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<p>In-vehicle human-machine interface (HMI) is vital for driving safety and user experience. However, as traditional vehicles evolve into intelligent vehicles, their interaction forms, functions, and modes have undergone profound transformation. Based on VOSviewer bibliometric software, this study systematically analyzes 438 publications over the past two decades following the PRISMA guidelines, aiming to identify research hotspots and development trends in the field of in-vehicle HMI design. The results highlight the critical role of human factors in advancing in-vehicle HMI design. Three future research directions are identified: quantitative analysis and threshold determination of human-machine interaction information in intelligent connected environments; adaptive interaction cognitive mechanism and personalized design; and dynamic evolution and cross-situational transmission of trust (e.g., quantitative information thresholds, adaptive cognitive load modeling, and trust calibration mechanisms). These findings provide a systematic understanding of the field and offer theoretical references for future in-vehicle HMI optimization, human factors-based safety evaluation, and adaptive interaction system development, thereby accelerating the innovative development and practical application of intelligent vehicles.</p>

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A Bibliometric Review of Human Factors-Based In-Vehicle Human-Machine Interface Design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

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  • Ya Gao,
  • Quan Yuan,
  • Ruhai Jiang,
  • Tao Gu,
  • Jingyu Li,
  • Kai She,
  • Baoru Gong

摘要

In-vehicle human-machine interface (HMI) is vital for driving safety and user experience. However, as traditional vehicles evolve into intelligent vehicles, their interaction forms, functions, and modes have undergone profound transformation. Based on VOSviewer bibliometric software, this study systematically analyzes 438 publications over the past two decades following the PRISMA guidelines, aiming to identify research hotspots and development trends in the field of in-vehicle HMI design. The results highlight the critical role of human factors in advancing in-vehicle HMI design. Three future research directions are identified: quantitative analysis and threshold determination of human-machine interaction information in intelligent connected environments; adaptive interaction cognitive mechanism and personalized design; and dynamic evolution and cross-situational transmission of trust (e.g., quantitative information thresholds, adaptive cognitive load modeling, and trust calibration mechanisms). These findings provide a systematic understanding of the field and offer theoretical references for future in-vehicle HMI optimization, human factors-based safety evaluation, and adaptive interaction system development, thereby accelerating the innovative development and practical application of intelligent vehicles.