Abstract

Objective The engagement of the lap belt with the pelvis is critical for occupant safety during vehicle frontal crashes to prevent occupant submarining. This study aims to develop a predictive model for submarining risk based on anthropometric parameters and lap belt positioning using finite element (FE) analyses. Methods FE analyses were conducted using human body models representing various body shapes (a 50th percentile male, low and high BMI males, and a 5th percentile female) in three seated postures (standard, reclined, and slouched). The lap belt-ASIS overlap and the belt-pelvis angle were used as key parameters for predicting submarining risk. A logistic regression analysis was utilized to correlate submarining occurrence with the initial values of these two parameters at the beginning of impact. Subsequently, this submarining prediction model was applied to computer tomography (CT) measurements of human subjects in different seated postures (upright, reclined, and slouched), and submarining risks were calculated based on the developed model. Results FE simulations indicated that submarining was more likely to occur as the initial belt-pelvis angle approached zero and there was a smaller initial belt-ASIS overlap. The logistic regression analysis demonstrated that the initial belt-pelvis angle and belt-ASIS overlap were statistically significant for predicting submarining risk. The derived model effectively distinguished submarining occurrence based on the initial values of these two parameters. The application of the submarining model to CT measurements of human subjects showed that submarining risk was lower in the order of upright, slouched, and reclined postures. In the reclined posture, the high submarining risk was attributed to a small belt-ASIS overlap and a rearward-tilted pelvis angle; whereas in the slouched posture, the risk was mostly associated with a rearward-tilted pelvis angle. Conclusions The submarining prediction model was developed based on the belt-pelvis angle and the belt-ASIS overlap. This predictive model may help to design restraint systems for various body types and seated postures of occupants.

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