Scholarlink

This paper presents a process for enhancing driver posture prediction in digital human modelling (DHM) tools by integrating real-world data from driver posture studies. The process leverages key data points such as seat position, steering wheel position, and eye point coordinates to position manikins and extract joint angles for ergonomic analysis. A test study involving 49 drivers was conducted, revealing significant variations in joint angles across different statures. These findings were used to develop stature-based strategies that demonstrated improved predictive accuracy for short and tall stature groups compared to the existing strategy in the DHM tool IPS IMMA. While the results highlight the potential benefits of this approach, limitations such as refined manikin body meshes, seat property considerations, and broader vehicle model validation are recommended. Overall, this method offers a promising solution for addressing incomplete datasets in occupant packaging studies, contributing to the development of more ergonomic and safer vehicle designs.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improving Occupant Packaging Posture Prediction Through Integration of Simulation and Real Data

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Estela Perez Luque,
  • Erik Brolin,
  • Pernilla Nurbo

摘要

This paper presents a process for enhancing driver posture prediction in digital human modelling (DHM) tools by integrating real-world data from driver posture studies. The process leverages key data points such as seat position, steering wheel position, and eye point coordinates to position manikins and extract joint angles for ergonomic analysis. A test study involving 49 drivers was conducted, revealing significant variations in joint angles across different statures. These findings were used to develop stature-based strategies that demonstrated improved predictive accuracy for short and tall stature groups compared to the existing strategy in the DHM tool IPS IMMA. While the results highlight the potential benefits of this approach, limitations such as refined manikin body meshes, seat property considerations, and broader vehicle model validation are recommended. Overall, this method offers a promising solution for addressing incomplete datasets in occupant packaging studies, contributing to the development of more ergonomic and safer vehicle designs.