Understanding automated vehicles (AVs) is crucial to improving their acceptance. Numerous approaches to visualizing relevant traffic information to passengers have been proposed and empirically evaluated. As this is time-consuming, costly, and reduces the possible design parameters, we employed multi-objective Bayesian optimization to optimize the design of visualizations in AVs. In particular, we evaluated multi-session aspects involving iterative optimization. We optimized the design for passenger trust and perceived safety while minimizing cognitive load. Results from an online study (N=74) show that this method effectively identifies visualization design parameter values that improve trust, safety, and predictability while making the design process more efficient and scalable. However, shortcomings of the computational approach when optimizing for subjective measurements are highlighted and discussed.

Automated vehicles promise to improve accessibility and access to personal mobility for everyone. However, their design and current research trends in visualizing relevant information do not reflect this commitment to accessibility for users with visual impairments. Therefore, we designed and implemented a visual and auditory communication concept for people with visual impairments seated inside fully automated vehicles. Furthermore, in an online video-based study (N=35, 12 with visual impairments), we compared three levels of auditory information communication: low (safety-relevant information only), medium (additionally including vehicle control and route updates), and high (additionally including sightseeing and destination information). Results showed that trust and user experience significantly improved with additional information, with a corresponding, albeit less robust, effect on perceived safety. However, they also revealed that a potential information saturation was reached with medium information. Our work helps to improve the accessibility of automated vehicles by guiding designers towards adequate information communication.

Wearable Augmented Reality (AR) is increasingly deployed in on-the-move contexts such as automated driving, cycling, and pedestrian navigation. To date, most systems rely on additive overlays that highlight hazards, intentions, or predictions without altering the scene itself. However, advances in head-mounted displays and computer vision now enable Diminished and Modified Reality techniques that suppress, transform, or substitute scene elements. These capabilities conceptually extend AR into Mediated Reality (MR), shifting the design space from "what to add" to "what is perceptually available." Because such mediation reshapes the evidential basis for situation awareness and trust calibration, it raises novel interaction challenges. This position paper argues that MR on the move must become governable, as users need mechanisms to configure, inspect, and understand mediation without compromising safety. Additionally, this position paper outlines design challenges related to governance granularity, epistemic signaling, and accountability, and frames MR on the move as a research agenda for governable perceptual mediation in dynamic, safety-critical environments.

The 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.

Conflicts between user preferences and automated system behavior already shape the experience of automated mobility. For example, a passenger may prefer assertive driving, yet the vehicle slows down early to follow a conservative policy or yield to other actors. Similar conflicts arise at merges, crossings, or right-of-way situations, where users must accept opaque decisions or attempt to negotiate through interfaces not designed for continuous, multi-actor relationships. This position paper argues that such approaches do not scale as mobility becomes more heterogeneous and automated. Instead, it proposes personal mobility agents that act as proxies for users, encode preferences such as comfort and safety margins, and negotiate traffic behavior with other agents under shared safety rules. The central idea is a shift from moment-to-moment user negotiation interfaces to delegation and oversight interfaces, in which proxy agents manage real-time conflicts while users can shape high-level policies and preferences.

High-fidelity simulators are considered vital for evaluating user experience of future systems. This research introduces and tests a novel method to increase psychological fidelity by inducing arousal via virtual height exposure before a simulated Urban Air Mobility (UAM) flight, hypothesizing that this would create a more realistic assessment of the passenger’s experience. We conducted a between-within-subjects study (N=40) examining the effects of this arousal induction alongside physical motion cues from a 3-Degree of Freedom chair. Despite physiological data confirming the arousal manipulation was successful, our findings are inconclusive: neither induced arousal nor motion cues yielded significant differences or equalities for core metrics like trust, perceived safety, or mental load. This finding challenges the assumption that higher-fidelity simulations universally produce different outcomes, suggesting that researchers may not need to induce arousal to gather valid feedback on UAM interface design.

Distracted driving remains a major safety concern, motivating approaches that aim to reduce visual overload before attention breaks down. However, visual overload varies across individuals, making it difficult to determine appropriate interventions for each driver. We investigate whether controllable visual blur can simplify the driving scene and mitigate distraction. To address this challenge, we propose BlurDriving, a target-selective, distance-aware blur system in a Virtual Reality (VR) urban driving simulator, and employ a Human-in-the-Loop Multi-Objective Bayesian Optimization (HITL-MOBO) framework to personalize blur configurations. Across two VR user studies, we evaluated driving under normal conditions in Study 1 and under cognitively demanding conditions in Study 2. We found that personalization revealed strong individual differences in blur preference but did not lead to significant improvements in objective driving performance compared to a no-blur baseline. Qualitative feedback revealed polarized responses: some drivers reported improved focus, while others experienced uncertainty, fatigue, or discomfort. These findings suggest that visual blur is not universally effective. Instead, its benefits depend on individual perceptual strategies and tolerance for visual uncertainty. This work highlights the limits of personalized visual simplification in safety-critical driving and informs adaptive in-vehicle interface design.

Traffic 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.

With automated vehicles (AVs), the absence of a human operator could necessitate external Human-Machine Interfaces (eHMIs) to communicate with other road users. Existing research primarily focuses on pedestrian-AV interactions, with limited attention given to other road users, such as cyclists and drivers of manually driven vehicles. So far, no studies have compared the effects of eHMIs across these three road user roles. Therefore, we conducted a within-subjects virtual reality experiment (N=40), evaluating the subjective and objective impact of an eHMI communicating the AV's intention to pedestrians, cyclists, and drivers under various levels of distraction (no distraction, visual noise, interference). eHMIs positively influenced safety perceptions, trust, perceived usefulness, and mental demand across all roles. While distraction and road user roles showed significant main effects, interaction effects were only observed in perceived usability. Thus, a unified eHMI design is effective, facilitating the standardization and broader adoption of eHMIs in diverse traffic.

Accessible and inclusive design has gained increased attention in HCI, yet practical implementation remains challenging due to resource-intensive prototyping methods. Traditional approaches such as workshops, A-B tests, and co-design sessions struggle to capture the diverse and complex needs of users with disabilities at scale. This position paper argues for an automated, accessible Human-in-the-Loop (HITL) design optimization process that shifts the designer's role from directly crafting prototypes to curating constraints for algorithmic exploration. By pre-constraining the design space based on specific user interaction needs, integrating adaptive multi-modal feedback channels, and personalizing feedback prompts, the HITL approach could efficiently refine design parameters, such as text size, color contrast, layout, and interaction modalities, to achieve optimal accessibility. This approach promises scalable, individualized design solutions while raising critical questions about constraint curation, transparency, user agency, and ethical considerations, making it essential to discuss and refine these ideas collaboratively at the workshop.

As the use of Head-Mounted Displays in moving vehicles increases, passengers can immerse themselves in visual experiences independent of their physical environment. However, interaction methods are susceptible to physical motion, leading to input errors and reduced task performance. This work investigates the impact of G-forces, vibrations, and unpredictable maneuvers on 3D interaction methods. We conducted a field study with 24 participants in both stationary and moving vehicles to examine the effects of vehicle motion on four interaction methods: (1) Gaze Pinch, (2) DirectTouch, (3) Handray, and (4) HeadGaze. Participants performed selections in a Fitts' Law task. Our findings reveal a significant effect of vehicle motion on interaction accuracy and duration across the tested combinations of Interaction Method x Road Type x Curve Type. We found a significant impact of movement on throughput, error rate, and perceived workload. Finally, we propose future research considerations and recommendations on interaction methods during vehicle movement.

Large-scale effects of head-up displays (HUDs) are currently unknown, as experiments focus primarily on empirical data from one participant. Therefore, we simulate the impact of augmented reality (AR) windshield HUDs on driving performance. Using the open-source simulators SUMO and CARLA, we model various AR HUD settings, such as fatigue, and assess their effects on key driving factors such as reaction time, speed adherence, lane-changing behavior, and acceleration. The HUD settings include brightness, information frequency, field of view, and relevance of displayed information. The simulation provides insight into how different HUD configurations may influence driving behavior, contributing to future vehicle design and safety guidelines for AR HUDs.

The absence of a human operator in automated vehicles (AVs) may require external Human-Machine Interfaces (eHMIs) to facilitate communication with other road users in uncertain scenarios, for example, regarding the right of way. Given the plethora of adjustable parameters, balancing visual and auditory elements is crucial for effective communication with other road users. With N=37 participants, this study employed multi-objective Bayesian optimization to enhance eHMI designs and improve trust, safety perception, and mental demand. By reporting the Pareto front, we identify optimal design trade-offs. This research contributes to the ongoing standardization efforts of eHMIs, supporting broader adoption.

Automated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis's efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.

Automated Urban Air Mobility (UAM) can improve passenger transportation and reduce congestion, but its success depends on passenger trust. While initial research addresses passengers' information needs, questions remain about how to simulate air taxi flights and how these simulations impact users and interface requirements. We conducted a between-subjects study (N=40), examining the influence of motion fidelity in Virtual-Reality-simulated air taxi flights on user effects and interface design. Our study compared simulations with and without motion cues using a 3-Degrees-of-Freedom motion chair. Optimizing the interface design across six objectives, such as trust and mental demand, we used multi-objective Bayesian optimization to determine the most effective design trade-offs. Our results indicate that motion fidelity decreases users' trust, understanding, and acceptance, highlighting the need to consider motion fidelity in future UAM studies to approach realism. However, minimal evidence was found for differences or equality in the optimized interface designs, suggesting personalized interface designs.

This paper explores the impact of uncertainty visualizations in automated vehicle (AV) functionality on user perceptions over a three-day longitudinal study. Participants (N=50) watched real-world driving videos twice daily, in the morning and evening. These videos depicted morning and evening commutes, featuring visualizations of AVs' pedestrian detection, vehicle recognition, and pedestrian intention prediction. We measured perceived safety, trust, mental workload, and cognitive load using a within-subjects design. Results show increased perceived safety and trust over time, with higher ratings in the evening sessions, reflecting greater predictability and user confidence in AV by the study's end. However, inconsistencies in pedestrian detection and intention prediction led to mixed reactions, highlighting the need for visualization stability and clarity refinement. Participants also desired a feature indicating the AV's intended path and options for manual intervention. Our findings suggest transparency and usability in AV visualizations can foster trust and perceived safety, informing future AV interface design.

Urban gardening is widely recognized for its numerous health and environmental benefits. However, the lack of suitable garden spaces, demanding daily schedules and limited gardening expertise present major roadblocks for citizens looking to engage in urban gardening. While prior research has explored smart home solutions to support urban gardeners, these approaches currently do not fully address these practical barriers. In this paper, we present PlantPal, a system that enables the cultivation of garden spaces irrespective of one's location, expertise level, or time constraints. PlantPal enables the shared operation of a precision agriculture robot (PAR) that is equipped with garden tools and a multi-camera system. Insights from a 3-week deployment (N=18) indicate that PlantPal facilitated the integration of gardening tasks into daily routines, fostered a sense of connection with one's field, and provided an engaging experience despite the remote setting. We contribute design considerations for future robot-assisted urban gardening concepts.

As living and working spaces become scarce and costly, interiors transitioning between living, working, and sleeping configurations while enabling customized setups are in demand. Traditional furniture consumes space and is cumbersome to rearrange. Shape-changing furniture could solve this, yet existing options lack resolution, stability, or adaptability. We present AirClick, which facilitates on-demand room transformation using modular interactive inflatables. Our fabrication process supports personally fabricated and retrofitted retail inflatables. The touch-actuated modules connect to a floor-based air connector grid, facilitating interaction while integrating with traditional furniture. In a lab study (N=20) across four scenarios (office, meeting room, apartment room, multipurpose hall), participants rapidly transformed rooms and perceived AirClick as significantly more usable with higher intention to use in the everyday scenarios than in the hall, indicating suitability for routine activities with low to medium requirements for robustness. User feedback highlights AirClick's usefulness and scalability in diverse settings, hence showing AirClick's space-saving and customizable design can enhance the functionality and adaptability of living and working spaces.

As autonomous robots become more common in public spaces, spontaneous encounters with laypersons are more frequent. For this, robots need to be equipped with communication strategies that enhance momentary transparency and reduce the probability of critical situations. Adapting these robotic strategies requires consideration of robot movements, environmental conditions, and user characteristics and states. While numerous studies have investigated the impact of distraction on pedestrians’ movement behavior [1]-[4], limited research has examined this behavior in the presence of autonomous robots. This research addresses the impact of robot type and robot movement pattern on distracted and undistracted pedestrians’ movement behavior. In a field setting, unaware pedestrians were videotaped while moving past two working, autonomous cleaning robots. Out of N = 498 observed pedestrians, approximately 8% were distracted by smartphones. Distracted and undistracted pedestrians did not exhibit significant differences in their movement behaviors around the robots. Instead, both the larger sweeping robot and the off-set rectangular movement pattern significantly increased the number of lateral adaptations compared to the smaller cleaning robot and the circular movement pattern. The off-set rectangular movement pattern also led to significantly more close lateral adaptations. Depending on the robot type, the movement patterns led to differences in the distances of lateral adaptations. The study provides initial insights into pedestrian movement behavior around an autonomous cleaning robot in public spaces, contributing to the growing field HRI research.

Urban Air Mobility (UAM) emerges as a potential solution to urban congestion. However, as it lacks integration with existing transportation systems, methods to study its impact are necessary. Traditional empirical approaches are insufficient to study large-scale effects in this not-yet-real context. We developed UAM-SUMO, an extension of the SUMO simulation platform, to simulate the impact of UAM on public transportation, particularly how air taxis affect traffic flow and mode choices. We detail the modifications to SUMO and the UAM operation parameters. We open-source our code at https://github.com/M-Colley/uam-sumo and present a proof-of-concept data collection and analysis for Ingolstadt, Germany.