As autonomous personal mobility vehicles (APMVs) are increasingly integrated into shared spaces, short-distance interactions between pedestrians and APMVs will become more frequent. To facilitate communication in shared spaces, APMVs equipped with external human-machine interfaces (eHMIs). Although the eHMI is primarily designed to communicate with pedestrians, its communication also affects the APMV passenger due to the short-distance interaction. This paper focused on the effect of passengers’ personality traits on their user experience when the APMV exhibits different eHMIs. An experiment was conducted in the field with 24 participants as APMV passengers who experienced three distinct eHMI types: eHMI-T (text-based), eHMI-NV (neutral voice-based), and eHMI-AV (affective voice-based). Through causal discovery analysis, our findings revealed that when the APMV is equipped with eHMI-T, various personality traits of passengers collectively influenced their user experience. In contrast, the eHMI-NV design demonstrated that personality traits had no direct influence on user experience. The eHMI-AV design primarily showed that agreeableness and extraversion negatively influenced concerns about drawing attention, which subsequently affected other user experience. Based on the results, this paper recommends designing different eHMIs based on the APMV ownerships, such as private or public shared APMVs.

Autonomous Personal Mobility Vehicles (APMVs) are designed to address the “last-mile” transportation challenge for everyone. When an APMV encounters a pedestrian, it uses an external Human-Machine Interface (eHMI) to negotiate road rights. Through this interaction, passengers are also passively exposed to the process. This study examines passengers' gaze behavior toward pedestrians during such interactions, focusing on whether passengers' personality traits influence their gaze patterns towards pedestrians when using different eHMI designs. When using a visual-based eHMI, which caused passengers to struggle in perceiving the communication content, the results suggested that passengers with higher Neuroticism scores, who were more sensitive to communication details, might seek cues from pedestrians' reactions. In addition, a multimodal eHMI (visual and voice) using neutral voice did not significantly affect the gaze behavior of passengers toward pedestrians, regardless of personality traits. In contrast, a multimodal eHMI using affective voice encouraged passengers with high Openness to Experience scores to focus on pedestrians' heads. In summary, this study revealed how different eHMI designs influence passengers' gaze behavior and highlighted the effects of personality traits on their gaze patterns toward pedestrians, providing new insights for personalized eHMI designs.

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.

Autonomous personal mobility vehicle (APMV) is a miniaturized autonomous vehicle designed for short-distance mobility to everyone. Due to its open design, APMV’s passengers are exposed to communications between the external human-machine interface (eHMI) on APMV and pedestrians. Therefore, effective eHMI designs for APMV need to consider potential impacts of APMV-pedestrian interactions on passengers’ subjective feelings. This study from the perspective of APMV passengers discussed three eHMI designs: (1) graphical user interface (GUI)-based eHMI with text message (eHMI-T), (2) multimodal user interface (MUI)-based eHMI with neutral voice (eHMI-NV), and (3) MUI-based eHMI with affective voice (eHMI-AV). In a riding field experiment (N = 24), eHMI-T made passengers feel awkward during the “silent time” when eHMI-T conveyed information exclusively to pedestrians, not passengers. MUI-based eHMIs with voice cues showed advantages, with eHMI-NV excelling in pragmatic quality and eHMI-AV in hedonic quality. Considering passengers’ personalities and genders in APMV eHMI design is also highlighted.

Eye typing interfaces enable a person to enter text into an interface using only their own eyes. But despite the inherent advantages of touchless operation and intuitive design, such eye-typing interfaces often suffer from slow typing speeds, resulting in slow words per minute (WPM) counts. In this study, we add word and letter prediction to the eye-typing interface and investigate users’ typing performance as well as their subjective experience while using the interface. In experiment 1, we compared three typing interfaces with letter prediction (LP), letter + word prediction (L + WP), and no prediction (NoP), respectively. We found that the interface with L + WP achieved the highest average text entry speed (5.48 WPM), followed by the interface with LP (3.42 WPM), and the interface with NoP (3.39 WPM). Participants were able to quickly understand the procedural design for word prediction and perceived this function as very helpful. Compared to LP and NoP, participants needed more time to familiarize themselves with L + WP in order to reach a plateau regarding text entry speed. Experiment 2 explored training effects in L + WP interfaces. Two moving speeds were implemented: slow (6.4°/s same speed as in experiment 1) and fast (10°/s). The study employed a mixed experimental design, incorporating moving speeds as a between-subjects factor, to evaluate its influence on typing performance throughout 10 consecutive training sessions. The results showed that the typing speed reached 6.17 WPM for the slow group and 7.35 WPM for the fast group after practice. Overall, the two experiments show that adding letter and word prediction to eye-typing interfaces increases typing speeds. We also find that more extended training is required to achieve these high typing speeds.

Robots in public spaces need to communicate with lay persons who are not directly involved in the robot task to coordinate their movements and resolve critical situations. Hereby, this communication aims at salience and clarity and at the same time needs to be unobtrusive. While in automated cars, communication with uninvolved road members has been investigated with the label external human-machine interface (eHMI) in human-robot interaction (HRI) this has not been systematically discussed. This study investigates some of the mainly discussed eHMI concepts (blinker lights, beep, and speech) for solving critical situations in HRI. Six critical situations were presented together with five communication strategies (presented as videos) in an online study with N = 175 participants. Mainly, criticality and trust were measured as dependent variables. Overall, situations including visually or hearing-impaired persons were perceived as most critical. For all situations, criticality was reduced with added interaction modalities. The combination of blinker lights and voice was ranked as the most preferred strategy for five situations and led to a reduction in criticality of all situations and higher trust in the robot. The relation between perceived criticality and trust was partially mediated by predictability and transparency. Design recommendations for solving critical situations through robots’ communication strategies in the public are discussed.

We present an eye typing interface with one-point calibration, which is a two-stage design. The characters are clustered in groups of four characters. Users select a cluster by gazing at it in the first stage and then select the desired character by following its movement in the second stage. A user study was conducted to explore the impact of auditory and visual feedback on typing performance and user experience of this novel interface. Results show that participants can quickly learn how to use the system, and an average typing speed of 4.7 WPM can be reached without lengthy training. The subjective data of participants revealed that users preferred visual feedback over auditory feedback while using the interface. The user study indicates that this eye typing interface can be used for walk-up-and-use interactions, as it is easily understood and robust to eye-tracking inaccuracies. Potential areas of application, as well as possibilities for further improvements, are discussed.

Nowadays, with the development of eye tracking technology, the gaze-interaction applications demonstrate great potential. Smooth pursuit based gaze typing is an intuitive text entry system with low learning effort. In this study, we provide a language-prediction function for a smooth-pursuit based gaze-typing system. Since the state-of-the-art neural network models have been successfully applied in language modeling, this study uses a pretrained model based on convolutional neural networks (CNNs) and develops a prediction function, which can predict both next possible letters and word. The results of a pilot experiment have shown that the next possible letters or word can be well predicted and selected. The mean typing speed can achieve 4.5 words per minute. The participants consider that the word prediction is helpful for reducing the visual search time.