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

Aug 3, 2026

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Augmented reality head-up display (AR-HUD) interfaces are crucial for professional drivers’ safety, yet optimizing them to reduce cognitive load remains challenging. This study proposes a novel IVPM-GA approach that integrates an IVPM method with a genetic algorithm and uses machine learning to predict cognitive load and iteratively optimize AR-HUD interface design. Experiments show IVPM-GA outperforms BP-GA, improves driving performance, and enhances user experience, with 80% of participants reporting visual comfort and reduced distraction, supporting occupational safety and health gains.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Background and significance

Motivation and contribution

Description of the proposed IVPM-GA method

Results

Conclusion

TYPE Original Research PUBLISHED 03 August 2023 DOI 10.3389/fpubh.2023.1195961 OPEN ACCESS EDITED BY Peishan Ning, Central South University, China REVIEWED BY Chaojie Fan, Central South University, China Yiming Wang, Nanjing University of Science and Technology, China Erfan Babaee, Mazandaran University of Science and Technology, Iran *CORRESPONDENCE Ju-Kyoung Kim [jkkim@sehan.ac.kr](jkkim@sehan.ac.kr) RECEIVED 29 March 2023 ACCEPTED 21 July 2023 PUBLISHED 03 August 2023 CITATION Teng J, Wan F, Kong Y and Kim J-K (2023) Machine learning-based cognitive load prediction model for AR-HUD to improve OSHof professional drivers. Front. Public Health 11:1195961 . doi: 10.3389/fpubh.2023.1195961 COPYRIGHT © 2023 Teng, Wan, Kong and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers Jian Teng 1, 2, Fucheng Wan 1, Yiquan Kong3 and Ju-Kyoung Kim 2* 1School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang, China, 2College of Education, Sehan University, Yeongam, Jeollanam-do, Republic of Korea, 3College of Computer and Intelligent Manufacturing, Lingnan Normal University, Zhanjiang, China Motivation: Augmented 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. Description: A novel method was proposed, combining the IVPM method with a GAto optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design. Results: Experimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPMGA 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. Conclusion: In this study, an innovative method was presented to optimize ARHUD 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. KEYWORDS AR-HUD interface design, OSH, cognitive load, machine learning, IVPM-GA 1. Introduction 1.1. Background and significance AR-HUD technology has become increasingly popular in the transportation industry over the past few years as an advanced driver assistance technology that is capable of improving OSH for professional drivers ( 1). AR-HUD technology is promising in providing drivers with critical information while minimizing visual distraction, improving safety and reducing cognitive load, Frontiers in Public Health 01 [frontiersin.org](frontiersin.org) which are recognized as vital factors for OSH (2) . AR-HUD technology offers several advantages for professional drivers, which covers real-

FAQs

What problem does the study address for professional drivers using AR-HUD?

The study targets the difficulty of optimizing AR-HUD interfaces to reduce cognitive load while improving driving safety and user experience for professional drivers.

How does the proposed IVPM-GA method work?

It combines an IVPM method with a genetic algorithm and uses machine learning to predict cognitive load, then iteratively optimizes the AR-HUD interface design.

What were the key findings from the experiments?

IVPM-GA showed superiority over the conventional BP-GA method, improved driving performance, and improved user experience, with 80% of participants rating the interface as visually comfortable and less distracting.

Aug 3, 2026

Upload by Patrick