Abstract
In the car industry, manikins are commonly used to evaluate the ergonomics and safety of vehicle interiors. However, they are often simplified and unrealistic for certain purposes. One major limitation is that manikins tend to be based on average human measurements which can diminish accuracy when assessing interaction. Although many large-scale 3D scanning campaigns have been conducted, the transfer of this information to automotive innovation tools is limited. To date, there is no direct methodology that introduces 3D scans of real people into the design software tools commonly used in the automotive sector.
This study presents a novel methodological approach to capturing, processing and generating digital human models for simulation. Relevant contributions include the design of a protocol for scanning people in postures that take autonomous driving use cases into consideration, as well as the development of advanced processing algorithms that solve the presence of occlusions due to the interaction between the body and seat. Finally, we have also created tools to mathematically transform and convert mesh objects into files to render them interoperable within design environments. Furthermore, the methodology has been validated using 12 participants representing different morphotypes of the target population.
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Valero, J. et al. (2023). Bridging the Gap Between Body Scanning and Ergonomic Simulation of Human Body Interaction in Autonomous Car Interiors. In: Scataglini, S., Harih, G., Saeys, W., Truijen, S. (eds) Advances in Digital Human Modeling . DHM 2023. Lecture Notes in Networks and Systems, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-031-37848-5_4
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