Artificial Intelligence (AI) is increasingly integrated into clinical practice, but its influence on patient decision-making, particularly when AI and physicians disagree, remains unclear. To examine collective advice, we investigated a breast cancer screening scenario using (1) a qualitative interview study (N=9) and (2) a quantitative experiment (N=339) where participants received either consistent or conflicting biopsy recommendations. Qualitative findings include the need for empathetic care, the importance of patient autonomy, and a desire for a four-eyes principle. Quantitative findings accordingly show that patients generally trust physicians more than AI but still tend to follow AI recommendations due to risk aversion. When both advised a biopsy, 99% adhered; if both advised against it, 25% still proceeded. In conflicting scenarios, 97% followed the physician's advice, whereas 66% followed the AI if it recommended the biopsy. These results underscore the need for careful interaction design of collective healthcare advice to prevent unnecessary healthcare procedures.

In automated vehicles, it is essential to feedforward motion intentions to users so that they understand the vehicle’s actions. Otherwise, non-transparency limits situation awareness and leads to mistrust. In this work, we are communicating the vehicle’s actions to the user either by displaying icons (planar HUD) or through augmented reality (contact-analog HUD) to increase transparency. We developed both concepts in a user-centered design process. Further, we evaluated them in two subsequent user studies (N = 27). In the first study, we focused on UX and trust in higher automation levels (cf. SAE level 3-5). In the second study, we focused on safety and error prevention in lower automation levels (cf. SAE levels 1-2). Our results show that both visualizations increase UX and trust in an automated system. Nevertheless, the AR approach outperforms the icon-based approach by achieving higher user experience as well as faster and less error-prone take-overs of participants.

Automated vehicles (AVs) are on the edge of being available on the mass market. Research often focuses on technical aspects of automation, such as computer vision, sensing, or artificial intelligence. Nevertheless, researchers also identified several challenges from a human perspective that need to be considered for a successful introduction of these technologies. In this paper, we first analyze human needs and system acceptance in the context of AVs. Then, based on a literature review, we provide a summary of current research on in-car driver-vehicle interaction and related human factor issues. This work helps researchers, designers, and practitioners to get an overview of the current state of the art.

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.

Self-driving cars will relief the human from the driving task. Nevertheless, the human might want to intervene in the driving process and thus needs the possibility to control the car. Switching back to fully manual controls is uncomfortable once being passive and engaging in non-driving-related activities. A more comfortable way is controlling the car with elemental maneuvers (e.g., "turn left" or "stop"). Whereas touch interaction concepts exist, contactless interaction through voice and mid-air gestures has not yet been explored for maneuver-based car control. In this paper, we, therefore, compare the general eligibility of voice and mid-air gesture with touch interaction as the primary maneuver selection mechanism in a driving simulator study. Our results show high usability for all modalities. Contactless interaction leads to a more positive emotional perception of the interaction, yet mid-air gestures lead to higher task load. Overall, voice and touch control are preferred over mid-air gestures by most users.

The uprising levels of autonomous vehicles allow the drivers to shift their attention to non-driving tasks while driving (i.e., texting, reading, or watching movies). However, these systems are prone to failure and, thus, depending on human intervention becomes crucial in critical situations. In this work, we propose using human actuation as a new mean of communicating take-over requests (TOR) through proprioception. We conducted a user study via a driving simulation in the presence of a complex working memory span task. We communicated TORs through four different modalities, namely, vibrotactile, audio, visual, and proprioception. Our results show that the vibrotactile condition yielded the fastest reaction time followed by proprioception. Additionally, proprioceptive cues resulted in the second best performance of the non-driving task following auditory cues.

In this demo paper we present a new visualization technique for dynamic networks. It displays the time slices of the dynamic network using two dimensional graph layouting algorithms and stacks these in the third dimension to show the development over time. The visualization ensures that the same node always has the same position in each time slice so that it is easy to follow its development. It also allows filtering data and influencing node appearance based on properties. Additionally we offer a two dimensional comparison view for two time slices which highlights changes in graph structure and (if available) in measures of nodes. The presented visualization technique is implemented using web technology and is available in a web-based analytics workbench. We demonstrate the benefits of these techniques by an analysis of a data set from a learning community.