Automated vehicles (AVs) reached technological maturity and will soon arrive on streets as traffic participants.Human traffic participants such as drivers, pedestrians, or cyclists will be increasingly confronted with the presence of AVs within their environment, not necessarily knowing or understanding what to expect and how to interact with them.Although AVs are designed to act safely, effective interaction in mixed traffic scenarios will depend on successful communication, interaction, or even negotiation beyond static rules and regulations.Prosocial behavior, such as yielding one's right of way, will be needed to resolve unclear traffic situations or foster traffic flow.However, what are the characteristics of such prosocial behavior, and how to measure this not only for automated vehicles

Road traffic is a social context where interactions between road users influence their emotional experiences and behavior.However, with the advancement of driving automation, traditional forms of human-to-human social interaction in traffic will be challenged.While previous research has explored new forms of communication, such as eHMIs for mixed traffic, less is known about the influential factors in decision-making for such communications and their experiential outcomes.To address this gap, we conducted a scoping review of 114 studies and proposed the Social Traffic Interaction Framework (STIF), which describes social interaction among road users across five phases: situational assessment, decision, action, acknowledgment, and affective outcome.To assess the real-world applicability of the STIF framework, 25 traffic scenarios were collected through interviews and analyzed according to the framework.Our findings provide a theoretical foundation for understanding and designing social interaction in mixed traffic that promotes safer, more empathetic, and emotionally positive experiences.

Promoting sustainable mobility requires technological innovation and changes in individual travel behavior. Using the Theory of Planned Behavior, we examined how attitudes, norms, and perceived control shape the willingness to adopt alternatives to car use. We designed 38 future commuting scenarios, each of which isolated a single dimension across three mobility concepts: public transportation, cycling, and shared automated vehicles. In an online survey (N = 168), participants rated their willingness to switch modes and pay more. To deepen our understanding, we conducted follow-up interviews (N = 10), exploring their everyday mobility practices and their likes and dislikes regarding the practicality of the future scenarios. Our findings show that features linked to instrumental attitudes and control beliefs elicit stronger intentions than affective cues, ecological appeals were less persuasive. We argue that effective behavior change depends on linking motivational factors to the realities of everyday mobility contexts.

The proliferation of (semi-)autonomous and learning interactive systems (i.e., artificial intelligence-driven, AI) will significantly change work practices. While AI promises an immense increase in performance, it also has novel experiential, social, cultural, and ethical implications. Unlike conventional systems, AI's potential agency and decision-making capabilities will turn AI-driven systems into counterparts and, at best, collaborators. We used a speculative, participatory approach to collect narratives about current and future work practices with AI, their conditions, and consequences. This informed ''WorkAI'', a card deck and a canvas to support the design of AI-driven work. The twenty cards encourage participants to apply important aspects such as reliability/trust or meaning to collaboration with AI. The toolkit was developed in two iterations. It provided structure to the discussion and stimulated questions that facilitated the development of future narratives about working with AI.

Work is an important part of our lives - not only as a way to earn a living but as a crucial source for experiencing meaningfulness. The introduction of autonomous systems (or in the widest sense "artificial intelligence", AI) will fundamentally impact work practices. However, while most existing models of human-AI collaboration focus on performance goals, less is known about their potential influence on job satisfaction. In this paper, we present an online experiment in which we compared the perception of job meaningfulness and accountability in a human-AI collaboration across three interaction paradigms: Supervisory, Advisory, and Interactive. Our results showed that, unlike the common notion of supervisory control, people find their job more satisfying when they directly interact with the AI and are involved and remain accountable for action and decision-making. Introducing AI as a teammate in the interactive paradigm was associated with the highest job meaningfulness.

Automated vehicles (AVs) are expected to encounter various ambiguous space-sharing conflicts in urban traffic. Bottleneck scenarios, where one of the parts needs to resolve the conflict by yielding priority to the other, could be utilized as a representative ambiguous scenario to understand human behavior in experimental settings. We conducted a controlled field experiment with a Wizard of Oz automated car in a bottleneck scenario. 24 participants attended the study by driving their own cars. They made yielding, or priority-taking decisions based on implicit and explicit locomotion cues on AV realized with an external display. Results indicate that acceleration and deceleration cues affected participants’ driving choices and their perception regarding the social behavior of AV, which further serve as ecological validation of related simulation studies.

Dying is a universal experience that entails uncertainty, loss, and termination. Often, people face death unprepared and miss out on opportunities to shape their final stage of life as well as their afterlife. To better understand how thanato-technology can support the dying and the bereaved, we performed a scoping review on the current state-of-art in Human Computer Interaction. Following the PRISMA-ScR procedure, we gathered and analyzed 107 relevant papers. We categorized theoretical and conceptual contributions into three overarching themes: digital remains, remembrance, and coping. We further highlight 18 practices, such as curation, honoring and letting go. We show that technology can help to capture the identity of the deceased, to validate the life lived, and to come to terms with death. However, available approaches focus more on the bereaved than on the dying. In addition, potentially important aspects of dying (e.g., balancing involvement and autonomy, spiritual meaning-making) remain largely unexplored.

The advance of “artificial intelligence”-(AI)-based technologies has the potential to transform work tremendously. Work is a major part of life and, thus, its meaningfulness or lack thereof will impact overall well-being. Previous research investigated human-AI collaboration at work mostly with a focus on performance. However, little attention is given to how collaboration with AI influences the meaningfulness of work and job satisfaction. In this paper, we present an online experiment to compare the perception of meaningfulness and relationship to the collaborator across different task distributions and collaborators (human/AI). Our results show that working with a human is more motivating and meaningful compared to working with an AI independent of the task. Moreover, the AI is more often viewed as a subordinate, while the human is perceived as a teammate. These results provide preliminary implications for the design of collaboration with AI that consider job satisfaction.

Studies on imminent Take-Over Requests (TORs) in automated driving have mainly addressed safety aspects rather than user experience (UX). In this study, we investigated the fulfillment of user needs during SAE L3 driving on a test track. Participants engaged in non-driving related tasks (NDRTs; using a smartphone or the auditory modality) had to respond to critical TORs to prevent an accident. Our results, based on qualitative methods, show that participants expect L3 vehicles to be safe and confirmed this assessment after the test track experience. Furthermore, participants preferred NDRTs using the auditory modality over the smartphone to maintain situation awareness. Our study indicates that drivers may behave responsibly in L3 vehicles, provided they are supported with user interfaces that fulfill their psychological needs.

Soon, voice assistants might be able to engage in fully-fledged social conversations with people, rather than merely providing a voice-operated interface to functionality and data. So far, not much is known about designing such "social" voice assistants and the potential social experiences, which could and should emerge in everyday situations. In the present paper, we created a design fiction to explore a sophisticated social voice assistant in the context of the car. Based on models from psychology and psychotherapy, we designed the fictional "virtual passenger" Kiro. We created a website for Kiro (http://www.heykiro.com/), distributed it, and collected responses in various ways (e.g., comments). We further ran a market research-type focus group. In general, we found people to accept Kiro as a conversation partner but not as a replacement for human-human conversations. We suggest designing social voice assistants in a way to enable novel types of socially fulfilling, yet distinct human-machine conversations.

Serious applications in virtual reality (VR) often imitate real environments, tasks, and interaction techniques. VR however, allows to transcend reality and augment the physical, cognitive, and perceptual capabilities of users through, for example, telekinesis. In this sense, VR offers “superpowers”, which are not always considered as central and unique elements of interaction design. In a workshop with interaction designers, we emphasized the notion of “superpowers” to think less about VR limitations to imitate reality, but more about its possibilities to augment human capabilities. Consequently, we developed a design method that explicitly introduced the concept of superpowers (e.g., X-Ray-Vision, super-memory, telekinesis) and a VR application to support exploring each superpower. We evaluated this approach with designers. They responded positively. The notion of superpowers combined with the possibility to easily experience them changed and widened their perspective: away from limitations and reality, towards possibilities and the unique experience of being in VR.

Road traffic is a social situation where participants heavily interact with each other. Consequently, communication plays an important role. Typically, the communication between pedestrians and drivers is nonverbal and consists of a combination of gestures, eye contact, and body movement. However, when vehicles become automated, this will change. Previous work has investigated the design and effectiveness of additional communication cues between pedestrians and automated vehicles. It remains unclear, though, how this impacts the perceptions of the quality of communication and impressions of mindfulness and prosociality. In this paper, we report an online experiment, where we evaluated the perception of communication cues in the form of on-road light projections, across different traffic scenarios and roles. Our results indicate that, while the cues can improve communication, their effect is dependent on traffic scenarios. These results provide preliminary implications for the design of communication cues that consider their prosocial aspects.

While the general debate about the potential of automated vehicles is pervasive, less is known about how people experience those vehicles in everyday life. To this end, we studied the experiential consequences of a speculative automated "valet parking" service. We designed three variants and confronted participants "in the wild." The automation replaced practical worries about how to navigate a parking garage with unease about the safety of the car, which clearly limited potential gains in positive experiences. The unease could be counteracted by providing feedback about the car itself and the process. This created the impression that the car is properly cared for. In addition, while parking in garages in itself was not especially enjoyable, it played an important role in a number of related positive practices (e.g., shopping routines). To ensure acceptance, automated systems need to carefully address the question of how those systems become embedded in everyday life.

Take-over requests (TORs) in highly automated vehicles are cues that prompt users to resume control. TORs however, are often evaluated in non-moving driving simulators. This ignores the role of motion, an important source of information for users who have their eyes off the road while engaged in non-driving related tasks. We ran a user study in a moving-base driving simulator to investigate the effect of motion on TOR responses. We found that with motion, user responses to TORs vary depending on the road context where TORs are issued. While previous work showed that participants are fast to respond to urgent cues, we show that this is true only when TORs are presented on straight roads. Urgent cues issued on curved roads elicit slower responses than non-urgent cues on curved roads. Our findings indicate that TORs should be designed to be aware of road context to accommodate natural user responses.

Highly automated vehicles, occasionally require users to resume vehicle control from non-driving related tasks by issuing cues called take-over request (TOR). Due to being engaged in non-driving related tasks (NDRT), users have a decreased level of situational awareness of the driving context. Therefore, user interface designs for TORs should ensure smooth transitions from the NDRTs to vehicle control. In this paper, we investigated the role of decision priming cues as TORs across different levels of NDRT engagement. In a driving simulator, users performed a reading span task while driving in automated mode. They received audio-visual TORs which primed them with an appropriate maneuver (steering vs. braking), depending on the traffic situation. Our results showed that priming users with upcoming maneuvers results in faster responses and longer time to collision to obstacles. However, the level of engagement in NDRT does not affect user responses to TORs.

Child cyclists are often at greater risk for traffic accidents. This is in part due to the development of children's motor and perceptual-motor abilities. To facilitate road safety for children, we explore the use of multimodal warning signals to increase their awareness and prime action in critical situations. We developed a bicycle simulator instrumented with these signals and conducted two controlled experiments. We found that participants spent significantly more time perceiving visual than auditory or vibrotactile cues. Unimodal signals were the easiest to recognize and suitable for encoding directional cues. However, when priming stop actions, reaction time was shorter when all three modalities were used simultaneously. We discuss the implications of these outcomes with regard to design of safety systems for children and their perceptual-motor learning.

Automation shortcomings in highly automated driving (level 3) might require the driver to take over vehicle control. When the vehicle issues a take-over request (TOR), the driver has to immediately comprehend the situation, which highly demands visual attention. Therefore, haptic channel has the potential to be used for conveying information about driving context at take-over to assist decision making. In this work, we introduce the concept and prototype design of a shape-changing steering wheel which conveys contextual information at take-over. We evaluated this concept in comparison with an identical wheel with vibration cues. Results showed that haptic cues on steering wheel at TOR reassure drivers of their decisions rather than assisting them in decision making, and overall workload ratings are decreased using vibration cues. To assist decision making, contextual haptic cues in level 3 should be located on-body or on drivers' seat.

Take-over situations in highly automated driving occur when drivers have to take over vehicle control due to automation shortcomings. Due to high visual processing demand of the driving task and time limitation of a take-over maneuver, appropriate user interface designs for take-over requests (TOR) are needed. In this paper, we propose applying ambient TORs, which address the peripheral vision of a driver. Conducting an experiment in a driving simulator, we tested a) ambient displays as TORs, b) whether contextual information could be conveyed through ambient TORs, and c) if the presentation pattern (static, moving) of the contextual TORs has an effect on take-over behavior. Results showed that conveying contextual information through ambient displays led to shorter reaction times and longer times to collision without increasing the workload. The presentation pattern however, did not have an effect on take-over performance.

Truck platooning has advantages for fuel consumption, road safety, and use of the existing road infrastructure. However, it does not simplify the job of truck drivers who reportedly fear driving in close distance and mistrust the automation. Prior work indicates that a platooning assistant system can help overcoming these issues. However, there are no practical experiences with on-the-road platooning that shed light on how drivers perceive and evaluate such a system. In this work, we close this gap and present PlatoonPal, a platooning assistance system which we rigorously developed in a user centered way. We further present the results of a field test with three platooning trucks where drivers used PlatoonPal under real conditions. The results show that drivers are positive using the system and preferred information related closely to the current situation. We conclude the paper with guidelines for the future development of assistance systems for platooning.