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In automated driving it is important to ensure drivers’ awareness of the currently active level of automation and to support transitions between those levels. This is possible with <tex-math notation="LaTeX">a </tex-math> suitable human-machine interface (HMI). In this driving simulator study, two visual HMI concepts (Concept <tex-math notation="LaTeX">A </tex-math> and <tex-math notation="LaTeX">B </tex-math> ) were compared with <tex-math notation="LaTeX">a </tex-math> baseline for informing drivers about three modes: manual driving, assisted driving, and automated driving. The HMIs, consisting of LED strips on the steering wheel that differed in luminance, color, and pattern, provided continuous information about the active mode and announced transitions. The assisted mode was conveyed in Concept <tex-math notation="LaTeX">A </tex-math> using <tex-math notation="LaTeX">a </tex-math> combination of amber and blue LEDs, while in Concept <tex-math notation="LaTeX">B </tex-math> only amber LEDs were used. During automated driving Concept <tex-math notation="LaTeX">A </tex-math> displayed blue LEDs and Concept B, turquoise. Both concepts were compared to <tex-math notation="LaTeX">a </tex-math> baseline HMI, with no LEDs. Thirty-eight drivers with driving licence were trained and participated. Objective measures (hands-on-wheel time, takeover time, and visual attention) are reported. Self-reported measures (mode awareness, trust, user experience, and user acceptance) from <tex-math notation="LaTeX">a </tex-math> previous publication are briefly repeated in this context (Muthumani et al.). Concept <tex-math notation="LaTeX">A </tex-math> showed 200 ms faster hands-on-wheel times than the baseline, while in Concept <tex-math notation="LaTeX">B </tex-math> several outliers were observed that prevented significance. The visual HMIs with LEDs did not influence the eyes-on-road time in any of the automation levels. Participants preferred Concept B, with more prominent differentiation between the automation levels, over Concept A.
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Diederichs et al. (2022) studied this question.
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