You're making things AR-kward: Exploring Augmented Reality In-the-WildEven though recent technological advancements have led to a growing interest in AR HMD, their actual deployment in everyday, outdoor and on-the-move applications remains uncertain. Most AR research is conducted in controlled laboratory settings, leaving a gap in our understanding of AR's potentials and complexities in real-world environments. This paper explores the social aspects surrounding AR use in public spaces, by investigating the use of AR HMD during two distinct use cases: shopping at a farmers market and outdoor presentations. Based on the analysis of observations and interviews with participants, passersby and vendors, we explore social impact of AR and share methodological and technological insights. We contribute seven lessons learned for researchers conducting AR studies in-the-wild. Our findings show that the current understanding of non-users should be revisited for in-the-wild AR studies. Furthermore, current AR HMD lack social components, inducing awkwardness in social situations, which might fade with continuous exposure.2025HSHelen Stefanidi et al.AR Navigation & Context AwarenessImmersion & Presence ResearchSocial Platform Design & User BehaviorMobileHCI
Augmented Reality on the Move: A Systematic Literature Review for Vulnerable Road UsersDue to the continuous improvement of Augmented Reality (AR) head-mounted displays (HMDs), these devices are bound to be increasingly integrated into our daily routines. So far, a major focus of AR research has been on indoor usage and deployment. However, since seamlessly supporting users in their activities while being on-the-move in various outdoor contexts becomes increasingly important, there is a need to investigate the current state-of-the-art of AR technologies while people are in motion outdoors. Therefore, we conducted a systematic literature review of pertinent HCI publications, specifically looking into applications concerning vulnerable road users. We identify the contexts in which such technologies have been researched, prevailing challenges in the field, and applied methodological approaches. Our findings show that most contributions address pedestrians, a shift towards HMDs, and a prevalence of lab studies due to technology limitations. Based on our findings, we discuss trends, existing gaps and opportunities for future research.2024HSHelen Stefanidi et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsAR Navigation & Context AwarenessMobileHCI
“Be with me and stay with me”: Insights from Co-Designing a Digital Companion to Support Patients Transitioning from Hospital to Cardiac RehabilitationCardiovascular diseases (CVDs) are a major global health challenge, compounded by a significant systemic treatment gap: the underutilization of cardiac rehabilitation (CR). Central to this issue is the crucial yet underperforming referral process to CR due to various factors, including patients’ lack of information. By co-designing a CR referral assistant with cardiac patients and healthcare professionals, this work explores how technology can support patient journeys from the acute hospital to CR, overcoming existing healthcare system barriers. This work (1) contributes a map of patients’ evolving needs tailored to their pathway, (2) provides design implications for interactive digital health technologies aiming to facilitate patient transitions across established healthcare system boundaries, and (3) discusses the potential of technology as a "patient companion" during their transition to CR.2024IHIsabel Höppchen et al.Mental Health Apps & Online Support CommunitiesTelemedicine & Remote Patient MonitoringRobots in Education & HealthcareDIS
Mode Awareness Interfaces in Automated Vehicles, Robotics, and Aviation: A Literature ReviewWith increasing automation capabilities and a push towards full automation in vehicles, mode awareness, i.e., the driver's awareness of the vehicle's current automation mode, becomes an important factor. While issues surrounding mode awareness are known, research concerning and design towards mode awareness appears to not yet be a focal point in the automated driving domain. In this paper, we provide a state-of-the art on mode awareness from the related domains of automated driving, aviation, and Human-Robot Interaction. We present a summary of existing mode awareness interface solutions as well as existing techniques and recognized gaps concerning mode awareness. We found that existing interfaces are often simple, sometimes outdated, yet are difficult to meaningfully expand without overloading the user. We also found predictive approaches as a promising strategy to lessen the need for mode awareness via separate indicators.2021YÖYasemin Dönmez Özkan et al.Automated Driving Interface & Takeover DesignAI-Assisted Decision-Making & AutomationAutoUI
Stop or Go? Let me Know! A Field Study on Visual External Communication for Automated ShuttlesIn mixed traffic environments, highly automated vehicles (HAV) can potentially be disruptive and a source of hazards due to their non-human driving behavior and a lack of ``traditional'' communication means (gestures, eye contact, and similar) to resolve issues or otherwise unclear situations. As a result, additional external human-machine interfaces (eHMI) for automated vehicles that replace the now absent human element in communication have been proposed. In this paper, we present the results from a study, in which two versions of a light band eHMI to communicate driving intend of an automated shuttle were evaluated in a real driving environment. We found that the green-red traffic light metaphor and simple animations could improve interaction success in certain aspects. We also found and discuss that the effect of using vs. not using the visual eHMIs was overall lower than expected and that the shuttle's position and observable driving behavior seemed to play a larger role than anticipated.2021AMAlexander G. Mirnig et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsAutoUI
Chase Lights in the Peripheral View: How the Design of Moving Patterns on an LED Strip Influences the Perception of Speed in an Automotive ContextLEDs on a strip, when turned on and off in a specific order, result in the perception of apparent motion (i.e. beta movement). In the automotive domain such chase lights have been used to alter drivers' perception of driving speed by manipulating the pixel speed of LEDs. We argue that the perceived velocity of beta movement in the peripheral view is not only based on the actual pixel speed but can be influenced by other factors such as frequency, width and brightness of lit LED segments. We conducted a velocity matching experiment (N=25) by systematically varying these three properties, in order to determine their influence on a participant's perceived velocity in a vehicle mock-up. Results show that a higher frequency and stronger brightness increased perceived velocity, whereas segment width had no influence. We discuss how findings may be applied when designing systems that use beta movement to influence the perception of ambient light velocity.2020AMAlexander Meschtscherjakov et al.University of SalzburgHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)In-Vehicle Haptic, Audio & Multimodal FeedbackCHI
Trolled by the Trolley Problem: On What Matters for Ethical Decision Making in Automated VehiclesAutomated vehicles have to make decisions, such as driving maneuvers or rerouting, based on environment data and decision algorithms. There is a question whether ethical aspects should be considered in these algorithms. When all available decisions within a situation have fatal consequences, this leads to a dilemma. Contemporary discourse surrounding this issue is dominated by the trolley problem, a specific version of such a dilemma. Based on an outline of its origins, we discuss the trolley problem and its viability to help solve the questions regarding ethical decision making in automated vehicles. We show that the trolley problem serves several important functions but is an ill-suited benchmark for the success or failure of an automated algorithm. We argue that research and design should focus on avoiding trolley-like problems at all rather than trying to solve an unsolvable dilemma and discuss alternative approaches on how to feasibly address ethical issues in automated agents.2019AMAlexander G. Mirnig et al.University of SalzburgAutomated Driving Interface & Takeover DesignAI Ethics, Fairness & AccountabilityCHI
"Where Does It Go?" - a Study on Visual On-Screen Designs for Exit Management in an Automated Shuttle BusRiding a highly automated bus has the potential to bring about a set of novel challenges for the passenger. As there is no human driver present, there is no one to talk to regarding driving direction, stops, or delays. This lack of a human element is likely to cause a stronger reliance on the in-vehicle means of communication, such as displays. In this paper, we present the results from a qualitative study, in which we tested three different on-screen visualizations for passenger information during an automated bus trip. The designs focused primarily on signaling the next stop and proper time to request the bus to stop in absence of a human driver. We found that adding geo-spatial details can easily confuse more than help and that the absence of a human driver makes passengers feel more insecure about being able to exit at the right stop. Thus, passengers are less receptive for visual cues signaling upcoming stops and more likely to input stop requests immediately upon leaving the station.2019AMAlexander G. Mirnig et al.Motion Sickness & Passenger ExperienceSocial & Collaborative VRAutoUI
Advanced Driver Assistance Systems for Aging Drivers - Insights on 65+ Drivers' Acceptance of and Intention to Use ADASAdvanced Driver Assistance Systems (ADAS) aim to increase safety by supporting drivers in the driving task. Especially older drivers (65+ years), given the nature of aging, could benefit from these systems. However, little is known about older drivers' acceptance of ADAS in general and how particular acceptance aspects influence their intention to use such systems. To address this research gap, we present results from a large-scale online survey (n=1328) with aging drivers, which was conducted in three European countries in 2019. We identified several demographic and driving-related variables, which are significantly related to acceptance. Furthermore, we found that older drivers' intention to use ADAS is most strongly predicted by favorable acceptance aspects (i.e., usefulness, reassurance, and trust), while unfavorable aspects (i.e., annoyance, irritation, and stress) were found to have less to none predictive power. The findings are discussed considering future research directions in this area.2019HBHanna Braun et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AutoUI
The Insurer's Paradox: About Liability, the Need for Accident Data, and Legal Hurdles for Automated DrivingIn light of recent incidents, it has become increasingly relevant to determine who is responsible in case of accidents involving automated vehicles. In this paper, we investigate the question of liability in automated vehicles of SAE levels 3 and above. We claim that there is a mismatch between current liability practices, where a designated driver is usually held responsible, and future perspectives, where the human assumes more and more a passive passenger-like role. Our claims are supported by the results from an interview study with insurance companies from two European countries. We show that insurers lack sufficient data to make informed decisions on how to apportion liability in SAE level 3+ scenarios. We discuss how these considerations have to be reflected in interfaces for the driver in order to make the legal status transparent for the driver.2019AMAlexander G. Mirnig et al.Automated Driving Interface & Takeover DesignPrivacy by Design & User ControlAutoUI
Follow Me: Exploring Strategies and Challenges for Collaborative DrivingCurrent research on Vehicle-to-Vehicle (V2V) communication aims at improving interaction between different vehicles by communication technologies and is mainly focused on driver-to-driver interaction. But how do drivers and passengers of two vehicles that have the same destination communicate with each other? In such a collaborative driving scenario, several factors such as the environmental context or the behavior of the vehicle occupants may influence the communication. In order to explore how information is exchanged in collaborative driving, we conducted an exploratory in-situ study with seven groups of two driver/co-driver pairs each, located in two separate vehicles. During the ride, the participants had to drive collaboratively on a predefined route solving different subtasks. We found that different social (e.g., driving habits, unpredicted intentions) and contextual factors (e.g., night/rain conditions, size or color of the vehicle) influenced collaboration. Our findings provide a deeper understanding of collaborative driving and inform future V2V communication designs.2018NPNicole Perterer et al.V2X (Vehicle-to-Everything) Communication DesignAutoUI
Acceptance Factors for Future Workplaces in Highly Automated TrucksOnce highly automated vehicles become available, drivers will be freed to perform activities other than driving when automated driving mode is activated. Such activities could include relaxing, reading, exercising, or working. The work of professional drivers such as truck drivers can be expected to be especially affected by this technology and to change accordingly as highly automated trucks enable the completion of other working related tasks during automated driving. But will such mobile working places be accepted by truck drivers? In this paper, we report on a survey (N=23) assessing technology acceptance towards Future Workplaces in highly Automated Trucks (FWAT). Results show that a majority of drivers is rather skeptical about FWAT, but that acceptance can be expected to strongly vary. Our paper provides some guidance on how to explain this variance and presents relevant acceptance factors for future FWAT usage.2018PFPeter Fröhlich et al.Automated Driving Interface & Takeover DesignImpact of Automation on WorkAutoUI
Interacting with Autonomous Vehicles: Learning from other DomainsThe rise of evermore autonomy in vehicles and the expected introduction of self-driving cars have led to a focus on human interactions with such systems from an HCI perspective over the last years. Automotive User Interface researchers have been investigating issues such as transition control procedures, shared control, (over)trust, and overall user experience in automated vehicles. Now, it is time to open the research field of automated driving to other CHI research fields, such as Human-Robot-Interaction (HRI), aeronautics and space, conversational agents, or smart devices. These communities have been dealing with the interplay between humans and automated systems for more than 30 years. In this workshop, we aim to provide a forum to discuss what can be learnt from other domains for the design of autonomous vehicles. Interaction design problems that occur in these domains, such as transition control procedures, how to build trust in the system, and ethics will be discussed.2018AMAlexander Meschtscherjakov et al.University of SalzburgAutomated Driving Interface & Takeover DesignAI Ethics, Fairness & AccountabilityHuman-Robot Collaboration (HRC)CHI
Automotive User Interfaces: Expert DiscussionAutomation is making significant advances in vehicles, with adaptive cruise control and lane keeping assistance being prominent technologies we encounter on the road today. How should we design user interactions for vehicles with automation? Panelists will lead the audience in discussions about (a) how to design interactions for driving-related and non-driving-related activities; (b) how the designs are affected by the availability of different types of vehicle automation, and how their effectiveness can be tested, (c) how we can approach the designs from the perspective of vehicle occupants, as well as from the perspective of other traffic participants, and (d) how to guide not only practice but also theory development about human-machine interaction for automated vehicles.2018SBSusanne Boll et al.University of OldenburgAutomated Driving Interface & Takeover DesignAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesCHI