Natural user interfaces (NUI) have been used to reduce driver distraction while using in-vehicle infotainment systems (IVIS), and multimodal interfaces have been applied to compensate for the shortcomings of a single modality in NUIs. These multimodal NUIs have variable effects on different types of driver distraction and on different stages of drivers' secondary tasks. However, current studies provide a limited understanding of NUIs. The design of multimodal NUIs is typically based on evaluation of the strengths of a single modality. Furthermore, studies of multimodal NUIs are not based on equivalent comparison conditions. To address this gap, we compared five single modalities commonly used for NUIs (touch, mid-air gesture, speech, gaze, and physical buttons located in a steering wheel) during a lane change task (LCT) to provide a more holistic view of driver distraction. Our findings suggest that the best approach is a combined cascaded multimodal interface that accounts for the characteristics of a single modality. We compared several combinations of cascaded multimodalities by considering the characteristics of each modality in the sequential phase of the command input process. Our results show that the combinations speech + button, speech + touch, and gaze + button represent the best cascaded multimodal interfaces to reduce driver distraction for IVIS. INDEX TERMS Cascaded multimodal interface, driver distraction, head-up display (HUD), human-computer interaction (HCI), in-vehicle infotainment system (IVIS), learning effect, natural user interface (NUI).

Cultural heritage (CH) artifacts, such as ceramics and clothes, reflect the unique characteristics of ancient cultures and have the potential to be sustainably employed in modern design and entertainment. In particular, the shape of ceramics reflects regional and historical characteristics, so datafication is a promising avenue to preserve these assets for future generations. However, design is a specialized domain that requires significant human (expert and novice) labor. This often tedious process decreases the labeler's motivation to complete the task, and data consistency varies with the experience and motivation of the labeler. To increase engagement, we developed an image labeling platform with graphical icon-based labeling methods and introduced gamification. The robust labeling methods with gamification increased novices' engagement and decreased the workload of expert and novice labelers, but decreased data agreement between experts and novices, so we consider opportunities for gamification within the specialized cultural heritage domain.

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Signs, landmarks, and other urban elements should attract attention to or harmonize with the environment for successful landscape design. These elements also provide information during navigation—particularly for people with cognitive difficulties or those unfamiliar with the geographical area. Nevertheless, some urban components are less eye-catching than intended because they are created and positioned irrespective of their surroundings. While quantitative measures such as eye tracking have been introduced, they help the initial or final stage of the urban design process and they involve expensive experiments. We introduce machine-learning-predicted visual saliency as iterative feedback for pedestrian attention during urban element design. Our user study focused on wayfinding signs as part of urban design and revealed that providing saliency prediction promoted a more efficient and helpful design experience without compromising usability. The saliency-guided design practice also contributed to producing more eye-catching and aesthetically pleasing urban elements. The study demonstrated that visual saliency can lead to an improved urban design experience and outcome, resulting in more accessible cities for citizens, visitors, and people with cognitive impairments.