53‐3: Dynamic Crosstalk Measurement for Augmented Reality 3D Head‐Up Display (AR 3D HUD) with Eye Tracking

Kang, Byongmin; Hwang, Chansol; Yanusik, Igor; Lee, Hui-Jin; Lee, Jun-Hwan; Lee, Jinho

doi:10.1002/sdtp.14788

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…In a limited time, participants should fully focus on the information of the entire driving scene. The scanning path length was significantly different from that of non-driving tasks, probably due to the different positions and distributions of environmental elements that attract participants' visual attention, which may reduce the effectiveness of scanning paths (43)(44)(45)(46)(47). In other words, the layout of AR-HUD visual elements may affect visual scanning strategies, such that drivers' cognitive load can be affected.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Teng

Wan

Kong

et al. 2023

Front. Public Health

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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.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Teng

Wan

Kong

et al. 2023

Front. Public Health

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“…Since the AR-HUD vision task belongs to the research category of three-dimensional space vision, there is an interactive relationship between fixation time, reaction time, and NASA-TLX cognitive load [28]. Therefore, the eyetracking data is transformed into a matrix.…”

Section: Visual Characteristics Analysis Of Ar-hudmentioning

confidence: 99%

Research on Optimization of AR-HUD Visual Interaction Design

Teng

2023

Preprint

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This study aims to explore AR-HUD(Augmented Reality-Head up Display) visual interaction cognitive load’s prediction algorithm model and obtain the best adaptation mode of AR-HUD interface visual Interaction Design. Through immersive driving simulation experiments, a driver assistance test system was established to analyze drivers’ eye movement behavior and visual resource allocation characteristics. The driver’s attention will be less focused on the driving task and correspondingly less on elements of the driving environment, negatively affecting the recovery of cognitive resources. The focus of this study is to establish a visual cognitive load index by combining the visual intensity model and the user’s subjective cognitive load evaluation of the interface. The AR-HUD visual Interaction Design coding and visual cognitive load index are used as the input and output layers to establish a visual cognitive load prediction neural network model. The neural network model was introduced into the genetic algorithm’s fitness function. The genetic algorithm was used to obtain the optimal AR-HUD Visual Interaction Design solution in the finite solution space. Then the optimal AR-HUD visual Interaction Design was obtained. The CH Scale scale was used to assess the validation of the algorithm’s soundness.

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“…Furthermore, they must be moved to measure dynamic crosstalk that occurs during facial motion. Such investigations of measurements have gathered recent attention, and there exist several proposals such as placing a camera behind the eyes of a printed face and moving them to measure it [5].…”

Section: Challengesmentioning

confidence: 99%

P‐158: Late‐News Poster: Dynamic Crosstalk Evaluation Method for Eye‐tracking based Autostereoscopic Display using a Virtual Image

Mitani,

Kato,

Nakahata

2023

Symp Digest of Tech Papers

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53‐3: Dynamic Crosstalk Measurement for Augmented Reality 3D Head‐Up Display (AR 3D HUD) with Eye Tracking

Cited by 3 publications

References 9 publications

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Research on Optimization of AR-HUD Visual Interaction Design

P‐158: Late‐News Poster: Dynamic Crosstalk Evaluation Method for Eye‐tracking based Autostereoscopic Display using a Virtual Image

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