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[Paper] “Help, Accident Ahead!”: Using Mixed Reality Environments in Automated Vehicles to Support Occupants After Passive Accident Experiences

Currently, car assistant systems mainly try to prevent accidents. Increasing built-in car technology also extends the potential applications in vehicles. Future cars might have virtual windshields that augment the traffic or individual virtual assistants interacting with the user. In this paper, we explore the potential of an assistant system that helps the car’s occupants to calm down and reduce stress when they experience an accident in front of them. We present requirements from a discussion (N = 11) and derive a system design from them. Further, we test the system design in a video-based simulator study (N = 43). Our results indicate that an accident support system increases perceived control and trust and helps to calm down the user.

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[Paper] A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles

Understanding user needs and behavior in automated vehicles (AVs) while traveling is essential for future in-vehicle interface and service design. Since AVs are not yet market-ready, current knowledge about AV use and perception is based […]

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[Paper] Synergiepotenziale von Virtual City Twins im Bereich automatisiertes Fahren – Beschleunigung der technischen Entwicklung und Überwindung von Akzeptanzbarrieren

Bei der Entwicklung hin zu einer automatisierten und vernetzten Mobilität wird in der (breiteren) Öffentlichkeit oft der Eindruck vermittelt, als sei die Technik bereits so weit fortgeschritten, dass eine problemlose Umsetzung kurzfristig möglich sei. Aktuell […]

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[Paper] Multivariate Confidence Calibration for Object Detection

Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object […]

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