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  4. Improving Driver Performance and Experience in Assisted and Automated Driving with Visual Cues in the Steering Wheel
 
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2022
Journal Article
Title

Improving Driver Performance and Experience in Assisted and Automated Driving with Visual Cues in the Steering Wheel

Abstract
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 a suitable human-machine interface (HMI). In this driving simulator study, two visual HMI concepts (Concept A and B ) were compared with a 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 A using a combination of amber and blue LEDs, while in Concept B only amber LEDs were used. During automated driving Concept A displayed blue LEDs and Concept B, turquoise. Both concepts were compared to a 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 a previous publication are briefly repeated in this context (Muthumani et al.). Concept A showed 200 ms faster hands-on-wheel times than the baseline, while in Concept B 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.
Author(s)
Diederichs, Frederik  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Muthumani, Arun
Feierle, Alexander
Galle, Melanie
Mathis, Lesley-Ann  
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Bopp-Bertenbreiter, Anja Valeria  
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Widlroither, Harald  
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Bengler, Klaus
Technische Universität München  
Journal
IEEE transactions on intelligent transportation systems  
Project(s)
Adaptive ADAS to support incapacitated drivers Mitigate Effectively risks through tailor made HMI under automation  
Funding(s)
H2020  
Funder
European Commission  
DOI
10.1109/TITS.2022.3162522
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Keyword(s)
  • Automated driving

  • human-machine interface

  • mode awareness

  • steering wheel

  • takeover

  • visual warning

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