Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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

2022 , Diederichs, Frederik , Muthumani, Arun , Feierle, Alexander , Galle, Melanie , Mathis, Lesley-Ann , Bopp-Bertenbreiter, Anja Valeria , Widlroither, Harald , Bengler, Klaus

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.

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PersonalAIzation - Exploring concepts and guidelines for AI-driven personalization of in-car HMIs in fully automated vehicles

2022 , Sundar, Shrivaas Madapusi , Bopp-Bertenbreiter, Valeria , Ziegler, Daniel , Kosuru, Ravi Kanth , Knecht, Christian , Pfleging, Bastian , Widlroither, Harald , Diederichs, Frederik

The role of the driver changes to that of a passenger in autonomous cars. Thus, the vehicle interior transforms from a cockpit into a multimedia station and workspace. This work explores concepts for Artificial Intelligence (AI) to provide a personalized user experience for the passengers in the form of Contextual Personalized Shortcuts and Personalized Services in the infotainment system. The two use cases were iteratively developed based on literature research and surveys. We evaluated AI- Personalized Services and compared AI-generated to the manually configurable shortcuts. AttrakDiff (Hassenzahl et al., 2003) and Car Technology Acceptance Model (CTAM; Osswald et al., 2012) were used to evaluate UX and user acceptance. The AI-Personalized interface obtained positive scores and reactions in the user testing and shows potential. Based on the insight from the user studies and literature review, we present and human-AI interaction guidelines to build effective AI-personalized HMIs.

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Artificial Intelligence for Adaptive, Responsive, and Level-Compliant Interaction in the Vehicle of the Future (KARLI)

2022 , Diederichs, Frederik , Wannemacher, Christoph , Faller, Fabian , Schmidt, Eike , Engelhardt, Doreen , Mikolajewski, Martin , Rittger, Lena , Voit, Michael , Widlroither, Harald , Martin, Manuel , Hashemi, Vahid , Sahakyan, Manya , Romanelli, Massimo , Kiefer, Bernd , Fäßler, Victor , Rößler, Tobias , Großerüschkamp, Marc , Kurbos, Andreas , Bottesch, Miriam , Immoor, Pia , Engeln, Arnd , Fleischmann, Marlis , Schweiker, Miriam , Pagenkopf, Anne , Daniela Piechnik , Mathis, Lesley-Ann

The KARLI project consortium investigates and develops monitoring systems for drivers and other occupants with new artificial intelligence approaches, based on high quality labeled data that is collected in real vehicles. The project’s target applications are integrated in vehicles that enable various levels of automation and transitions of control. Level-compliant occupant behavior is assessed with AI algorithms and modulated with responsive and adaptive human machine interface (HMI) solutions. The project also targets the prediction and prevention of motion sickness in order to improve the user experience, enabling productivity and maintaining an adequate driver state. The user-centered approach is represented by defining five KARLI User Roles which specify the driving related behavior requirements for all levels of automation. The project results will be evaluated at the end of the project. The KARLI applications will be evaluated regarding user experience benefits and AI performance measures. The KARLI project is approaching two main challenges that are ambitious and have a high potential: First, raising and investigating the potential of AI for driver monitoring and driver-vehicle interaction, and second, accelerating the transfer from research to series production applications.

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