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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. [ABSTRACT FROM AUTHOR]

Cobalt ethylenediamine ([Co(en)3]3+) is widely used for NOx remediation in flue gas. However, its industrial applications are limited due to the lack of an efficient regeneration method that converts Co3+ to Co2+. Here, hydrogen chloride is applied as an additive and the performance of cobalt ethylenediamine under variable flue gas compositions is analyzed. The results demonstrate that low pH values favor the conversion of Co3+ to Co2+. Further, a reaction temperature of 100 °C and more than three minutes reaction time were found to accelerate the reduction of Co3+. The NOx removal efficiency could reach more than 70 % after the first regeneration. After 23 consecutive days of continuous experiments, the denitration efficiency of flue gas could be as high as 60 % or more. [ABSTRACT FROM AUTHOR]

AbstractIn 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. [ABSTRACT FROM AUTHOR]