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Auflistung nach Autor:in "Braun, Michael"

1 - 3 von 3
  • Workshopbeitrag
    9thWorkshop Automotive HMIs: Natural and Adaptive UIs to Support Future Vehicles
    (Mensch und Computer 2021 - Workshopband, 2021) Riener, Andreas; Pfleging, Bastian; Detjen, Henrik; Braun, Michael; Peintner, Jakob
    Modern vehicles allow control by the driver with multimodal user interfaces (UIs), touch interaction on screens, speech input, and mid-air gestures. Such UIs are driver-focused and optimized for limited distraction to not compromise road safety in manual driving. Nevertheless, they are often complex and it might be difficult to find specific features. Automated driving in L3+ will disrupt the design of automotive UIs as drivers become passengers, at least for certain parts along the way. Similarly, the car is being transformed into a social space where passengers can be granted control over systems because they can devote their full attention without imposing safety risks. The complexity of advanced driver assistance, in-vehicle information and interaction systems requires explanation to the user, e.g., in which state the system is, interaction possibilities, expectations from the driver or take over timing. We expect novel technologies to allow for natural interaction and adaptivity to design valuable and future-proof interaction concepts for the changing interior of (automated) vehicles. The goal of this workshop is, thus, to discuss how natural and adaptive user interfaces can help to solve the mentioned challenges and to identify opportunities for future research and collaboration.
  • Dissertation
    Affective automotive user interfaces
    (2020) Braun, Michael
    Technological progress in the fields of ubiquitous sensing and machine learning has been fueling the development of user-aware human-computer interaction in recent years. Especially natural user interfaces, like digital voice assistants, can benefit from understanding their users in order to provide a more naturalistic experience. Such systems can, for example, detect the emotional state of users and accordingly act in an empathic way. One major research field working on this topic is Affective Computing, where psycho-physiological measures, speech input, and facial expressions are used to sense human emotions. Affective data allows natural user interfaces to respond to emotions, providing promising perspectives not only for user experience design but also for safety aspects. In automotive environments, informed estimations of the driver’s state can potentially avoid dangerous errors and evoking positive emotions can improve the experience of driving. This dissertation explores Affective Automotive User Interfaces using two basic interaction paradigms: firstly, emotion regulation systems react to the current emotional state of the user based on live sensing data, allowing for quick interventions. Secondly, emotional interaction synthesizes experiences which resonate with the user on an emotional level. The constituted goals of these two interaction approaches are the promotion of safe behavior and an improvement of user experience. Promoting safe behavior through emotion regulation: Systems which detect and react to the driver’s state are expected to have great potential for improving road safety. This work presents a model and methods needed to investigate such systems and an exploration of several approaches to keep the driver in a safe state. The presented methods include techniques to induce emotions and to sample the emotional state of drivers. Three driving simulator studies investigate the impacts of emotionaware interventions in the form of implicit cues, visual mirroring and empathic speech synthesis. We envision emotion-awareness as a safety feature which can detect if a driver is unfit or in need of support, based on the propagation of robust emotion detection technology. Improving user experience with emotional interaction: Emotional perception is an essential part of user experience. This thesis entails methods to build emotional experiences derived from a variety of lab and simulator studies, expert feedback, car-storming sessions and design thinking workshops. Systems capable of adapting to the user’s preferences and traits in order to create an emotionally satisfactory user experience do not require the input of emotion detection. They rather create value through general knowledge about the user by adapting the output they generate. During this research, cultural and generational influences became evident, which have to be considered when implementing affective automotive user interfaces in future cars. We argue that the future of user-aware interaction lies in adapting not only to the driver’s preferences and settings but also to their current state. This paves the way for the regulation of safe behavior, especially in safety-critical environments like cars, and an improvement of the driving experience.
  • Workshopbeitrag
    Emotions in the Age of Automated Driving - Developing Use Cases for Empathic Cars
    (Mensch und Computer 2019 - Workshopband, 2019) Oehl, Michael; Ihme, Klas; Bosch, Esther; Pape, Anna-Antonia; Vukelić, Mathias; Braun, Michael
    Improving user experience of highly automated vehicles is key to increase their acceptance. One possibility to realize this is the design of empathic cars that are capable of assessing the emotional state of vehicle occupants and react to it accordingly by providing tailored support. At the moment, the central challenge is to derive relevant use cases as basis for the design of future empathic cars. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective computing, affective interfaces and automated driving as forum for the development of a roadmap towards empathic vehicles using design thinking methods. During the workshop, we will gain a common understanding of the central concepts and listen to impulse talks about current and recent projects on emotions during automated driving. Based on this, relevant use cases are generated in group work and discussed with the goal to identify potential research and knowledge gaps. Finally, a road map for research towards the realization of automated empathic cars is formulated from the results.