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dc.contributor.advisorBras, Bert
dc.contributor.authorAmin, Ishit Bhadresh
dc.date.accessioned2021-06-10T16:48:16Z
dc.date.available2021-06-10T16:48:16Z
dc.date.created2020-05
dc.date.issued2020-01-09
dc.date.submittedMay 2020
dc.identifier.urihttp://hdl.handle.net/1853/64646
dc.description.abstractThe focus of this thesis is on using advanced driver-assistance system (ADAS) sensors available on newer year cars and applying them in a novel way to predict post-crash damage. This is split into three steps, first is to predict crush or intrusion on the exterior line using the accelerometer signal during impact. Next, using the array of parking sensors and the adaptive cruise control (ACC) radar, the impact location on the vehicle is predicted. Once the location and crush are determined, the final step is to combine the two to predict which parts are damaged on the car. To test this algorithm, publicly available crash test data is used for crush prediction and simulated ADAS data is used for location prediction. Parking sensor and ACC radar placement shows that location detection is more accurate for front and rear impacts than side impacts. Overall, there are more accurate finite element and lumped parameter models that can be used for crash prediction; however, this approach is easier to implement and does not require vehicle parameters only available to manufacturers.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectADAS
dc.subjectcrush
dc.subjectimpact
dc.subjectdamage
dc.titleVehicle Damage Prediction after Impact
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentMechanical Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberLeamy, Michael
dc.contributor.committeeMemberTorello , David
dc.date.updated2021-06-10T16:48:17Z


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