探索应用智能算法增强AR-HUD视觉交互设计

IF 0.8 Q4 Computer Science

International Journal of Information Technologies and Systems Approach Pub Date : 2023-07-21 DOI:10.4018/ijitsa.326558

Jian Teng, Fucheng Wan, Yiquan Kong

{"title":"探索应用智能算法增强AR-HUD视觉交互设计","authors":"Jian Teng, Fucheng Wan, Yiquan Kong","doi":"10.4018/ijitsa.326558","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":52019,"journal":{"name":"International Journal of Information Technologies and Systems Approach","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent Algorithms\",\"authors\":\"Jian Teng, Fucheng Wan, Yiquan Kong\",\"doi\":\"10.4018/ijitsa.326558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":52019,\"journal\":{\"name\":\"International Journal of Information Technologies and Systems Approach\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2023-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information Technologies and Systems Approach\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijitsa.326558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Technologies and Systems Approach","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijitsa.326558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}

引用次数: 0

摘要

本研究旨在优化AR-HUD的视觉交互设计，减少复杂驾驶情况下的认知负荷。采用眼动追踪技术进行沉浸式驾驶仿真，分析客观生理指标，测量主观认知负荷。此外，将视觉认知负荷指数集成到BP-GA神经网络模型中进行负荷预测，从而推导出AR-HUD设计的最优解。优化后的AR-HUD界面显示，与之前的原型相比，认知负荷显著降低。试验组WP量表平均总分为25.63分，对照组为43.53分，显著提高41.4%。本研究提出了一种优化AR-HUD设计的创新方法，可有效降低复杂驾驶情况下的认知负荷。研究结果证明了所提出的算法在增强用户体验和性能方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent Algorithms

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Information Technologies and Systems Approach COMPUTER SCIENCE, INFORMATION SYSTEMS-

自引率

12.50%

发文量