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Physics-Informed Machine Learning for Occupant Safety

30 Chapters

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Occupant safety landscape

crash injury mechanisms, regulatory and consumer-test protocols, and the role of simulation and data-driven models.

Occupantlandscape

Physics of occupant protection

kinematics, restraint loading, energy absorption, and injury criteria (HIC, chest deflection, Nij, etc.) as constraints for learning.

occupantprotection

Finite element occupant models

LS-DYNA/PAM-CRASH model architecture, material models, contacts, and the computational cost of high-fidelity simulations.

occupantLS-DYNA/PAM-CRASH

Data generation for occupant safety ML

design of experiments, sampling strategies, simulation campaigns, and handling of noisy or sparse physical test data.

generationoccupant

Surrogate modeling with physics constraints

Gaussian processes, polynomial chaos, and neural networks for crashworthiness response prediction.

Surrogatemodeling

Physics-informed neural networks (PINNs) for occupant dynamics

formulating ODE/PDE residuals for lumped-mass and multibody occupant models.

Physics-informednetworks

Hybrid physics-ML approaches

coupling reduced-order models with neural networks for real-time restraint system optimization.

physics-MLapproaches

Injury risk prediction with physics-informed features

incorporating kinematic and kinetic invariants into classification and regression models.

predictionphysics-informed

Uncertainty quantification in occupant safety ML

Bayesian neural networks, ensemble methods, and propagation of material and loading uncertainties.

Uncertaintyquantification

Interpretable ML for occupant safety

SHAP, sensitivity analysis, and physics-consistent feature attribution for design decisions.

Interpretableoccupant

Restraint system optimization

using physics-informed surrogates for airbag, seatbelt, and seat design under crash pulses.

Restraintoptimization

Occupant classification and pose estimation

computer vision and sensor fusion with physics-based constraints for adaptive restraint deployment.

Occupantclassification

Real-time injury prediction for autonomous vehicles

physics-informed recurrent models for pre-crash and in-crash scenarios.

Real-timeprediction

Digital twin of the occupant

combining biomechanical models with online learning for personalized safety systems.

occupantcombining

Validation and verification of physics-informed ML models

metrics, cross-validation with physical tests, and regulatory acceptance.

Validationverification

Data-efficient learning with physics-based priors

transfer learning, multi-fidelity modeling, and active learning for crash data.

Data-efficientphysics-based

Graph neural networks for occupant safety

representing human body models and contact networks with physics-informed message passing.

networksoccupant

Physics-informed reinforcement learning for adaptive restraint control

reward shaping with injury criteria and safety constraints.

Physics-informedreinforcement

Generative models for occupant safety

physics-constrained VAEs and GANs for synthetic injury data and scenario augmentation.

Generativeoccupant

Multiscale modeling

linking tissue-level injury criteria to whole-body kinematics using physics-informed machine learning.

Multiscalemodeling

frontal impact occupant protection—building a PINN surrogate for chest deflection and optimizing restraint parameters.

occupantprotection

side impact and far-side occupant—physics-informed ML for injury risk under lateral loading.

far-sideoccupant

rear impact and whiplash—hybrid models for neck injury prediction and seat design.

whiplashprediction

child occupant safety—physics-informed scaling laws and ML for CRS performance.

occupantphysics-informed

pedestrian and cyclist protection—physics-informed ML for head injury prediction from vehicle design.

pedestrianprotection

Deployment of physics-informed ML in CAE workflows

integration with LS-DYNA, model export, and real-time hardware-in-the-loop.

Deploymentphysics-informed

Regulatory and ethical considerations

model transparency, certification, and bias in occupant safety ML.

Regulatoryconsiderations

Emerging trends

foundation models for crash simulation, differentiable physics, and quantum-assisted optimization.

Emergingfoundation

develop a physics-informed surrogate for a simplified occupant model and validate against simulation data.

physics-informedsurrogate

end-to-end physics-informed ML pipeline for a real-world occupant safety problem, from data to deployment.

Capstoneend-to-end