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.

Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will prefer other means of transportation. Yet, capturing and understanding how individuals perceive cycling risk is complex and often slow, with researchers defaulting to traditional surveys and in-loco interviews. In this study, we tackle this problem. We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments and asking them to select the one they perceive as safer for cycling, if any. Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals’ responses, learns preferences directly from images, and includes ties (often discarded in the literature). Effectively, this model learns to predict human-style perceptions, evaluating which cycling environments are perceived as safer. Our model achieves good results, showcasing this approach has a real-life impact, such as improving interventions’ effectiveness. Furthermore, it facilitates the continuous assessment of changing cycling environments, permitting short-term evaluations of measures to enhance perceived cycling safety. Finally, our method can be efficiently deployed in different locations with a growing number of openly available street-view images.

While the use of robots in public spaces is increasing, still few studies explore the resulting everyday human-robot interactions (HRI). The present study sought to bridge the disparity between real-world interactions and the frequently examined hypothetical interactions. To do so, we investigate the imagined and actual interaction with an ice cream serving robot. In two studies and an exploratory study comparison, we examined user acceptance and preference for the degree of anthropomorphic appearance. Although a typical human service task was taken over by a robot, an industrial robot was preferred according to participants' ratings in both studies. Moreover, both studies demonstrated that robot enthusiasm significantly relates to participants' acceptance of the robot for the task. Besides these commonalities, the results showed also that while humans were preferred over robots in the imagined setting, no clear preference was found in the real-life setting. Additional analyses compared the free text answers of the two studies and provided insights into participants' general attitudes toward robots in the workforce. In line with the higher preferences for humans over robots in the imagined setting, considerably more participants mentioned a better customer experience with humans as important in the imagined study compared to the participants who interacted with the robot. The studies strikingly demonstrated that imaginary settings yield similar outcomes to those where participants physically engage with the robot in certain aspects, such as their preference for anthropomorphism. However, this phenomenon does not appear to hold for other facets, such as their favored service agent.

There is great application potential for mobile robots that move along sidewalks, footpaths and cycle paths. At the same time, the legal framework for their operation is currently lacking, so that a realistic evaluation of the suitable characteristics of a robot, its behavior, and the resulting acceptance by the population is still pending. The currently running project RoboTraces aims to fill this gap and classifies the behavior of people during encounters with autonomous systems in everyday life. For this purpose, a multi-month study is currently being conducted in which a robot moves through Freiberg, a medium-size city with 40,000 inhabitants in Central Europe. We are recording the reactions of passers-by, classifying them according to the context of the situation and assessing their significance for the planning of movement corridors for small mobile systems.

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.

Recent advances in the automated detection of motorcycle riders’ helmet use have enabled road safety actors to process large scale video data efficiently and with high accuracy. To distinguish drivers from passengers in helmet use, the most straightforward way is to train a multi-class classifier, where each class corresponds to a specific combination of rider position and individual riders’ helmet use. However, such strategy results in long-tailed data distribution, with critically low class samples for a number of uncommon classes. In this paper, we propose a novel approach to address this limitation. Let n be the maximum number of riders a motorcycle can hold, we encode the helmet use on a motorcycle as a vector with 2n bits, where the first n bits denote if the encoded positions have riders, and the latter n bits denote if the rider in the corresponding position wears a helmet. With the novel helmet use positional encoding, we propose a deep learning model that stands on existing image classification architecture. The model simultaneously trains 2n binary classifiers, which allows more balanced samples for training. This method is simple to implement and requires no hyperparameter tuning. Experimental results demonstrate our approach outperforms the state-of-the-art approaches by 1.9% accuracy.