This study aims to optimize the visual interaction design of AR-HUD and reduce cognitive load in complex driving situations. An immersive driving simulation incorporating eye-tracking technology was utilized to analyze objective physiological indices and measure subjective cognitive load using the NASA-TLX. Additionally, a visual cognitive load index was integrated into a BP-GA neural network model for load prediction, enabling the derivation of an optimal solution for AR-HUD design. The optimized AR-HUD interface demonstrated a significant reduction in cognitive load compared to the previous prototype. The experimental group achieved a mean total score of 25.63 on the WP scale, whereas the control group scored 43.53, indicating a remarkable improvement of 41.4%. This study presents an innovative approach to optimizing AR-HUD design, effectively reducing cognitive load in complex driving situations. The findings demonstrate the potential of the proposed algorithm to enhance user experience and performance.
MotivationAugmented reality head-up display (AR-HUD) interface design takes on critical significance in enhancing driving safety and user experience among professional drivers. However, optimizing the above-mentioned interfaces poses challenges, innovative methods are urgently required to enhance performance and reduce cognitive load.DescriptionA novel method was proposed, combining the IVPM method with a GA to optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design.ResultsExperimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPM-GA significantly enhanced the driving performance, and user experience was enhanced since 80% of participants rated the IVPM-GA interface as visually comfortable and less distracting.ConclusionIn this study, an innovative method was presented to optimize AR-HUD interfaces by integrating IVPM with a GA. IVPM-GA effectively reduced cognitive load, enhanced driving performance, and improved user experience for professional drivers. The above-described findings stress the significance of using machine learning and optimization techniques in AR-HUD interface design, with the aim of enhancing driver safety and occupational health. The study confirmed the practical implications of machine learning optimization algorithms for designing AR-HUD interfaces with reduced cognitive load and improved occupational safety and health (OSH) for professional drivers.
Abstract-This paper analyzed the definition of the integration of new technology and teacher education in the future education space station, and discusses the new technology and the depth of the integration of the teacher education connotation. And then from the perspective of process division dimension fusion of new technology and teacher education, build the future educational space station "new technology and teacher education integration mode is established. Finally, the scientific evaluation model sustained track record and evaluate the quality of the sample data.
Given the phenomenon those left-behind children are prone to learning disabilities and psychological problems because their parents work outside for a long time. This paper puts forward a design scheme for an educational intervention system for left-behind children with learning disabilities. The plan is to collect and analyze the data on left-behind children's learning status. According to the results of data analysis, it can predict the learning behavior, knowledge, and emotion of left-behind children. Early warning indicators and implementation rules of intervention strategies are designed in advance for the system. When the diagnosis result of left-behind children exceeds the warning index, corresponding intervention measures should be taken to solve the learning disabilities of left-behind children. Through the study of the educational intervention system for left-behind children with learning disabilities, the data on left-behind children's learning situation are collected, analyzed, and designed, and a new educational intervention system of intelligent and interactive learning is constructed.
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