Methodology for vehicle safety development and assessment accounting for occupant response variability to human and non-human factors. Issue 4 (29th June 2020)

• Record Type: Journal Article
• Authors: Perez-Rapela, Daniel; Forman, Jason L.; Huddleston, Samuel H.; Crandall, Jeff R.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 24:Issue 4(2021)
• Page Start: 384
• Page End: 399
• Publication Date: 2020-06-29
• Subjects: Response surface -- Monte Carlo analysis -- vehicle safety -- far-side impacts -- human body models; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2020.1830380
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: The use of standardized anthropomorphic test devices and test conditions prevent current vehicle development and safety assessments from capturing the breadth of variability inherent in real-world occupant responses. This study introduces a methodology that overcomes these limitations by enabling the assessment of occupant response while accounting for sources of human- and non-human-related variability. Although the methodology is generic in nature, this study explores the methodology in its application to human response in far-side motor vehicle crashes as an example. A total of 405 human body model simulations were conducted in a mid-sized sedan vehicle environment to iteratively train two neural networks to predict occupant head excursion and thoracic injury as a function of occupant anthropometry, impact direction and restraint configuration. The neural networks were utilized in Monte Carlo simulations to calculate the probability of head-to-intruding-door impacts and thoracic AIS 3+ as a function of the restraint configuration. This analysis indicated that the vehicle used in this study would lead to a range of 667 to 2, 448 head-to-intruding-door impacts and a range of 3, 041 to 3, 857 cases of thoracic AIS 3+ in the real world, depending on the seatbelt load limiter. These real-world results were later successfully validated using United States field data. This far-side assessment illustrates how the methodology incorporates the human and non-human variability, generates response surfaces that characterize the effects of the variability, and ultimately permits vehicle design considerations and injury predictions appropriate for real-world field conditions.