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Sicherheitskritische Mensch-Computer-Interaktion ist nicht nur derzeit, sondern auch zukünftig ein äußerst relevantes Thema. Hierbei kann ein Lehr- und Fachbuch, wie dieses, immer nur einen punktuellen Stand abdecken. Dennoch kann der Versuch unternommen werden, aktuelle Trends zu identifizieren und einen Ausblick in die Zukunft zu wagen. Genau das möchte dieses Kapitel erreichen: Es sollen zukünftige Entwicklungen vorausgesagt und versucht werden, diese korrekt einzuordnen. Das ist an dieser Stelle nicht nur durch den Herausgeber, sondern durch Abfrage bei zahlreichen am Lehrbuch beteiligten Autoren geschehen. Neben einem Ausblick auf Grundlagen und Methoden werden dementsprechend auch sicherheitskritische interaktive Systeme und sicherheitskritische kooperative Systeme abgedeckt.
Automotive user interfaces and automated vehicle technology pose numerous challenges to support all diverse facets of user needs. These range from inexperienced, thrill-seeking, young novice drivers to elderly drivers with a mostly opposite set of preferences together with their natural limitations. To allow assessing the (hedonic) quality of automotive user interfaces and automated driving technology (i. e., UX) already during development, the proposed workshop is dedicated to the quest of finding objective, quantifiable criteria to describe future driving experiences. The workshop is intended for HCI, AutomotiveUI, and “Human Factors” researchers and practitioners as well for designers and developers. In adherence to the conference main topic “Interaktion – Verbindet – Alle”, this workshop calls in particular for contributions in the areas of human factors and ergonomics (user acceptance, trust, user experience, driving fun, natural user interfaces, etc.) with focus on hedonic quality and design of user experience to enhance the safety feeling in ADS.
Automotive user interfaces and, in particular, automated vehicle technology pose a plenty of challenges to researchers, vehicle manufacturers, and third-party suppliers to support all diverse facets of user needs. To give an example, they emerge from the variation of different usergroups ranging from inexperienced, thrill-seeking young novice drivers to elderly drivers with all their natural limitations. To allow assessing the quality of automotive user interfaces and automated driving technology already during development and within virtual test processes, the proposed workshop is dedicated to the quest of finding objective, quantifiable quality criteria for describing future driving experiences. The workshop is intended for HCI, AutomotiveUI, and “Human Factors" researchers and practitioners as well for designers and developers. In adherence to the conference main topic “Spielend einfach interagieren “, this workshop calls in particular for contributions in the area of human factors and ergonomics (user acceptance, trust, user experience, driving fun, natural user interfaces, etc.) and artificial intelligence (predictive HMIs, adaptive systems, intuitive interaction).
In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.