Journal on Multimodal User Interfaces最新文献

Shared Automated Vehicles (SAVs) promise to make automated mobility accessible to a wide range of people while reducing air pollution and improving traffic flow. In the future, these vehicles will operate with no human driver on board, which poses several challenges that might differ depending on the cultural context and make one-fits-all solutions demanding. A promising substitute for the driver could be Digital Companions (DCs), i.e. conversational agents presented on a screen inside the vehicles. We conducted interviews with Colombian participants and workshops with German and Korean participants and derived two design concepts of DCs as an alternative for the human driver on SAVs: a human-like and a robot-like. We compared these two concepts to a baseline without companion using a scenario-based online questionnaire with participants from Colombia (N = 57), Germany (N = 50), and Korea (N = 29) measuring anxiety, security, trust, risk, control, threat, and user experience. In comparison with the baseline, both DCs are statistically significantly perceived as more positively. While we found a preference for the human-like DC among all participants, this preference is higher among Colombians while Koreans show the highest openness towards the robot-like DC.

Virtual reality (VR) technology has been increasingly focusing on incorporating multimodal outputs to enhance the sense of immersion and realism. In this work, we developed AirWhisper, a modular wearable device that provides dynamic airflow feedback to enhance VR experiences. AirWhisper simulates wind from multiple directions around the user’s head via four micro fans and 3D-printed attachments. We applied a Just Noticeable Difference study to support the design of the control system and explore the user’s perception of the characteristics of the airflow in different directions. Through multimodal comparison experiments, we find that vision-airflow multimodality output can improve the user’s VR experience from several perspectives. Finally, we designed scenarios with different airflow change patterns and different levels of interaction to test AirWhisper’s performance in various contexts and explore the differences in users’ perception of airflow under different virtual environment conditions. Our work shows the importance of developing human-centered multimodal feedback adaptive learning models that can make real-time dynamic changes based on the user’s perceptual characteristics and environmental features.

The transportation of goods by road is crucial in Tanzania’s Central and North-South Corridors. However, challenges such as truck congestion and road accidents are affecting the efficiency of these routes. Road crashes are prevalent in low- and middle-income countries, with Africa experiencing an exceptionally high rate. This study examines the opinions of Tanzanian truck drivers on the effectiveness of Advanced Driver Assistance Systems in reducing road safety risks. A discriminant analysis was conducted to assess the awareness and use of these systems among different driver experienced groups. The results highlight the importance of improving infrastructure, ensuring vehicle safety standards, providing comprehensive driver training, and integrating innovative Intelligent Transport Systems to address road safety issues. In conclusion, the study provides valuable insights for policymakers and stakeholders to strengthen road safety measures in Tanzania, facilitating smoother road freight transport operations and promoting economic growth.

Close following to lead vehicles is associated with increased risk of rear-end crashes in road traffic. One way to reduce instances of close following is through increased use of the Advanced Driver Assistance System (ADAS) Adaptive Cruise Control (ACC), which is designed to adjust vehicle speed to maintain a safe time headway. Since the activation of ACC is driver-initiated, there is a need to influence the propensity of drivers to use the function. This research aimed to explore whether in-vehicle nudging interventions could be effective for this purpose. A field trial was conducted to consecutively assess the effects of two nudges on drivers’ utilization of ACC, compared to baseline usage. Exposing the participants (n = 49) to the first ambient design nudge resulted in a 46% increase in ACC usage on average. Following the introduction of the second nudge (a competitive leaderboard nudge), the average increase among participants (n = 48) during the complete treatment period reached 61%. The changes in ACC utilization varied between individual drivers, highlighting the need to monitor behavioral outcomes of nudges and adapt them when needed. In conclusion, this research shows that utilizing in-vehicle nudging is a promising approach to increase the use of vehicle functions contributing to improved traffic safety.

The primary safety hazard at unsignalized intersections, particularly in urban areas, is pedestrian-vehicle collisions. Due to its complexity and inattention, pedestrian crossing behaviour has a significant impact on their safety. This study introduces a novel framework to enhance pedestrian safety at unsignalized intersections by developing a predictive model of pedestrian crossing behaviour using machine learning algorithms. While accounting for crossing behaviour as the dependent variable and other independent variables, the analysis prioritises accuracy and internal validity. Important feature scores for the different algorithms were assessed. The model results revealed that the arrival first of a pedestrian or vehicle, pedestrian delay, vehicle speed, pedestrian speed, age, gender, traffic hour, and vehicle category are highly influencing variables for analysing pedestrian behaviour while crossing at unsignalized intersections. This study found that the prediction of pedestrian behaviour based on random forest, extreme gradient boosting and binary logit model achieved 81.72%, 77.19% and 74.95%, respectively. Algorithms, including k-nearest neighbours, artificial neural networks, and support vector machines, have varying classification performance at every step. The findings of this study may be used to support infrastructure-to-vehicle interactions, enabling vehicles to successfully negotiate rolling pedestrian behaviour and improving pedestrian safety.

Storytelling is a long-established tradition and listening to stories is still a popular leisure activity. Caused by technization, storytelling media expands, e.g., to social robots acting as multi-modal storytellers, using different multimodal behaviours such as facial expressions or body postures. With the overarching goal to automate robotic storytelling, we have been annotating stories with emotion labels which the robot can use to automatically adapt its behavior. With it, three different approaches are compared in two studies in this paper: 1) manual labels by human annotators (MA), 2) software-based word-sensitive annotation using the Linguistic Inquiry and Word Count program (LIWC), and 3) a machine learning based approach (ML). In an online study showing videos of a storytelling robot, the annotations were validated, with LIWC and MA achieving the best, and ML the worst results. In a laboratory user study, the three versions of the story were compared regarding transportation and cognitive absorption, revealing no significant differences but a positive trend towards MA. On this empirical basis, the Automated Robotic Storyteller was implemented using manual annotations. Future iterations should include other robots and modalities, fewer emotion labels and their probabilities.