A Hidden Markov Framework to Capture Human-Machine Interaction in Automated 
Vehicles

Janssen, CP; Boyle, LN; Kun, AL; Ju, W; Chuang, LL

doi:10.1080/10447318.2018.1561789

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A Hidden Markov Framework to Capture Human-Machine Interaction in Automated Vehicles

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Chuang, LL
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Janssen, C., Boyle, L., Kun, A., Ju, W., & Chuang, L. (2019). A Hidden Markov Framework to Capture Human-Machine Interaction in Automated Vehicles. International Journal of Human–Computer Interaction, 35(11), 947-955. doi:10.1080/10447318.2018.1561789.

Zitierlink: https://hdl.handle.net/21.11116/0000-0001-B66F-6

Zusammenfassung

A Hidden Markov Model framework is introduced to formalize the beliefs that humans may have about the mode in which a semi-automated vehicle is operating. Previous research has identified various “levels of automation,” which serve to clarify the different degrees of a vehicle’s automation capabilities and expected operator involvement. However, a vehicle that is designed to perform at a certain level of automation can actually operate across different modes of automation within its designated level, and its operational mode might also change over time. Confusion can arise when the user fails to understand the mode of automation that is in operation at any given time, and this potential for confusion is not captured in models that simply identify levels of automation. In contrast, the Hidden Markov Model framework provides a systematic and formal specification of mode confusion due to incorrect user beliefs. The framework aligns with theory and practice in various interdisciplinary approaches to the field of vehicle automation. Therefore, it contributes to the principled design and evaluation of automated systems and future transportation systems.