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.

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.

In the context of intelligent systems for human-vehicle collaboration, the fusion of information space, physical space, and user cognitive space has become a trend. This paper aims to address the challenges posed by information perception gaps and cognitive limitations experienced by drivers by leveraging Augmented Reality Head-up Displays (ie, AR-HUD) to compensate for perceptual deficiencies and enhance driver cognition. We introduce the innovative concept of the Human-Machine Team Situation Awareness (ie, TSA) loop model. Firstly, we analyze the cognitive characteristics of drivers and the spatiotemporal information elements within hazardous scenarios. Subsequently, AR-HUDs are employed to provide drivers with perceptual compensation and cognitive enhancement. Furthermore, we design AR-HUD interfaces for two representative scenarios. The results demonstrate that, with the support of AR-HUDs, the integration of dynamic interface elements proves to be more effective in compensating for perceptual deficiencies, and the inclusion of predictive information contributes to improved driving performance. Notably, in emergency situations, AR-HUDs play a crucial role in providing decision-enhancing information to drivers. The proposed theoretical framework offers opportunities for expanding the theoretical approaches and application domains of AR-HUDs.

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.

Autonomous vehicles (AVs) have the opportunity to reduce accident and injury rates in urban areas and improve safety for vulnerable road users (VRUs). To realize these benefits, AVs have to communicate with VRUs like pedestrians. While there are proposed solutions concerning the visualization or modality of external human-machine interfaces, a research gap exists regarding the AVs’ communication strategy when interacting with pedestrians. Our work presents a comparative study of an autonomous delivery vehicle with three communication strategies ranging from polite to dominant in two scenarios, at a crosswalk or on the street. We investigated these strategies in an online-based video study in a German (N = 34) and a Chinese sample (N = 56) regarding compliance, acceptance and trust. We found that a polite strategy led to more compliance in the Chinese but not the German sample. However, the polite strategy positively affected trust and acceptance of the AV in both samples equally.

In the cut-in scenario of the ACC system, there is often a lack of harmony between people and cars due to the limitations of sensors and control strategies. Finding and solving the conflict between the driver and the machine is essential to achieve harmonious Human-Machine Cooperation. This research is to understand the conflict between the driver and ACC system in the cut-in scenario based on the previous work of driver trust experiment. The research selected eight drivers for in-depth interview, and the results showed that the biggest conflict between the driver and ACC was that the driver's cognitive and behavioural patterns were significantly different from the ACC system. It is mainly reflected on three aspects: the different definition of the cut-in scenario, the risk perception and the stress of the impending danger, and the perceptual process of cut-in scenario. In order to reduce human-machine conflict, the research proposed three design strategies: (1) Redefine the cut-in scenario based on the driver's cognition. (2) Keep the ACC human-machine interface consistent with the driver's psychological perception. (3) Help drivers cope with dangerous scenario with three levels of warning signals: guidance information, warning information and takeover information.

A concept consisting of a new configurable vacuum gripper system and a corresponding method for determining optimal grasp configurations solely based on 3D vision is introduced. The robot system consists of a dynamically configurable vacuum gripper, a visual sensor, and a robot arm that are used in combination with a new grasp planner to robustly grasp unknown objects in arbitrary positions. For this purpose, formalized aspects of selecting contact surfaces for arbitrary suction cups are described; the concept involves visual detection of the objects, segmentation, iterative grasp planning, and action execution. The approach allows for a fast and efficient, yet precise execution of grasps. The core idea is a two-step 3D data acquisition approach and grasp point computation that takes advantage of the fact that the suction cups of the gripper can all be aligned axis-parallel. Therefore, an adequate sensor-based surface acquisition is done from a single viewpoint with respect to the gripper. Results of realworld experiments show that the proposed concept is suitable for a wide range of different and unknown objects in our setup.

We consider an approach to generative art which exploits the structure of magic squares as a generative engine. Magic squares are used for image generation by mapping their properties into visual schemes. Our underlying hypothesis is that order in some form can serve as a prerequisite for a particular set of aesthetic events. By resorting to a mathematical structure that is not formulated as a function, but for which an inherent order is present, we reduce the most difficult task in generative art, the design of a generative system, to the much easier task of designing representational schemes for the visualization of number patterns. A set of schemes to achieve this task has been realized within a joint project between programmers and artists. The results of this project are discussed and illustrated.

Microblog has been prevailing all over the world and there are more and more researches concentrating on uncovering the latent attributes of users. This is the first research aiming at distinguish between the organizational users and the personal users. In this poster, we restrict our attention solely to the verified users on Sina microblog, which is the most famous microblog in China. Then we build classifiers to identify the users' type. Finally we achieve an accuracy of over 87%, and we find some interesting different points of two kinds of users. They have diverse choice for tweet-posting clients, and organizational users prefer to create tweets originally. At the same time, the users of the same type are more likely to be co-mentioned and build a reciprocated relation in microblog. Moreover, they have different patterns of tweet-posting time series.

Friend recommendation is popular in social network services to help people make new friends and expand their networks. Friend recommendation is either based on topological structures of a social network, or derived from profile information of users. However, dynamically recommending friends by considering both social connections and a context of social connections (e.g., similar interest) in a way of visual exploration is not well supported by existing tools. In this paper, we propose a novel visual system, SFViz (Social Friends Visualization), to support users to explore and find friends interactively under a context of interest. Our approach leverages both semantic structure of activity data and topological structures in social networks. In SFViz, a hierarchical structure of social tags is generated to help users navigate through a network of interest. Multiscale and cross-scale aggregations of similarity among people are presented in the hierarchy to support users to seek potential friends. We report a case study using SFViz to explore the recommended friends based on people's tagging behaviors in a music community, Last.fm. The results indicate that our system can enhance users' awareness of their social networks under different interest contexts, and help users seek potential friends sharing similar interests in an interactive way.

In this paper we describe a method for directly deducing new interpolating subdivision masks for meshes from corresponding approximating subdivision masks. The purpose is to avoid complex computation for producing interpolating subdivision masks on extraordinary vertices. The method can be applied to produce new interpolating subdivision schemes, solve some limitations in existing interpolating subdivision schemes and satisfy some application needs. As cases, in this paper a new interpolating subdivision scheme for polygonal meshes is produced by deducing from the Catmull-Clark subdivision scheme. It can directly operate on polygonal meshes, which solves the limitation of Kobbelt's interpolating subdivision scheme. A new √3 interpolating subdivision scheme for triangle meshes and a new √2 interpolating subdivision scheme for quadrilateral meshes are also presented in the paper by deducing from √3 subdivision schemes and 4-8 subdivision schemes respectively. They both produce C 1 continuous limit surfaces and avoid the blemish in the existing interpolating √3 and √2 subdivision masks where the weight coefficients on extraordinary vertices can not be described by formulation explicitly. In addition, by adding a parameter to control the transition from approximation to interpolation, they can produce surfaces intervening between approximating and interpolating which can be used to solve the "popping effect" problem when switching between meshes at different levels of resolution. They can also force surfaces to interpolate chosen vertices.