Vehicle Interior Space Optimization through Occupant Seating Layout Apportioning
Published July 10, 2017 by SAE International in United States
Annotation of this paper is available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
Digital human models (DHM) have greatly enhanced design for the automotive environment. The major advantage of the DHMs today is their ability to quickly test a broad range of the population within specific design parameters. The need to create expensive prototypes and run time consuming clinics can be significantly reduced. However, while the anthropometric databases within these models are comprehensive, the ability to position the manikin’s posture is limited and needs lot of optimization. This study enhances the occupant postures and their seating positions, in all instances the occupant was instructed to adjust to the vehicle parameters so they were in their most comfortable position. While all the Occupants are accommodated to their respective positions which finally can be stacked up for space assessments. This paper aims at simulating those scenarios for different percentiles / population which will further aid in decision making for critical parameters. Understanding the usage patterns of the seats by users will have huge impact on setting the target for overall vehicle length, including the luggage compartment. Although SAE J1517  and SAE J4004  recommends the practice of predicting the driver-selected seat position for population percentiles, but it is based on the US population. Premananth et al  suggests that a correction factor is required over prediction model of SAE J4004 and SAE J1517 to encompass the diversity found in India. Therefore, it becomes vital to examine the same scenarios either by user trials/clinics or by digital simulation for Indian population.
CitationMudavath, S., Dharmar, G., and Somani, S., "Vehicle Interior Space Optimization through Occupant Seating Layout Apportioning," SAE Technical Paper 2017-28-1923, 2017, https://doi.org/10.4271/2017-28-1923.
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