Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    PersonalAIzation - Exploring concepts and guidelines for AI-driven personalization of in-car HMIs in fully automated vehicles
    ( 2022)
    Sundar, Shrivaas Madapusi
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    Bopp-Bertenbreiter, Valeria
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    Ziegler, Daniel orcid-logo
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    Kosuru, Ravi Kanth
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    Knecht, Christian
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    Pfleging, Bastian
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    Widlroither, Harald
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    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.
  • Publication
    Artificial Intelligence for Adaptive, Responsive, and Level-Compliant Interaction in the Vehicle of the Future (KARLI)
    ( 2022)
    Diederichs, Frederik
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    Wannemacher, Christoph
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    Faller, Fabian
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    Schmidt, Eike
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    Engelhardt, Doreen
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    Mikolajewski, Martin
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    Rittger, Lena
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    Voit, Michael
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    Widlroither, Harald
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    Martin, Manuel
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    Hashemi, Vahid
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    Sahakyan, Manya
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    Romanelli, Massimo
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    Kiefer, Bernd
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    Fäßler, Victor
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    Rößler, Tobias
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    Großerüschkamp, Marc
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    Kurbos, Andreas
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    Bottesch, Miriam
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    Immoor, Pia
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    Engeln, Arnd
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    Fleischmann, Marlis
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    Schweiker, Miriam
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    Pagenkopf, Anne
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    Daniela Piechnik
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    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.