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Developing a Highly Automated Driving Scenario to Investigate User Intervention: When Things Go Wrong

MPS-Authors
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Faltaous,  S
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/persons84065

Machulla,  T
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Multisensory Perception 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

Faltaous, S., Machulla, T., Baumann, M., & Chuang, L. (2017). Developing a Highly Automated Driving Scenario to Investigate User Intervention: When Things Go Wrong. In Adjunct Proceedings (pp. 67-71). New York, NY, USA: ACM Press.


Cite as: https://hdl.handle.net/21.11116/0000-0000-C38D-5
Abstract
Current levels of vehicle automation (i.e., SAE-L2) require users to be vigilant and to intervene when automated vehicles fail to perform appropriately. In this work, we developed a scenario for investigating how humans respond, in the absence of notifications for system failure. In order to develop better notifications to elicit user intervention, it is necessary to first understand how humans would intervene, even without the aid of in-vehicle notifications. We provide a description of how this is implemented in a driving simulator using Unity, a game engine. In addition, we report preliminary results. Overall, we found that participants were more aroused and cautious under conditions of low environment visibility, even though visibility had no bearing on the likelihood of vehicle automation to fail. We present recommendations for how the current scenario could be improved for subsequent research.