Wheeler: A Three-Wheeled Input Device for Usable, Efficient, and Versatile Non-Visual InteractionBlind users rely on keyboards and assistive technologies like screen readers to interact with user interface (UI) elements. In modern applications with complex UI hierarchies, navigating to different UI elements poses a significant accessibility challenge. Users must listen to screen reader audio descriptions and press relevant keyboard keys one at a time. This paper introduces Wheeler, a novel three-wheeled, mouse-shaped stationary input device, to address this issue. Informed by participatory sessions, Wheeler enables blind users to navigate up to three hierarchical levels in an app independently using three wheels instead of navigating just one level at a time using a keyboard. The three wheels also offer versatility, allowing users to repurpose them for other tasks, such as 2D cursor manipulation. A study with 12 blind users indicates a significant reduction (40%) in navigation time compared to using a keyboard. Further, a diary study with our blind co-author highlights Wheeler's additional benefits, such as accessing UI elements with partial metadata and facilitating mixed-ability collaboration.2024MIMd Touhidul Islam et al.Vibrotactile Feedback & Skin StimulationHaptic WearablesVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)UIST
Text a Bit Longer or Drive Now? Resuming Driving after Texting in Conditionally Automated CarsIn this study, we focus on different strategies drivers use in terms of interleaving between driving and non-driving related tasks (NDRT) while taking back control from automated driving. We conducted two driving simulator experiments to examine how different cognitive demands of texting, priorities, and takeover time budgets affect drivers' takeover strategies. We also evaluated how different takeover strategies affect takeover performance. We found that the choice of takeover strategy was influenced by the priority and takeover time budget but not by the cognitive demand of the NDRT. The takeover strategy did not have any effect on takeover quality or NDRT engagement but influenced takeover timing.2024NCNabil Al Nahin Ch et al.Automated Driving Interface & Takeover DesignAutoUI
AI-Augmented Brainwriting: Investigating the use of LLMs in group ideationThe growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process – the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.2024OSOrit Shaer et al.Wellesley CollegeGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCHI
Perceptions of Trucking Automation: Insights from the r/Truckers CommunityRecent technological advancements in automation have sparked interest in how automation will affect truck drivers and the trucking industry. However, there is a gap in the literature addressing how truck drivers perceive automation and how they believe it will impact trucking. This study aims to understand truck drivers’ perspectives on automation in the trucking industry. Extending a preliminary study, we conducted a broader analysis of comments discussing automation in the r/Truckers subreddit from February 2017 to March 2021. In general, the community had negative sentiments towards automation in the trucking industry. Participants speculated when automation would become mainstream in trucking and discussed the feasibility of automation in the context of executing non-driving tasks and having accommodating infrastructure. Our findings indicate that truck drivers seek to participate in conversations about the future and to prepare themselves for when automation is more prominent in the trucking industry.2021LOLisa Orii et al.Impact of Automation on WorkAutoUI
How Will Drivers Take Back Control in Automated Vehicles? A Driving Simulator Test of an Interleaving FrameworkWe explore the transfer of control from an automated vehicle to the driver. Based on data from N=19 participants who participated in a driving simulator experiment, we find evidence that the transfer of control often does not take place in one step. In other words, when the automated system requests the transfer of control back to the driver, the driver often does not simply stop the non-driving task. Rather, the transfer unfolds as a process of interleaving the non-driving and driving tasks. We also find that the process is moderated by the length of time available for the transfer of control: interleaving is more likely when more time is available. Our interface designs for automated vehicles must take these results into account so as to allow drivers to safely take back control from automation.2021DNDivyabharathi Nagaraju et al.Automated Driving Interface & Takeover DesignAutoUI
Exploring the Concept of the (Future) Mobile OfficeThis video shows a concept of a future mobile office in a semi-automated vehicle that uses augmented reality. People perform non-driving tasks in current, non-automated vehicles even though that is unsafe. Moreover, even for passengers there is limited space, it is not social, and there can be motion sickness. In future cars, technology such as augmented reality might alleviate some of these issues. Our concept shows how augmented reality can project a remote conversant onto the dashboard. Thereby, the driver can keep an occasional eye on the road while the automated vehicle drives, and might experience less motion sickness. Potentially, this concept might even be used for group calls or for group activities such as karaoke, thereby creating a social setting. We also demonstrate how integration with an intelligent assistant (through speech and gesture analysis) might save the driver from having to grab a calendar to write things down, again allowing them to focus on the road.2019CJChristian P. Janssen et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Motion Sickness & Passenger ExperienceAR Navigation & Context AwarenessAutoUI
Camera-View Augmented Reality: Overlaying Navigation Instructions on a Real-Time View of the RoadAugmented reality navigation aids have been investigated in a number of studies, and results are encouraging, especially for large, head-up displays. However, such displays are not commercially available – in fact they are rare in laboratories as well. In this paper we ask: would drivers be well-served with a navigation aid that overlays AR content on a live feed from a camera that shows the forward road? To answer this question we conducted a simulator-based study and compared the use of such a navigation aid to the use of a head-up AR aid, as well as to the use of 2D map shown on a head-down display. Our results confirm prior results that a head-up AR navigation aid can keep drivers’ visual attention on the road, and that drivers like such a navigation aid. Our results also indicate that a camera-view AR navigation aid might not be well-received by drivers.2018SSS. Tarek Shahriar et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AR Navigation & Context AwarenessAutoUI
Human-Machine Interaction for Vehicles: Review and OutlookToday’s vehicles have myriad user interfaces, from those related to the moment-to-moment control of the vehicle, to those that allow the consumption of information and entertainment. The bulk of the work in this domain in the recent past and the present is related to manual driving. In exploring human-machine interaction for manual driving a key issue has been assessing the effects of in-vehicle interfaces on driving safety. Very frequently this is done in the context of an application, such as navigation, entertainment, or communication. With recent advances in automated vehicles, there is an increased attention to user interactions as they relate to creating a place for work and play during a trip. Given that it is unlikely that most vehicles will be fully automated in the near future, there are also significant efforts to understand how to help the driver switch between different modes of automation. This paper provides a review of these areas of research, as well as recommendations for future work.2018AKAndrew L KunUniversity of New HampshireAutomated Driving Interface & Takeover DesignTeleoperated DrivingCHI
Understanding Collaborative Decision Making Around a Large-Scale Interactive TabletopWe present findings from an empirical study of how groups of eight users collaborate on a decision-making task around an interactive tabletop. To our knowledge, this is the first study to examine co-located collaboration in larger groups (of 8-12 users) seated around a large-scale high-resolution multi-touch horizontal display. Our findings shed light on: 1) the effect of collaboration patterns of larger groups on equity of participation; 2) the role of participants’ position around the tabletop in forming collaborations; and 3) the mechanisms, which facilitate coordination and collaboration in larger group interacting around large-scale tabletops; We also contribute computational methods that leverage image processing to analyze interaction around large-scale tabletops. Finally, we discuss implications for the design of large-scale tabletop systems for supporting co-located collaboration in larger groups.2018LWLauren Westendorf et al.Meetings and Decision MakingCSCW
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