We present a system for 3D hand gesture recognition based on low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. Our system fuses data coming from two ToF sensors which is used to build up a large database and subsequently train a multilayer perceptron (MLP). We demonstrate that we are able to reliably classify a set of ten hand gestures in real-time and describe the setup of the system, the utilised methods as well as possible application scenarios.

Misconceptions of vehicle automation functionalities lead to either non-use or dangerous misuse of assistant systems, harming the users’ experience by reducing potential comfort or compromise safety. Thus, users must understand how and when to use an assistant system. In a preliminary online survey, we examined the use, trust, and the perceived understanding of modern vehicle assistant systems. Despite remaining incomprehensibility (36–64%), experienced misunderstandings (up to 9%), and the need for training (around 30%), users reported high trust in the systems. In the following study with first-time users, we examine the effect of different User Onboarding approaches for an automated parking assistant system in a Tesla and compare the traditional text-based manual with a multimodal augmented reality (AR) smartphone application in means of user acceptance, UX, trust, understanding, and task performance. While the User Onboarding experience for both approaches shows high pragmatic quality, the hedonic quality was perceived significantly higher in AR. For the automated parking process, reported hedonic and pragmatic user experience, trust, automation understanding, and acceptance do not differ, yet the observed task performance was higher in the AR condition. Overall, AR might help motivate proper User Onboarding and better communicate how to operate the system for inexperienced users.

Mission- and safety-critical domains are more and more characterized by interactive and multimedia systems varying from large-scale technologies (e. g. airplanes) to wearable devices (e. g. smartglasses) operated by professional staff or volunteering laypeople. While technical availability, reliability and security of computer-based systems are of utmost importance, outcomes and performances increasingly depend on sufficient human-machine interaction or even cooperation to a large extent. While this i-com Special Issue on “Human-Machine Interaction and Cooperation in Safety-Critical Systems” presents recent research results from specific application domains like aviation, automotive, crisis management and healthcare, this introductory paper outlines the diversity of users, technologies and interaction or cooperation models involved.

Deeply engaging nurses in a participatory co-design process, especially in times of COVID-19, is challenging. In this case study, we shed light on the process of developing a prototype for AR-glasses in nursing. We show the challenges we faced, the methods we used and how they contribute to the core principles of participatory design. A special focus is laid on small-scale interventions with high-impact, that helped us to truly engage users. We introduce empathetic ways to connect contrasting work environments, establish mutual understanding, make the abstract more graspable with playful tools like PLAYMOBIL®, and support co-design development with online formats. Finally, we discuss the transferability to other projects.