Methodology for vehicle safety development and assessment accounting for occupant response variability to human and non-human factors

Perez-Rapela, Daniel; Forman, Jason; Huddleston, Samuel H.; Crandall, Jeff R.

doi:10.1080/10255842.2020.1830380

Cited by 11 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it cannot be known if the rib fracture prediction obtained from a single HBM representing some subpopulation average is an over- or underestimation; there is a need for more knowledge about the injury distribution in order to make informed design choices. A recent study presented a methodology to compute distributions of probabilistic rib fracture risks through HBM simulations in a far-side crash scenario ( Perez-Rapela et al, 2021 ). In that study, a response surface (i.e., a meta-model) was created to predict HBM rib fracture risk based on six parameters describing human, crash, and safety system variability.…”

Section: Introductionmentioning

confidence: 99%

Influences of human thorax variability on population rib fracture risk prediction using human body models

Larsson

Iraeus

Holcombe

et al. 2023

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Rib fractures remain a common injury for vehicle occupants in crashes. The risk of a human sustaining rib fractures from thorax loading is highly variable, potentially due to a variability in individual factors such as material properties and geometry of the ribs and ribcage. Human body models (HBMs) with a detailed ribcage can be used as occupant substitutes to aid in the prediction of rib injury risk at the tissue level in crash analysis. To improve this capability, model parametrization can be used to represent human variability in simulation studies. The aim of this study was to identify the variations in the physical properties of the human thorax that have the most influence on rib fracture risk for the population of vehicle occupants. A total of 15 different geometrical and material factors, sourced from published literature, were varied in a parametrized SAFER HBM. Parametric sensitivity analyses were conducted for two crash configurations, frontal and near-side impacts. The results show that variability in rib cortical bone thickness, rib cortical bone material properties, and rib cross-sectional width had the greatest influence on the risk for an occupant to sustain two or more fractured ribs in both impacts. Therefore, it is recommended that these three parameters be included in rib fracture risk analysis with HBMs for the population of vehicle occupants.

show abstract

Section: Introductionmentioning

confidence: 99%

Influences of human thorax variability on population rib fracture risk prediction using human body models

Larsson

Iraeus

Holcombe

et al. 2023

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…By contrast, computational alternatives using Finite Element (FE) analysis provide the means to represent the complex morphology and non-linear material response of the human body. Commonly known as FE Human Body Models (FE-HBM), not only are these models capable of matching human responses ( Wu et al, 2017 ; Chen et al, 2018 ) but also have the potential to model omnidirectional responses and predict injury at the tissue levels ( Iraeus and Lindquist, 2014 ; Perez-Rapela et al, 2021b ). The capabilities of HBMs open avenues to evaluate safety in non-traditional cases ( Katagiri et al, 2016 ; Perez-Rapela et al, 2019 ) and future seating configurations ( Rawska et al, 2020 , Rawska et al, 2021 ; Östh et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Hello, world! VIVA+: A human body model lineup to evaluate sex-differences in crash protection

John

Klug

Kranjec

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Finite element Human Body Models are increasingly becoming vital tools for injury assessment and are expected to play an important role in virtual vehicle safety testing. With the aim of realizing models to study sex-differences seen in the injury- and fatality-risks from epidemiology, we developed models that represent an average female and an average male. The models were developed with an objective to allow tissue-based skeletal injury assessment, and thus non-skeletal organs and joints were defined with simplified characterizations to enhance computational efficiency and robustness. The model lineup comprises female and male representations of (seated) vehicle occupants and (standing) vulnerable road users, enabling the safety assessment of broader segments of the road user population. In addition, a new workflow utilized in the model development is presented. In this workflow, one model (the seated female) served as the base model while all the other models were generated as closely-linked derivative models, differing only in terms of node coordinates and mass distribution. This approach opens new possibilities to develop and maintain further models as part of the model lineup, representing different types of road users to reflect the ongoing transitions in mobility patterns (like bicyclists and e-scooter users). In this paper, we evaluate the kinetic and kinematic responses of the occupant and standing models to blunt impacts, mainly on the torso, in different directions (front, lateral, and back). The front and lateral impacts to the thorax showed responses comparable to the experiments, while the back impact varied with the location of impact (T1 and T8). Abdomen bar impact showed a stiffer load-deflection response at higher intrusions beyond 40 mm, because of simplified representation of internal organs. The lateral shoulder impact responses were also slightly stiffer, presumably from the simplified shoulder joint definition. This paper is the first in a series describing the development and validation of the new Human Body Model lineup, VIVA+. With the inclusion of an average-sized female model as a standard model in the lineup, we seek to foster an equitable injury evaluation in future virtual safety assessments.

show abstract

“…Another approach was proposed in Adam and Untaroiu (2011) and Untaroiu and Adam (2013) where first a classification of pre-crash occupant postures was performed and a genetic algorithm was then used to optimize the restraint system for the different classes. In Perez-Rapela et al (2020) neural networks in combination with Monte Carlo simulations are used to account for occupant response variability in the assessment of safety systems. An overview of design optimization for structural crashworthiness can be found in Fang et al (2017).…”

Section: Introductionmentioning

confidence: 99%

“…This scenario catalogue tends to be the load cases tested in regulations or consumer information testing. However, ideally these scenario catalogues should cover a wide range of scenarios to finally design a robust restraint system and not to miss potentially critical scenarios, likely to happen in the field (Perez-Rapela et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The question arises in this context, of how we can, with a limited number of simulations, focus on the critical areas of the design space if these are unknown when starting the simulation study. This is particularly challenging, as the criticality is determined by a combination of intrinsic and extrinsic factors as well as the applied safety system (Perez-Rapela et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Approach for machine learning based design of experiments for occupant simulation

Schneider

Kofler²,

D’Addetta³

et al. 2022

Front. Future Transp.

View full text Add to dashboard Cite

The complexity of crash scenarios in the context of vehicle safety is steadily increasing. This is especially the case on the way to mixed traffic challenges with non-automated and automated driving vehicles. The number of simulations required to design a robust restraint system is thus also increasing. The vast range of possible scenarios here is causing a huge parameter space. Simultaneously biofidelic simulation models are resulting in very high computational costs and therefore the number of simulations should be limited to a feasible operational range. In this study, a machine-learning based design of experiments algorithm is developed, which specifically addresses the issues when designing a safety system with a limited number of simulation samples taking diversity of the occupant and accident scenario into account. In contrast to an optimization task, where the aim is to meet a target function, our job has been to find the critical load case combinations to make sure that these are addressed and not missed. A combination of a space-filling approach and a metamodel has been established to find the critical scenarios in order to improve the system for those cases. It focuses specifically on the areas that are difficult to predict by the metamodel. The developed method was applied to iteratively generate a simulation matrix of a total of 208 simulations with a generic interior model and a detailed FE human body model. Kinematic and strain-based injury metrics were used as simulation output. These were used to train the metamodels after each iteration and derive the simulation matrix for the next iteration. In this paper we present a method that allows the training of a robust metamodel for the prediction of injury criteria, considering both varying load cases and varying restraint system parameters for individual anthropometries and seating postures. Based on that, restraint systems or metamodels can be optimized to achieve the best overall performance for a huge variety of possible scenarios with a specific focus on critical scenarios.

show abstract

Methodology for vehicle safety development and assessment accounting for occupant response variability to human and non-human factors

Cited by 11 publications

References 20 publications

Influences of human thorax variability on population rib fracture risk prediction using human body models

Influences of human thorax variability on population rib fracture risk prediction using human body models

Hello, world! VIVA+: A human body model lineup to evaluate sex-differences in crash protection

Approach for machine learning based design of experiments for occupant simulation

Contact Info

Product

Resources

About