In interpersonal interactions, individuals often exhibit avoidance behaviors toward others they find unpleasant, which can undermine the comfort of everyday social experiences. Existing human-computer interaction (HCI) research has primarily focused on promoting social connections, while support for avoidance-oriented social situations remains underexplored. To address this gap, we propose leveraging Diminished Reality (DR) technology to obscure perceptual cues of undesirable individuals. We designed and implemented a mixed reality prototype system and conducted experiments manipulating both the occlusion method and social distance. Results indicate that DR significantly reduces users’ social anxiety and sense of social presence. Moreover, participants generally expressed positive attitudes toward usage intention and ethical considerations. This work extends HCI research on social comfort, shifting the focus from “facilitating connection” to “supporting avoidance”.

Aggressive driving often triggers anger and retaliatory behaviors, posing threats to traffic safety. This paper proposes an AI-driven apology mechanism based on an Augmented Reality Head-Up Display (AR-HUD), which delivers immediate apologies on behalf of offending drivers during traffic conflicts and repairs damaged social relations through prosocial lies. We conducted a 2 (scenario risk: high vs. low) × 5 (apology depth) mixed-design experiment (N = 40) to evaluate its effectiveness. Results show that AI apologies enhanced positive emotions and forgiveness intentions while reducing anger, with participants also perceiving psychological benefits. These effects were consistent across both high- and low-risk scenarios. Our findings offer a practical design pathway for human-AI emotional regulation in traffic contexts.

Chinese Kung Fu not only emphasizes external techniques and movements but also places great importance on the cultivation of internal states such as Fa Li (force exertion) and Qi (energy control). However, existing motor learning systems predominantly focus on improving movement accuracy, with limited attention to the awareness and guidance of these internal states. Kung Fu films often use visual effects (VFX) to vividly express traditional cultural imagery. Inspired by this, we designed and developed a wearable motor learning system and interactive interface that integrates interactive VFX and haptic feedback to enhance users’ awareness of internal states such as force exertion and Qi, thereby improving both learning effectiveness and user experience. Through prototyping and an exploratory study, we found that the system significantly improved users’ awareness of Qi and force exertion, as well as their overall training experience. In addition, we identified several usability issues and proposed corresponding design improvements. This study introduces a novel visualization framework for internal state cues in Kung Fu training, offering new perspectives and practical approaches for the design of motor learning systems.

Visual blind spots caused by occlusion from lead vehicles represent a significant latent risk factor contributing to traffic accidents. Although Level 2 (L2) autonomous driving systems can partially mitigate this issue, human drivers are still required to actively perceive potential hazards and understand the behaviour of the autonomous vehicle to ensure driving safety. Therefore, designing interactive interfaces that reduce the risks associated with visual blind spots is critical in autonomous driving scenarios. However, current research on this typical scenario remains relatively limited. This study proposes an innovative cooperative driving warning strategy, focussing on driving situations where the driver's line of sight is blocked by a lead vehicle. The strategy integrates environmental cues and behavioural information from the autonomous driving system through an augmented reality (AR) interface, aiming to facilitate efficient cooperation between human drivers and autonomous systems in perceiving visual blind spots. We systematically evaluated the proposed interface prototype using a driving simulator under L2 autonomous driving conditions. The results indicate that the interface significantly enhances drivers' situation awareness and reduces their reaction time to potential hazards. Additionally, the design improves the quality of human-machine cooperation by decreasing conflicts with the autonomous system, increasing trust in the system, and significantly boosting user satisfaction. This study provides new insights into the design of human-machine cooperative perception interfaces for blind spot scenarios and offers both theoretical foundations and practical implications for the future development of cooperative driving systems.

In autonomous driving vehicles, the heterogeneity between human and automation agents can cause conflicts in decision-making and behaviour due to the difference in perception of hazardous situations. Augmented Reality Human-Machine Interfaces (AR-HMI) provide an opportunity to support driving performance by enabling drivers to intuitively access shared perception and explanation of the automated vehicle. One possible approach to AR-HMI design is to simplify the information of driving tasks based on vehicle context understanding, although there is currently a lack of systematic understanding of how collaborative mechanisms or cognitive features contribute to AR-HMI information design. Therefore, this work develops an augmented reality cognitive interface design method for autonomous driving. It aims to identify novel collaborative interface information visualization and provide a common language and inspiration for the design space.

Real-time goal collaboration represents a promising approach to human-vehicle cooperative driving; however, it remains underexplored. To address this gap, we introduced an innovative human-vehicle cooperative approach and designed four interactive types with increasing autonomous levels to implement it. Additionally, we proposed seven interface design principles to design three increasing levels of transparency for the four interactive types, aiming to enhance collaboration. Experimental results demonstrate the favorable reception of the proposed cooperative approach by users. Furthermore, higher interactive autonomous levels result in reduced workload, and higher interface transparency levels lead to increased satisfaction, trust, and mutual dependence. Notably, the combination of the highest interactive autonomous level and interface transparency level, which exhibited the best performance, is recommended for practical application. This collaborative approach expands the research domain of human-vehicle cooperative driving and offers extensive potential applications across various relevant scenarios.

Affective robots can elicit psychological responses such as attachment and intimacy, which may help alleviate anxiety and enrich users’ emotional experiences. While such robots can take the form of agents (controlled by algorithms) or avatars (controlled by humans or animals), the differential effects of these embodiment forms on users’ emotional responses remain underexplored, particularly in scenarios where avatars are controlled by animals. In this study, we conducted a Wizard of Oz experiment to compare the emotional experience and anxiety relief provided by a robotic cat under different embodiment forms and interaction modes (unidirectional vs. bidirectional). The results indicate that the avatar embodiment significantly enhances users’ affective experiences, fostering stronger emotional bonds and more effective anxiety relief. However, no significant differences were found between the interaction modes with respect to either anxiety relief or emotional outcomes. These findings offer valuable insights for the design of emotionally engaging embodied intelligent systems in contexts such as emotional companionship and mental health interventions. • A wearable robot prototype enables remote interaction between humans and real cats. • Robot embodiment forms and interaction modes affect users’ anxiety and affective experience. • The avatar form leads to greater anxiety relief and stronger affective experiences. • No significant emotional differences are found between interaction modes.

Augmented Reality (AR) has the potential to help drivers become aware of invisible hazards through an Augmented Reality Head-Up Display (AR-HUD). However, this issue is still underexplored. To address it, a novel warning system for invisible dangers in AR-HUD user interfaces has been designed as a carrier for agents' cognitive information to enhance driver perception. This design was created by using a team cooperation perception model that combined the perception cycle of a human driver with a computational agent. Furthermore, user experiments were conducted to investigate the impact of this design on safe driving in two typical scenarios. The experimental results showed that this design can significantly improve drivers’ situation awareness and reaction time in both human-driving and auto-pilot modes, and enhance human drivers' trust in the auto-pilot system. The model and design can be generalized to more AR-HUD scenarios requiring human-machine perception and cognitive cooperation.

Driver’s instinctive responses and skill-based behaviors enable them to react faster and better control their vehicle in dangerous situations. This study incorporated dynamic spatial information design (DSID) in an augmented reality head-up display (AR-HUD) under manual driving conditions. By integrating the skill, rule, and knowledge (SRK) taxonomy and situation awareness (SA) theory, our AR-HUD successfully evoked drivers’ instinctive responses and improved driving safety. First, we converted symbol and sign information processed at the knowledge-based and rule-based levels, respectively, into signal information processed at the skill-based level. Then we developed four AR-HUD interfaces with different dynamic designs for use in a hazardous scenario at an intersection. Finally, we investigated each design’s impact on drivers’ SA and driving performance. Experimental results demonstrated that our DSID enhanced drivers’ SA and accident-avoidance capabilities while reducing their cognitive workload. Among the four AR-HUD interfaces, the one that incorporated all three information elements under study (i.e., lateral warning, dynamic driving space, and speedometer) performed the best. This indicates that our proposed framework has potential applications in other similar dangerous driving scenarios, thus contributing to the development of safer and more efficient driving environments.