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.

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.

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.

The dynamic motion of fluids is essential in generating aesthetically appealing effects like the oriental ink painting and fluid art. Due to the prohibitively high cost required in volumetric fluid simulations, implementing an interactive volumetric painting system in immersive virtual environments (IVEs) is challenging. We propose a framework to generate immersive fluid dynamic environments by solving the Navier-Stokes equation on the viewing sphere. With this approach, we largely reduce the complexity without losing the effective resolution. We demonstrate our method on a real-time 360-degree painting system and verify the usability of our interface prototype with examples.

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.

Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.

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.

Abstract This paper presents a novel framework that synthesize Chinese sign language (CSL) animation on ubiquitous platform‐independent devices with consideration of the self‐adaptive rendering capability of the device display screen. The display resolution visibility (DRV) factor is introduced here as the measurement of the system's adaptability. Both the 3D virtual human model and sign animation scripts are simplified according to the DRV Factor of the ubiquitous device. Consequently, a real‐time transmission scheme is proposed for sign language animation over a heterogeneous network. Simulations results show that these innovations significantly reduce the transmission bandwidth and work very well on the ubiquitous devices. Copyright © 2011 John Wiley & Sons, Ltd.