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Analyzing dynamic postures of vehicle occupants in various situations is valuable for improving occupant accommodation and safety. Accurate tracking of an occupant’s head is of particular importance because the head has a large range of motion, controls gaze, and may require special protection in dynamic events including crashes. Previous vehicle occupant posture studies have primarily used marker-based optical motion capture systems or multiple video cameras for tracking facial features or markers on the head. However, the former approach has limitations for collecting on-road data, and the latter is limited by requiring intensive manual postprocessing to obtain suitable accuracy. This paper presents an automated on-road head tracking method using a single Microsoft Kinect V2 sensor, which uses a time-of-flight measurement principle to obtain a 3D point cloud representing objects in the scene at approximately 30 Hz.

Current finite element (FE) human body models (HBMs) generally only represent young and mid-size male occupants and do not account for body shape and composition variations among the population. Because it generally takes several years to build a whole-body HBM, a method to rapidly develop HBMs with a wide range of human attributes (size, age, obesity level, etc.) is critically needed. Therefore, the objective of this study was to evaluate the feasibility of using a mesh morphing method to rapidly generate skeleton and whole-body HBMs based on statistical geometry targets developed previously. THUMS V4.01 mid-size male model jointly developed by Toyota Motor Corporation and Toyota Central R&D Labs was used in this study as the baseline HBM to be morphed. Radial basis function (RBF) was used to morph the baseline model into the target geometries.