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

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dc.contributor.authorGurram, Shivaprasad
dc.contributor.authorReddy, Venkata Sai Kumar
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.examinerDavidsson, Johan
dc.contributor.supervisorIraeus, Johan
dc.contributor.supervisorJohn, Jobin
dc.contributor.supervisorLarsson, Karl-Johan
dc.date.accessioned2021-09-23T12:04:19Z
dc.date.available2021-09-23T12:04:19Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractThis 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.sv
dc.identifier.coursecodeMMSX30sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/304192
dc.language.isoengsv
dc.relation.ispartofseries2021:70sv
dc.setspec.uppsokTechnology
dc.subjectFEMsv
dc.subjectHuman body modelsv
dc.subjectHybrid III modelsv
dc.subjectSAFERsv
dc.subjectMachine learningsv
dc.subjectConvolutional Autoencodersv
dc.subjectNeural networkssv
dc.subjectPrincipal component analysissv
dc.subjectGaussian processsv
dc.subjectSurrogate modellingsv
dc.titleVehicle Occupant Kinematics prediction using Machine Learning: A study to understand applications of machine learning in prediction of vehicle occupant kinematics during crash and pre-crashsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeAutomotive engineering (MPAUT), MSc
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