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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 on observations in other transportation modes, interviews, or surveys about the hypothetical situation. In this paper, we close this gap by presenting real-world insights into the attitude towards highly automated driving and non-driving-related activities (NDRAs). Using a Wizard of Oz AV, we conducted a real-world driving study (N = 12) with six rides per participant during multiple days. We provide insights into the users’ perceptions and behavior. We found that (1) the users’ trust a human driver more than a system, (2) safety is the main acceptance factor, and (3) the most popular NDRAs were being idle and the use of the smartphone.

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SAIAD 2021

[Paper] Towards Black-Box Explainability with Gaussian Discriminant Knowledge Distillation

In this paper, we propose a method for post-hoc explainability of black-box models. The key component of the semantic and quantitative local explanation is a knowledge distillation (KD) process which is used to mimic the […]

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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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