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Crash Test and HBM Validation for AI-Assisted Simulation

30 Chapters
View Course Samples Online
01
Course overview
scope, concepts, and technical context of crash test and HBM validation for AI-assisted simulation.
technicalHBM
02
Crash test fundamentals
test types, instrumentation, data acquisition, and key metrics for vehicle safety assessment.
instrumentationacquisition
03
HBM validation fundamentals
human body model types, validation protocols, and correlation metrics against cadaver and volunteer data.
HBMvalidation
04
AI-assisted simulation landscape
surrogate modeling, machine learning for crashworthiness, and validation challenges.
AI-assistedsimulation
05
Regulatory and consumer rating frameworks
FMVSS, Euro NCAP, C-NCAP, and their impact on crash test and HBM validation requirements.
Regulatoryconsumer
06
Experimental design for crash tests
test matrix definition, boundary conditions, and repeatability considerations for AI training data.
Experimentaldefinition
07
HBM development and validation workflow
model construction, material calibration, and validation against PMHS and volunteer tests.
HBMdevelopment
08
Data acquisition and signal processing for crash tests
sensor selection, filtering, and synchronization for high-quality datasets.
acquisitionprocessing
09
AI model development for crash simulation
data preprocessing, feature engineering, and model selection for surrogate modeling.
developmentsimulation
10
Validation metrics and acceptance criteria
CORA, ISO/TS 18571, and statistical measures for comparing simulation and test results.
Validationacceptance
11
HBM validation for injury prediction
brain, thorax, and lower extremity injury criteria and their validation status.
HBMvalidation
12
Case study
frontal impact crash test and HBM validation for driver injury assessment.
HBMvalidation
13
Case study
side impact crash test and HBM validation for occupant protection.
HBMvalidation
14
Case study
pedestrian protection crash test and HBM validation for head and leg impact.
pedestrianprotection
15
AI-assisted crash simulation
building surrogate models from crash test data and HBM outputs.
AI-assistedsimulation
16
Uncertainty quantification in crash test and HBM validation
sources of variability and their impact on AI model reliability.
Uncertaintyquantification
17
Sensitivity analysis and parameter identification for HBM calibration using test data.
Sensitivity analysis and parameter identification for HBM calibration using test data.
Sensitivityanalysis
18
Machine learning for HBM validation
pattern recognition in validation metrics and automated correlation assessment.
HBMvalidation
19
Data management and curation for AI-assisted simulation
database structures, metadata standards, and quality control.
managementcuration
20
Verification and validation (V&V) framework for AI-assisted crash simulation
ASME V&V 40 and related standards.
Verificationvalidation
21
Practical exercise
processing crash test data and computing validation metrics for a given HBM.
exerciseprocessing
22
Practical exercise
training a surrogate model for crash pulse prediction and validating against test data.
exercisetraining
23
Practical exercise
HBM calibration using optimization and machine learning techniques.
exerciseHBM
24
Integration of HBM validation into AI-assisted simulation workflows
from test data to predictive models.
IntegrationHBM
25
Case study
AI-assisted optimization of vehicle structure using validated HBM and crash test data.
AI-assistedoptimization
26
Real-time crash simulation and HBM validation for autonomous vehicle safety assessment.
Real-time crash simulation and HBM validation for autonomous vehicle safety assessment.
Real-timesimulation
27
Emerging trends
digital twins, virtual testing, and AI-driven certification for crash safety.
EmergingAI-driven
28
Ethical and regulatory considerations for AI in crash safety
transparency, reproducibility, and acceptance.
regulatoryconsiderations
29
Project
end-to-end crash test and HBM validation for an AI-assisted simulation application.
end-to-endHBM
30
Future directions
advanced HBM, AI explainability, and integration with real-world safety systems.
directionsadvanced