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Feel the Movement: Real Motion Influences Responses to Take-over Requests in Highly Automated Vehicles

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Bülthoff,  HH
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83861

Chuang,  L
Project group: Cognition & Control in Human-Machine Systems, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Borojeni, S., Boll, S., Heuten, W., Bülthoff, H., & Chuang, L. (2018). Feel the Movement: Real Motion Influences Responses to Take-over Requests in Highly Automated Vehicles. In G. Mandryk, M. Hancock, M. Perry, & A. Cox (Eds.), 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13). New York, NY, USA: ACM Press.


Cite as: http://hdl.handle.net/21.11116/0000-0001-7D54-5
Abstract
Take-over requests (TORs) in highly automated vehicles are cues that prompt users to resume control. TORs however, are often evaluated in non-moving driving simulators. This ignores the role of motion, an important source of information for users who have their eyes off the road while engaged in non-driving related tasks. We ran a user study in a moving-base driving simulator to investigate the effect of motion on TOR responses. We found that with motion, user responses to TORs vary depending on the road context where TORs are issued. While previous work showed that participants are fast to respond to urgent cues, we show that this is true only when TORs are presented on straight roads. Urgent cues issued on curved roads elicit slower responses than non-urgent cues on curved roads. Our findings indicate that TORs should be designed to be aware of road context to accommodate natural user responses.