Vehicle Occupant Kinematics prediction using Machine Learning: A study to understand applications of machine learning in prediction of vehicle occupant kinematics during crash and pre-crash
Typ
Examensarbete för masterexamen
Program
Automotive engineering (MPAUT), MSc
Publicerad
2021
Författare
Gurram, Shivaprasad
Reddy, Venkata Sai Kumar
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This work presents surrogate models using deep learning and statistical techniques
for predictive modeling of occupant kinematics in crash scenarios. Finite Element
Methods (FEM) involving discretization of Partial Differential Equations (PDEs)
are widely used for vehicle crash and injury analysis involving Finite Element human
body models (FE-HBMs). Doing a parametric study of pre-cash and in-crash
scenarios involving a complex FE-HBM model depicting a detailed representation
of the human anatomy is computationally expensive and time consuming, mainly
due to the long pre-crash event. This thesis aims at evaluating machine learning
models along with dimensionality reduction tools as the inexpensive surrogates for
determining the kinematics of the vehicle occupants.
A FE model of the Hybrid III crash test dummy was used to obtain nodal displacement
outputs of the dummy for crash scenarios which in turn are the outputs
predicted from the surrogates. These outputs from FE simulations form a large data
set in high dimensional format. Performing machine learning with this large data
set can become complex. This thesis utilizes one-dimensional convolutional autoencoder
(AE-1D) and Principle component analysis (PCA) to obtain a compressed
version of the original data. Supervised learning is used to train the surrogates on
this low dimensional space with a set of crash parameters acting as inputs. Gaussian
process regression (GPR), Feedforward neural networks (FFNN) and Random forest
regression (RFR) were used as the machine learning models to build the surrogates.
LS-Dyna FE solver was used to generate the sample results for a set of crash parameters.
We use in-crash kinematics instead of pre-crash due to the computational
limitations in simulating the required samples. For the same reason, a complex FE
Human body model such as SAFER or THUMS model was not used.
This thesis provides an understanding of using statistical and neural network based
dimensionality reduction methods paired with different ML models on the accuracy
of the kinematics predictions. The accuracy of the predictions varies considerably
depending on the use of PCA or AE-1D for dimensionality reduction and also the
sample size. PCA and AE-1D have similar performance in compressing and decompressing
the original data. AE-1D paired with either FFNN or Gaussian process
provided the most accurate predictions in most sample sizes.
Beskrivning
Ämne/nyckelord
FEM , Human body model , Hybrid III model , SAFER , Machine learning , Convolutional Autoencoder , Neural networks , Principal component analysis , Gaussian process , Surrogate modelling