Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    How to Indicate AI at Work on Vehicle Dashboards: Analysis and Empirical Study
    ( 2024)
    Rössger, Peter
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    Acevedo, Cristián
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    Bottesch, Miriam
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    Nau, Samuel
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    Stricker, Tobias
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    Diederichs, Frederik
    The KARLI project aims to create an adaptive AI system for future vehicles. It’s focusing on motion sickness, level-compliant driver behavior, and AI-HMI (artificial intelligence human-machine Interface). The project explores making AI activities visible through avatars, aiming to enhance user experiences and empower users to understand and influence AI decisions for a positive interaction with technology. AI representations in HMIs range from non-representational to realistic, introducing a classification that includes "HMI-integrated." The analysis explores AI representations in vehicle HMIs, citing Nio's Nomi and Waymo's ride service as examples. AI depictions in films, ranging from abstract (HAL 9000) to realistic (Ava from "Ex Machina"), are examined. The KARLI project aims to differentiate itself by explicitly representing AI activity on screens in non-fictional and automotive contexts. Pros and cons of different levels of abstraction in AI avatars are made. A study predominantly involving females and younger individuals, showing a positive attitude toward AI was conducted. Three design variants of the avatar were tested in a comparative laboratory study. All tested designs received negative Net Promoter Scores, with the abstract figurative design rated the best and the figurative design the creepiest. All designs scored low on "Intention to Use," indicating participants’ reluctance, and "Product Loyalty" echoed this sentiment. A final design was created based on the results of analysis and study.
  • 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.