Today's social media (SM) platforms are toolkits consisting of features with different use cases, some strongly related to habitual and regretful use. Especially Infinite Scrolling (IS) has been reported to make users feel like they are being caught in a loop, regretfully elongating SM sessions. We investigated and defined this loop while unveiling the processes that make users break it. Based on a one-week-long field study (N=46), we unfolded and categorized general reasons for leaving social media and related those to IS. In light of our findings, we argue that SM interventions should not only focus on the app but incorporate the user's context, as most reasons to break SM sessions were not related to the app but the user's general context. Our findings and prior work also indicate the coexistence of multiple loops, which we define as inner (intra-session) loops surrounded by an outer (habitual) loop.

Automotive user interface (AUI) evaluation becomes increasingly complex due to novel interaction modalities, driving automation, heterogeneous data, and dynamic environmental contexts. Immersive analytics may enable efficient explorations of the resulting multilayered interplay between humans, vehicles, and the environment. However, no such tool exists for the automotive domain. With AutoVis, we address this gap by combining a non-immersive desktop with a virtual reality view enabling mixed-immersive analysis of AUIs. We identify design requirements based on an analysis of AUI research and domain expert interviews (N=5). AutoVis supports analyzing passenger behavior, physiology, spatial interaction, and events in a replicated study environment using avatars, trajectories, and heatmaps. We apply context portals and driving-path events as automotive-specific visualizations. To validate AutoVis against real-world analysis tasks, we implemented a prototype, conducted heuristic walkthroughs using authentic data from a case study and public datasets, and leveraged a real vehicle in the analysis process.

External communication of automated vehicles is proposed to replace driver-pedestrian communication in ambiguous crossing situations. So far, research has focused on simpler scenarios with one attentive pedestrian and one automated vehicle. This virtual reality study (N=115) investigates a more complex scenario with other crossing pedestrians, a distracting task on the smartphone, and external communication by the automated vehicle. Interaction effects were found for crossing duration, gaze behavior, and subjective measures. For attentive pedestrians, the external communication resulted in shorter crossing durations, higher perceived safety, as well as lower perceived criticality, cognitive workload, and effort. These positive effects were not found when pedestrians were distracted. Instead, distracted pedestrians benefited from other crossing pedestrians because they looked less at the stopping vehicle, felt safer, perceived the situation as less critical, and reported lower cognitive workload and effort. Pedestrians initiated crossings earlier with a group or external communication and later with a smartphone.

External Human-Machine Interfaces (eHMIs) have been evaluated to facilitate interactions between Automated Vehicles (AVs) and pedestrians. Most eHMIs are, however, visual/ light-based solutions, and multi-modal eHMIs have received little attention to date. We ran an experimental video study (N = 29) to systematically understand the effect on pedestrian’s willingness to cross the road and user preferences of a light-based eHMI (light bar on the bumper) and two sound-based eHMIs (bell sound and droning sound), and combinations thereof. We found no objective change in pedestrians’ willingness to cross the road based on the nature of eHMI, although people expressed different subjective preferences for the different ways an eHMI may communicate, and sometimes even strong dislike for multi-modal eHMIs. This shows that the modality of the evaluated eHMI concepts had relatively little impact on their effectiveness. Consequently, this lays an important groundwork for accessibility considerations of future eHMIs, and points towards the insight that provisions can be made for taking user preferences into account without compromising effectiveness.

Automated vehicles will alter traffic fundamentally. While users can engage in non-driving-related tasks such as reading or even sleeping, the possibility to interact with other road users such as pedestrians via, for example, eye contact vanishes. Therefore, external communication of automated vehicles is currently researched with various concepts spanning dimensions such as anthropomorphism, technology, viewpoint, locus, message type, and others. However, the proposed concepts are mostly evaluated in simple scenarios, such as one person trying to cross in front of one automated vehicle. Therefore, we implemented a WebGL application of a four-lane road and conducted a within-subject study (N=46) to study the effects of nine concepts with and without the presence of other pedestrians and altering the yielding target of the automated vehicle. We found that all concepts were rated better than having no external communication. However, the effects were not uniform across the concepts.