak of three levels of situation awareness (perception, comprehension and projection). Automation can have negative influences on situation awareness, as the operator is taken "out of the loop" [5], [6]. In case of reaching system boundaries or in the event of system errors, a lack of situation awareness can lead to late, wrong or even missing counterreactions by the operator [7], [8].…”

Section: Introductionmentioning

confidence: 98%

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Effects of human-machine interaction mechanisms on situation awareness in partly automated driving

Wulf

Zeeb

Rimini-Doring

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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In recent years, one of the goals of driver assistance systems has been to relieve the driver of parts of his burden of driving. Current developments are able to take over full control of the vehicle in specific use cases. Automated driving in traffic jams is seen as the next step towards automated driving. One of the main challenges is ensuring the driver's ability to take back responsibility when being driven by the car. The assumption that drivers tend to perform secondary tasks creates a target conflict.Within this context, it is the aim of the current study to analyze the impacts of an exemplary secondary task and certain Human-Machine Interaction (HMI) mechanisms on the driver's level of situation awareness and the resulting driving safety. The implemented HMI mechanisms are a "driver's safety device" and a "video image". While it is hypothesized that the secondary task has a negative influence on situation awareness, the HMI mechanism's effect is supposed to be positive. In addition, the overall user's acceptance will also be analyzed.The results show that the implemented driver's safety device has no effect on situation awareness but is still able to support driving safety by assisting the driver in the execution of actions. The video image has a positive effect on situation awareness but because the complacency-effect is enhanced its effect on driving safety is negligible. As assumed, the secondary task has a negative impact on situation awareness and driving safety.

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Section: Dependent Variablesmentioning

confidence: 99%

Section: Dependent Variablesmentioning

confidence: 99%

Section: A Test Designmentioning

confidence: 99%

“…It follows the scheme developed by Rauch [6] in order to measure situation awareness. The menu is displayed in the left part of the central display and is operated via a touch-screen.…”

Section: A Test Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 3 more Smart Citations

Effects of human-machine interaction mechanisms on situation awareness in partly automated driving

Wulf

Zeeb

Rimini-Doring

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Cooperation Between Vehicle and Driver: Predicting the Driver’s Takeover Capability in Cooperative Automated Driving Based on Orientation Patterns

Herzberger,

Usai,

Schwalm

et al. 2024

Cooperatively Interacting Vehicles

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This chapter first describes central development steps of cooperative vehicle control before focusing on the cooperation within the vehicle, between driver and co-system. To enable smooth transitions within this internal cooperation, both agents (driver and co-system) need a mutual understanding of the current capabilities for safely executing the driving task. For this purpose, first the model of confidence horizons is briefly introduced, which represents these mutual capability assessments. In the following, the focus of this chapter is on the assessment of the driver’s ability to take over. First, the state of the art of Driver State Monitoring Systems (DSMS) as well as current challenges are presented. Here it is shown that a prediction based purely on driver observation is not yet possible. Therefore, an alternative approach, the diagnostic takeover request (TOR), is presented, which predicts the takeover capability based on the driver’s initial orientation reaction. In the following, two driving simulator studies are presented in which the diagnostic TOR was used for the first time and thereafter the results are presented and discussed. Finally, a brief outlook is given on how both the diagnostic TOR and the concept of confidence horizons will be further developed.

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