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This paper reports on the development and validation of an automated seat-dimension extraction system that can efficiently and reliably measure SAE J2732 (2008) seat dimensions from 3D seat scan data. The automated dimension-extraction process consists of four phases: (1) import 3D seat scan data along with seat reference information such as H-point location, back and cushion angles, (2) calculate centerline and lateral cross-section lines on the imported 3D seat scan data, (3) identify landmarks on the centerline and cross-section lines based on the SAE J2732 definitions, and (4) measure seat-dimensions using the identified landmarks. To validate the automated seat measurements, manually measured dimensions in a computer-aided-design (CAD) environment and automatically extracted ones in the current system were compared in terms of mean discrepancy and intra- and inter-observer standard deviations (SD).

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.