A two degrees of freedom model–based optimization method for occupant restraint systems in vehicle crash

Zhang, Junyuan; Wang, Danqi; Ni, Yingying; Chen, Chao

doi:10.1007/s00158-019-02340-5

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They found that a combination of concave crash pulse and upward restraint stiffness was the best to maximize the restraining performance. Zhang et al (2019) developed a two-degrees-of-freedom model for optimizing occupant restraint systems [14]. Although these findings can give vehicle safety engineers general guidelines, it is difficult to draw THOR dummy-specific new knowledge using this model due to the ignored design aspects of restraint systems, which include knee bolster, belt route, seat stiffness, and so on, for simplification.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Occupant Restraint System Using Machine Learning for THOR-M50 and Euro NCAP

Heo,

Cho,

Kim

2024

Machines

View full text Add to dashboard Cite

In this study, we propose an optimization method for occupant protection systems using a machine learning technique. First, a crash simulation model was developed for a Euro NCAP MPDB frontal crash test condition. Second, a series of parametric simulations were performed using a THOR dummy model with varying occupant safety system design parameters, such as belt attachment locations, belt load limits, crash pulse, and so on. Third, metamodels were developed using neural networks to predict injury criteria for a given occupant safety system design. Fourth, the occupant safety system was optimized using metamodels, and the optimal design was verified using a subsequent crash simulation. Lastly, the effects of design variables on injury criteria were investigated using the Shapely method. The Euro NCAP score of the THOR dummy model was improved from 14.3 to 16 points. The main improvement resulted from a reduced risk of injury to the chest and leg regions. Higher D-ring and rearward anchor placements benefited the chest and leg regions, respectively, while a rear-loaded crash pulse was beneficial for both areas. The sensitivity analysis through the Shapley method quantitatively estimated the contribution of each design variable regarding improvements in injury metric values for the THOR dummy.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization of Occupant Restraint System Using Machine Learning for THOR-M50 and Euro NCAP

Heo,

Cho,

Kim

2024

Machines

View full text Add to dashboard Cite

show abstract

“…Therefore, matching the appropriate restraint system can effectively restrain the driver's motion attitude and reduce the injury. To improve the protection effect of the restraint system, Hirosuke et al [21] and Zhang et al [22], combined with the engineering optimization theories such as multi-objective optimization design, robustness optimization design, and reliability optimization design, established an optimization platform and conducted matching optimization analysis on the key parameters of the restraint system. NSGA-II is an improved version of the non-dominated sorting genetic algorithm (NSGA), which exhibits excellent exploration performance [23,24].…”

Section: Introductionmentioning

confidence: 99%

Optimization Design and Injury Analysis of Driver’s Restraint System in Sedan Small Offset Collision

Yang

Shi

et al. 2022

Processes

View full text Add to dashboard Cite

A combination of airbag, seatbelt, and other restraint systems greatly reduces injury to drivers in small offset collisions. However, the airbag causes accidental injury to the driver in the deployment process. To maximize the protection effect of the restraint system on the driver, this study proposes a pre-tensioned force-limiting seatbelt. A small offset collision accident with video information was simulated by using a Neon sedan and the THUMS (v.4.0.2) finite element model. The effectiveness of the accident model and the matching use of a pre-tensioned force-limiting seatbelt and airbag for driver protection were verified. To obtain the best parameter matching of protection effect, first, the seatbelt force-limiting A, pre-tensioned force B, pre-tensioned time C, airbag ignition time D, and mass flow coefficient E were selected as influencing factors, and orthogonal tests with different factor levels were designed. Then, the direct analysis method was applied to analyze the influence laws of each factor on driver dynamic response and injury. In addition, the radial basis function surrogate model was constructed by synthesizing each kind of critical injury value to the human body. Combined with NSGA-II multi-objective genetic algorithm, the structural performance parameters of the restraint system were optimized and matched. Results showed that the optimal protection matching parameters of the restraint system were 4933.5 N−2499.9 N−16 ms−15.3 ms−0.5 (A−B−C−D−E). Finally, the best matching parameters were input into the accident model for verification. After optimization, the WIC and Nij of drivers were reduced by 37.9% and 45.3%, respectively. The results show that the optimized restraint system can protect the driver the most.

show abstract

Driving fatigue detection based on fusion of EEG and vehicle motion information

Lan,

Zhao,

Liu

et al. 2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

A two degrees of freedom model–based optimization method for occupant restraint systems in vehicle crash

Cited by 4 publications

References 20 publications

Optimization of Occupant Restraint System Using Machine Learning for THOR-M50 and Euro NCAP

Optimization of Occupant Restraint System Using Machine Learning for THOR-M50 and Euro NCAP

Optimization Design and Injury Analysis of Driver’s Restraint System in Sedan Small Offset Collision

Driving fatigue detection based on fusion of EEG and vehicle motion information

Contact Info

Product

Resources

About