Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Designing User Interfaces for Automated Driving: A Simulator Study on Individual Information Preferences

2024 , Driesen Micklitz, Tim , Fellmann, Michael , Röcker, Carsten

Automated Driving (AD) can free users from driving and create time for their disposal. However, since manufacturers increasingly target a wide customer range with AD, such systems and their User Interfaces (UI) must accommodate different user characteristics and preferences. This paper aims to analyze the effects of individual characteristics on the information preferences in UIs for surrounding road infrastructure (for instance, lane markings or traffic signs) and system limits describing hindering factors for AD (for instance, construction sites or unsuitable weather conditions). To do so, we performed a driving simulator study with 43 participants. Results show that users with a more positive attitude towards technology prefer more infrastructure information. Furthermore, users familiar with Automatic Cruise Control prefer less system limit information, while higher experience with Steering Assists relates to higher preference in this regard. These findings add concrete mechanisms to the theory of personalized AD UIs and inform product development on how to create more personalized user experiences. By this, we aim to address challenges regarding the acceptance, adoption, and usage of AD.

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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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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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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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