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The dimensions of an automobile seat are important factors affecting a driver’s seating comfort, fit, and satisfaction. In this regard, seat engineers put forth tremendous efforts to evaluate the dimensions of a product seat until the dimensions are consistent with the design reference in a computer aided design (CAD). However, the existing evaluation process is heavily reliant on seat engineers’ manual tasks which are highly repetitive, labor intensive, and time-demanding tasks. The objective of this study is to develop an automated system that can efficiently and accurately evaluate seat products by comparing estimated seat dimensions from a CAD model or a 3D scan model. By using the developed system, the evaluation time for comparing 18 seat dimensions on CAD and scan models has been substantially reduced to less than one minute, which is 99% time saving compared to two hours in the manual process.

Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. Motion sickness could be mitigated through control of the vehicle motion dynamics, design of the interior environment, and other interventions. However, a lack of a definitive etiology of motion sickness challenges the design of automated vehicles (AVs) to address motion sickness susceptibility effectively. Few motion sickness studies have been conducted in naturalistic road-vehicle environments; instead, most research has been performed in driving simulators or on motion platforms that produce prescribed motion profiles. To address this gap, a vehicle-based experimental platform using a midsize sedan was developed to quantify motion sickness in road vehicles. A scripted, continuous drive consisting of a series of frequent 90-degree turns, braking, and lane changes were conducted on a closed track.

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.

Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures.

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).