Uncertainty inherently exists in the autonomous decision-making process of robots. Involving humans in resolving this uncertainty not only helps robots mitigate it but is also crucial for improving human-robot interactions. However, in public urban spaces filled with unpredictability, robots often face heightened uncertainty without direct human collaborators. This study investigates how robots can engage bystanders for assistance in public spaces when encountering uncertainty and examines how these interactions impact bystanders' perceptions and attitudes towards robots. We designed and tested a speculative `peephole' concept that engages bystanders in resolving urban robot uncertainty. Our design is guided by considerations of non-intrusiveness and eliciting initiative in an implicit manner, considering bystanders' unique role as non-obligated participants in relation to urban robots. Drawing from field study findings, we highlight the potential of involving bystanders to mitigate urban robots' technological imperfections to both address operational challenges and foster public acceptance of urban robots. Furthermore, we offer design implications to encourage bystanders' involvement in mitigating the imperfections.

Abstract Enhancing transparency in automated vehicle (AV) behaviour can help pedestrians understand how AVs work, which builds trust and ensures safer interactions. As AVs increasingly operate as part of a coordinated network, it is important to make their connectivity clear, helping pedestrians anticipate AV behaviour on the road. This study uses a qualitative design exploration to investigate visual methods for conveying AV connectivity. In the first phase, design concepts were generated using a biomimicry approach, drawing inspiration from nature, such as the harmonious chirping of crickets. In the second phase, focus groups with 16 participants were conducted to gather new concept ideas and evaluate the biomimicry-inspired designs. Our findings suggest that network symbols (e.g., Wi-Fi signals) or graphical elements resembling these symbols (e.g., ripple waves) are more effective in communicating connectivity than abstract methods, such as light patterns moving in a coordinated manner across vehicles. Highly visible connectivity cues may enhance pedestrians' perceived safety, a promising area for future research. This research contributes to ongoing efforts in designing intuitive visual communication strategies for AVs, moving beyond intent communication to include how AVs function as a network.

Abstract Autonomous vehicles (AVs) tend to disrupt the atmosphere and pedestrian experience in urban shared spaces, undermining the focus of these spaces on people and placemaking. We investigate how external human-machine interfaces (eHMIs) supporting AV-pedestrian interaction can be extended to consider the characteristics of an urban shared space. Inspired by urban HCI, we devised three place-based eHMI designs that (i) enhance a conventional intent eHMI and (ii) exhibit content and physical integration with the space. In an evaluation study, 25 participants experienced the eHMIs in an immersive simulation of the space via virtual reality and shared their impressions through think-aloud, interviews, and questionnaires. Results showed that the place-based eHMIs had a notable effect on influencing the perception of AV interaction, including aspects like visual aesthetics and sense of reassurance, and on fostering a sense of place, such as social interactivity and the intentionality to coexist. In measuring qualities of pedestrian experience, we found that perceived safety significantly correlated with user experience and affect, including the attractiveness of eHMIs and feelings of pleasantness. The paper opens the avenue for exploring how eHMIs may contribute to the placemaking goals of pedestrian-centric spaces and improve the experience of people encountering AVs within these environments.

Policymakers advocate for the use of external Human-Machine Interfaces (eHMIs) to allow autonomous vehicles (AVs) to communicate their intentions or status. Nonetheless, scalability concerns in complex traffic scenarios arise, such as potentially increasing pedestrian cognitive load or conveying contradictory signals. Building upon precursory works, our study explores ‘interconnected eHMIs,’ where multiple AV interfaces are interconnected to provide pedestrians with clear and unified information. In a virtual reality study (N=32), we assessed the effectiveness of this concept in improving pedestrian safety and their crossing experience. We compared these results against two conditions: no eHMIs and unconnected eHMIs. Results indicated interconnected eHMIs enhanced safety feelings and encouraged cautious crossings. However, certain design elements, such as the use of the colour red, led to confusion and discomfort. Prior knowledge slightly influenced perceptions of interconnected eHMIs, underscoring the need for refined user education. We conclude with practical implications and future eHMI design research directions.

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.

Obesity is a global health concern associated with various demographic and lifestyle factors including physical activity (PA). Research studies generally used self-reported PA data or, when accelerometer-based activity trackers were used, highly aggregated data (e.g., daily average). This suggests that the rich potential of detailed activity tracker data is largely under-exploited and that deeper analyses may help better understand such relationships. This is particularly true in children and adolescents who are distinct and engage more in bursts of PA. This article presents ABIPA, a machine learning-based methodology that integrates various aspects of accelerometer-based PA data into weight status prediction for adolescents. We propose a method to derive features regarding the structure of different PA time series using Auto-Regressive Integrated Moving Average (ARIMA). The ARIMA-based PA features are combined with other individual attributes to predict weight status and the importance of these features is further unveiled. We apply ABIPA to a dataset about young adolescents (N = 206) containing, for each participant, a 7-day continuous accelerometer dataset (60 Hz, GENEActiv tracker from ActivInsights) and a range of their socio-demographic, anthropometric, and lifestyle information. The results indicate that our method provides a practical approach for integrating accelerometer-based PA patterns into weight status prediction and paves the way for validating their importance in understanding obesity factors.

The ability of autonomous vehicles (AVs) to interact socially with pedestrians poses a significant impact on their integration with urban traffic. This is particularly important for vehicle-pedestrian shared spaces due to increased social requirements in comparison to vehicular roads. Current pedestrian experience in shared spaces suffers from negative attitudes towards AVs and the consequently low acceptability of AVs in these spaces. HRI work shows that the acceptability of robots in public spaces can be positively impacted by their perceived sociability (i.e., possessing social skills), which can be enhanced by their ability to express emotions. Inspired by this approach, we follow a systematic process to design emotional expressions for AVs using the headlight ("eye'') area and investigate their impact on perceived sociability of AVs in shared spaces, by conducting expert focus groups (N=12) and an online video-based user study (N=106). Our findings confirm that the perceived sociability of AVs can be enhanced by emotional expressions indicated through emotional eyes. We further discuss implications of our findings for improving pedestrian experience and attitude in shared spaces and highlight opportunities to use AVs' emotional expressions as a new external communication strategy for future research.