Virtual evaluation of automotive safety with variation in occupant posture and shoulder belt fit is gaining importance, and there is a need of methods facilitating analysis of occupant postures in driving studies. This study is aimed to develop an AI-based computer vision method to automatically quantify occupant posture and shoulder belt position over time in a car. Traceable defined key points on the occupant were related with the shoulder belt and quantified over time in real 3D coordinates by predefined key measurements, utilising the underlying spatial information of a Intel RealSense 3D Camera. The key points are defined as traceable key points relevant to relate the occupant to the vehicle environment and to estimate shoulder belt position. Key point prediction results suggest an average deviation of around 1cm per coordinate, which enable a reliable spatial categorization of the respective tracked occupant by analyzing the key measurements. This method providing continuous information of the occupant position and belt fit will be useful to identify common occupant postures as well as more extreme postures, to be used for expanding variations in postures for vehicle safety assessments.

中文翻译：

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使用安全特定关键点自动量化乘员姿势和肩带适合度

随着乘员姿势和肩带贴合度的变化对汽车安全性进行虚拟评估变得越来越重要，并且需要一种方法来促进驾驶研究中乘员姿势的分析。本研究旨在开发一种基于人工智能的计算机视觉方法，以自动量化车内乘员的姿势和肩带位置。利用英特尔实感 3D 摄像头的基础空间信息，通过预定义的关键测量值在真实 3D 坐标中随时间推移对乘员的可追踪定义关键点进行量化。关键点被定义为与将乘员与车辆环境相关联并估计肩带位置相关的可追踪关键点。关键点预测结果表明每个坐标的平均偏差约为 1cm，通过分析关键测量值，可以对各个被跟踪的乘员进行可靠的空间分类。这种提供乘员位置和安全带配合的连续信息的方法将有助于识别常见的乘员姿势以及更极端的姿势，用于扩大车辆安全评估的姿势变化。