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Occupant Injury Uncertainty Quantification with AI

30 Chapters

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Course overview

occupant injury risk assessment, uncertainty sources, and the role of AI in quantification.

occupantassessment

Injury biomechanics fundamentals

injury criteria (HIC, BrIC, CFC, etc.), tolerance limits, and their variability.

biomechanicscriteria

Uncertainty taxonomy in occupant safety

aleatoric vs. epistemic, parametric and model-form uncertainty in crash simulations.

Uncertaintytaxonomy

Data acquisition and preprocessing for injury UQ

experimental crash tests, human body models, and field data.

acquisitionpreprocessing

Probabilistic modeling of occupant response

random variables, distributions, and correlation structures for input parameters.

Probabilisticmodeling

Monte Carlo methods for injury uncertainty propagation

sampling strategies, convergence, and computational cost.

uncertaintypropagation

Surrogate modeling with AI

Gaussian processes, polynomial chaos expansions, and neural network surrogates for crash simulations.

Surrogatemodeling

Sensitivity analysis

global methods (Sobol, Morris) and AI-based feature importance for injury metrics.

Sensitivityanalysis

Bayesian inference for injury model calibration

priors, likelihoods, and posterior computation with MCMC.

Bayesianinference

AI-driven uncertainty quantification in human body models

emulators for finite element simulations.

AI-drivenuncertainty

Machine learning for injury risk prediction

classification and regression models with uncertainty estimates.

predictionclassification

Quantifying uncertainty in injury criteria

probabilistic HIC and BrIC, confidence intervals, and exceedance probabilities.

Quantifyinguncertainty

Reliability-based design optimization for occupant protection

incorporating UQ in restraint system design.

Reliability-basedoptimization

UQ for frontal impact injury prediction using AI surrogates and Monte Carlo.

predictionsurrogates

side impact injury uncertainty quantification with Bayesian neural networks.

uncertaintyquantification

pedestrian injury risk UQ using Gaussian process emulators.

pedestrianGaussian

Handling high-dimensional input spaces

dimension reduction (PCA, autoencoders) for UQ in crash models.

Handlinghigh-dimensional

Multi-fidelity AI surrogates

combining low- and high-fidelity simulations for efficient UQ.

Multi-fidelitysurrogates

Uncertainty quantification in real-time injury risk assessment

AI models for onboard systems.

Uncertaintyquantification

Validation and verification of AI-based UQ models

metrics, cross-validation, and confidence assessment.

Validationverification

Uncertainty propagation in coupled occupant-restraint models

AI-enhanced co-simulation.

Uncertaintypropagation

Quantifying model-form uncertainty

Bayesian model averaging and ensemble learning for injury predictions.

Quantifyingmodel-form

AI for inverse UQ

estimating occupant parameters from injury outcomes using deep learning.

estimatingoccupant

Probabilistic injury risk curves

construction with AI and UQ, and application in regulation.

Probabilisticconstruction

Robustness of AI models to noisy and missing data

imputation and uncertainty-aware training.

Robustnessimputation

Uncertainty quantification in injury biomechanics across populations

age, sex, and anthropometry.

Uncertaintyquantification

AI-based global sensitivity analysis for occupant protection system optimization.

AI-based global sensitivity analysis for occupant protection system optimization.

AI-basedsensitivity

Practical implementation

software tools (Python, UQLab, PyMC) and workflow for AI-driven UQ.

implementationsoftware

UQ for rear-seat occupant injury with limited data and transfer learning.

rear-seatoccupant

Future directions

integration of AI UQ with digital twins and real-world injury surveillance.

directionsintegration