Understanding How Mobile Interactions Shape Grasp and Contact Patterns Beyond the TouchscreenThe way users hold a smartphone depends on the interaction task, yet little is known about the fingers' engagement with the device's surfaces beyond the touchscreen. Such an understanding not only opens up opportunities for novel on- and off-screen interactions, but also the device’s possible physical affordances. We present a study (N=23) that examines the hands' physical engagement with the smartphone beyond the touchscreen across nine mobile interactions. Grasps were annotated from photographs, and contact regions were captured using residual heat traces from grasping the device. Our findings show that fingers and palms adopt a variety of support roles and postures when engaging with the smartphone's back and side edges. The hand-contact maps reveal distinct patterns, differing in contact frequency and placement. This work contributes an empirical characterisation of hands' back and edge engagement, highlighting design opportunities for future smartphone usage extending beyond the touchscreen.2026CSCarolin Stellmacher et al.University of BremenOne-Handed Operation & Mobile GesturesTouch Target Selection & PointingCHI
Improving Low-Vision Chart Accessibility via On-Cursor Visual ContextDespite widespread use, charts remain largely inaccessible for Low-Vision Individuals (LVI). Reading charts requires viewing data points within a global context, which is difficult for LVI who may rely on magnification or experience a partial field of vision. We aim to improve exploration by providing visual access to critical context. To inform this, we conducted a formative study with five LVI. We identified four fundamental contextual elements common across chart types: axes, legend, grid lines, and the overview. We propose two pointer-based interaction methods to provide this context: Dynamic Context, a novel focus+context interaction, and Mini-map, which adapts overview+detail principles for LVI. In a study with N=22 LVI, we compared both methods and evaluated their integration to current tools. Our results show that Dynamic Context had significant positive impact on access, usability, and effort reduction; however, worsened visual load. Mini-map strengthened spatial understanding, but was less preferred for this task. We offer design insights to guide the development of future systems that support LVI with visual context while balancing visual load.2026YSYotam Sechayk et al.The University of TokyoVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Interactive Data VisualizationUncertainty VisualizationCHI
Exploring the Impacts of Background Noise on Auditory Stimuli of Audio-Visual eHMIs for Hearing, Deaf, and Hard-of-Hearing PeopleExternal Human-Machine Interfaces (eHMIs) have been proposed to enhance communication between automated vehicles (AVs) and pedestrians, with growing interest in multi-modal designs such as audio-visual eHMIs. Just as poor lighting can impair visual cues, a loud background noise may mask the auditory stimuli. However, its effects within these systems have not been examined, and little is known about how pedestrians --- particularly Deaf and Hard-of-Hearing (DHH) people --- perceive different types of auditory stimuli. We conducted a virtual reality study (Hearing N=25, DHH N=11) to examine the effects of background noise (quiet and loud) on auditory stimuli (baseline, bell, speech) within an audio-visual eHMI. Results revealed that: (1) Crossing experiences of DHH pedestrians significantly differ from Hearing pedestrians. (2) Loud background noise adversely affects pedestrians' crossing experiences. (3) Providing an additional auditory eHMI (bell/speech) improves crossing experiences. We outlined four practical implications for future eHMI design and research.2026WXWenge Xu et al.Birmingham City UniversityExternal HMI (eHMI) — Communication with Pedestrians & CyclistsAudio Accessibility (Captions, Sign Language, Vibration)CHI
Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCIField studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.2026XGXinyue Gui et al.The University of TokyoAutomated Driving Interface & Takeover DesignExternal HMI (eHMI) — Communication with Pedestrians & CyclistsUser Research Methods (Interviews, Surveys, Observation)CHI
Don't Worry, Just Follow Me: Prototyping and In-the-Wild Evaluation of Smart Pole Interaction Unit with MobilityPedestrian–automated vehicle(AV) encounters in shared spaces often involve hesitation and ambiguity. Vehicle-mounted external human–machine interfaces(eHMIs) can help, but obscured or poorly timed communications create significant challenges. To address this, we present a mobile smart pole interaction unit(SPIU) with integrated cameras and LED displays, designed as a pedestrian-side system to deliver explicit cues(``WALK,'' ``STOP''). An in-the-wild evaluation of the SPIU(N=21) using a four-factor analysis (CarBehavior, Mobility, eHMI, SPIU) showed that the SPIU improved understandability, trust, and perceived safety, and reduced workload compared with the baseline, with a combination(eHMI+SPIU) yielding the strongest results. Beyond these quantitative benefits, participants appreciated the mobility of the SPIU for its ``clear'' and ``easy to decide'' mediation. This work contributes to(1) a design and deployment framework for a mobile SPIU and(2) an in-the-wild evaluation protocol for pedestrian–AV interactions in nonsignalized spaces. Our work sparks discussions on real world evaluations involving detailed vehicle kinematics and accessible multimodality(e.g., audio), focusing on the role of personal robots as user-side eHMIs.2026VCVishal Chauhan et al.The University of TokyoExternal HMI (eHMI) — Communication with Pedestrians & CyclistsSocial Robot InteractionTeleoperation & TelepresenceCHI
MIRAGE: Enabling Real-Time Automotive Mediated RealityTraffic is inherently dangerous, with around 1.19 million fatalities annually. Automotive Mediated Reality (AMR) can enhance driving safety by overlaying critical information (e.g., outlines, icons, text) on key objects to improve awareness, altering objects' appearance to simplify traffic situations, and diminishing their appearance to minimize distractions. However, real-world AMR evaluation remains limited due to technical challenges. To fill this sim-to-real gap, we present MIRAGE, an open-source tool that enables real-time AMR in real vehicles. MIRAGE implements 15 effects across the AMR spectrum of augmented, diminished, and modified reality using state-of-the-art computational models for object detection and segmentation, depth estimation, and inpainting. In an on-road expert user study (N=9) of MIRAGE, participants enjoyed the AMR experience while pointing out technical limitations and identifying use cases for AMR. We discuss these results in relation to prior work and outline implications for AMR ethics and interaction design.2026PJPascal Jansen et al.Ulm UniversityAutomated Driving Interface & Takeover DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)In-Vehicle Haptic, Audio & Multimodal FeedbackCHI
GTA: Generative Traffic Agents for Simulating Realistic Mobility BehaviorPeople's transportation choices reflect complex trade-offs shaped by personal preferences, social norms, and technology acceptance. Predicting such behavior at scale is a critical challenge with major implications for urban planning and sustainable transport. Traditional methods use handcrafted assumptions and costly data collection, making them impractical for early-stage evaluations of new technologies or policies. We introduce Generative Traffic Agents (GTA) for simulating large-scale, context-sensitive transportation choices using LLM-powered, persona-based agents. GTA generates artificial populations from census-based sociodemographic data. It simulates activity schedules and mode choices, enabling scalable, human-like simulations without handcrafted rules. We evaluate GTA in Berlin-scale experiments, comparing simulation results against empirical data. While agents replicate patterns, such as modal split by socioeconomic status, they show systematic biases in trip length and mode preference. GTA offers new opportunities for modeling how future innovations, from bike lanes to transit apps, shape mobility decisions.2026SLSimon Lämmer et al.ScaDS.AI, Leipzig UniversityV2X (Vehicle-to-Everything) Communication DesignGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCHI
ProVoice: Designing Proactive Functionality for In-Vehicle Conversational Assistants using Multi-Objective Bayesian Optimization to Enhance Driver ExperienceThe next step for In-vehicle Conversational Assistants (IVCAs) will be their capability to initiate and automate proactive system interactions throughout journeys. However, diverse drivers make it challenging to design voice interventions tailored towards individual on-road expectations. This paper evaluates the effectiveness of Human-in-the-Loop (HITL) Multi-Objective Bayesian Optimization (MOBO) in design by implementing ProVoice: a Virtual Reality (VR) driving simulator integrating MOBO to investigate the effects of IVCA design variants on perceived mental demand, predictability, and usefulness. By reporting the Pareto Front from a within-subjects VR study (N=19), this paper proposes optimal design trade-offs. Follow-up analysis demonstrates MOBO’s success in discovering effective intervention strategies, with reduced participant mental demand, alongside enhanced predictability and usefulness while engaging with the proactive IVCA. Implications for computational techniques in future research on proactive intervention strategies are discussed. ProVoice can extend to include alternative design parameters and driving scenarios, encouraging intervention design on a broad scale.2026JSJosh Susak et al.UCL Interaction CentreAutomated Driving Interface & Takeover DesignVoice User Interface (VUI) DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)CHI
Towards Inclusive External Human-Machine Interface: Exploring the Effects of Visual and Auditory eHMI for Deaf and Hard-of-Hearing PeopleExternal Human-Machine Interfaces (eHMIs) have been proposed to facilitate communication between Automated Vehicles (AVs) and pedestrians. However, no attention was given to Deaf and Hard-of-Hearing (DHH) people. We conducted a formative study through focus groups with 6 DHH people and 6 key stakeholders (including researchers, assistive technologists, and automotive interface designers) to compare proposed eHMIs and extract key design requirements. Subsequently, we investigated the effects of visual and auditory eHMI in a virtual reality user study with 32 participants (16 DHH). Results from our scenario suggesting that (1) DHH participants spent more time looking at the AV; (2) both visual and auditory eHMIs enhanced trust, usefulness, and perceived safety; and (3) only visual eHMIs reduced the time to step into the road, time looking at the AV, gaze time, and percentage looking at active visual eHMI components. Lastly, we provided five practical implications for making eHMI inclusive of DHH people.2026WXWenge Xu et al.Birmingham City UniversityExternal HMI (eHMI) — Communication with Pedestrians & CyclistsDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)Teleoperated DrivingCHI
VIP-Sim: A User-Centered Approach to Vision Impairment Simulation for Accessible DesignPeople with vision impairments (VIPs) often rely on their remaining vision when interacting with user interfaces. Simulating visual impairments is an effective tool for designers, fostering awareness of the challenges faced by VIPs. While previous research has introduced various vision impairment simulators, none have yet been developed with the direct involvement of VIPs or thoroughly evaluated from their perspective. To address this gap, we developed VIP-Sim. This symptom-based vision simulator was created through a participatory design process tailored explicitly for this purpose, involving N=7 VIPs. 21 symptoms, like field loss or light sensitivity, can be overlaid on desktop design tools. Most participants felt VIP-Sim could replicate their symptoms. VIP-Sim was received positively, but concerns about exclusion in design and comprehensiveness of the simulation remain, mainly whether it represents the experiences of other VIPs.2025MRMax Rädler et al.Visual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Universal & Inclusive DesignParticipatory DesignUIST
Evaluating Interfaces for Non-Driving Related Tasks While Operating an E-scooterMicromobility vehicles, such as e-scooters, provide ecological and financial benefits over automotive transportation. However, as with car drivers, micromobility users often perform non-driving related tasks (NDRTs), interacting with stereo controls or navigation tasks, which can lead to accidents. It remains unclear what control interfaces are appropriate and safe for micromobility. We evaluated six interface modalities for NDRTs and conducted a within-subjects study with 35 participants (yielding n=210 observations) in an e-scooter simulator to compare modality safety and preferences. Our results align with existing work on gaze and tactility in the automotive NDRTs context. However, unique to e-scooters, interfaces that required users to alter their grip on the handlebars were less preferred as they compromised stability. Social comfort also emerged as a critical factor due to concerns about public visibility. This work aims to encourage the design of safer, more socially acceptable interfaces for e-scooters and other emerging micromobility vehicles.2025KTKenshikimyo Terao et al.In-Vehicle Haptic, Audio & Multimodal FeedbackMicromobility (E-bike, E-scooter) InteractionAutoUI
SPAT: Situational Prosocial and Aggressive Behavior Perception in Traffic ScaleAutomated 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 but for all road users? Here, we describe a new scale to measure perceived social behavior in urban traffic scenarios. Through an online survey on \textit{N} = 318 individuals and a validation study, we developed the Situational Prosocial and Aggressive Behavior in Traffic Scale and assessed it psychometrically.2025HİHatice Şahin İppoliti et al.Teleoperated DrivingV2X (Vehicle-to-Everything) Communication DesignAI-Assisted Decision-Making & AutomationAutoUI
Mind Games! Exploring the Impact of Dark Patterns in Mixed Reality ScenariosMixed Reality (MR) integrates virtual objects with the real world, offering potential but raising concerns about misuse through dark patterns. This study explored the effects of four dark patterns, adapted from prior research, and applied to MR across three targets: places, products, and people. In a two-factorial within-subject study with 74 participants, we analyzed 13 videos simulating MR experiences during a city walk. Results show that all dark patterns significantly reduced user comfort, increased reactance, and decreased the intention to use MR glasses, with the most disruptive effects linked to personal or monetary manipulation. Additionally, the dark patterns of Emotional and Sensory Manipulation and Hiding Information produced similar impacts on the user in MR, suggesting a re-evaluation of current classifications to go beyond deceptive design techniques. Our findings highlight the importance of developing ethical design guidelines and tools to detect and prevent dark patterns as immersive technologies continue to evolve.2025LMLuca-Maxim Meinhardt et al.Mixed Reality WorkspacesDark Patterns RecognitionMobileHCI