Optimization of restraint system parameters for reclined drivers in frontal collisions based on finite element modeling

Objective To address the increased injury risk for drivers in reclined postures during frontal collisions, this study aims to identify an optimal set of key restraint system parameters (including Belt Limit Force, Pretensioner Firing Time, and Airbag Mass Flow Coefficient) for a 30° reclined seating position. The goal is to minimize upper body injury criteria (the Head Injury Criterion HIC, the Neck Injury Criterion Nij, and the Chest Deflection Defchest) through a systematic optimization framework, thereby providing a quantitative design reference for restraint systems in autonomous driving scenarios.Methods This study constructed a finite element model of the driver restraint system by integrating the Hybrid III 50th percentile dummy, seatbelt, and airbag models, based on the finite element model of a certain type of sedan, and verified its validity. To simulate the reclined seating posture, the seatback angle was adjusted to 30°. Five restraint system parameters were first investigated through an orthogonal experimental design, and three key parameters were subsequently selected for response surface modeling and NSGA-II optimization. A second-order response surface model was established based on the experimental data, and the NSGA-II algorithm was combined to find the optimal parameters of the restraint system.Results The results show that the validation results showed good agreement between simulation and test responses, with CORA scores above 0.75 for all key channels. The optimal parameter combination is a belt limit force of 2000 N, a pretensioner firing time of 16.41 ms, and an airbag mass flow coefficient of 0.68. Compared with the initial parameters, the optimized restraint system parameters reduce the driver’s HIC by 22.73% (from 379.20 to 308.98), the Nij by 11.96% (from 0.4101 to 0.3663), and the Defchest by 32.37% (from 25.19 mm to 19.03 mm). The maximum deviation between the response surface model and the simulation results is 12.20%.Conclusion The model constructed in this study is effective and reliable, and the optimized parameters can significantly reduce the risk of upper body injuries for reclined drivers in frontal collisions. It provides a quantitative reference for the design of restraint systems for reclined postures in autonomous driving scenarios, lays a foundation for future research on safety protection for nontraditional postures, and is of great significance for improving the riding safety of intelligent vehicles.