Sensor Modelling with Recurrent Conditional GANs

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dc.contributor.authorArnelid, Henrik
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Physics (Chalmers)en
dc.date.accessioned2019-07-03T14:55:20Z
dc.date.available2019-07-03T14:55:20Z
dc.date.issued2018
dc.description.abstractAutonomous vehicles rely on many sensors in order for the vehicles to perceive their surroundings. Consequently it is important with safety verification of the sensors which typically is done by collecting data in many different scenarios which is time consuming and expensive. For this reason, autonomous driving software companies are interested in virtual verification where the scenarios are simulated. In this thesis we have developed and used the Recurrent Conditional Generative Adversarial Network (RCGAN) in order to model the longitudinal error of sensors. The RCGAN is a modification of the original generative adversarial network (GAN) framework which makes use of recurrent neural networks and conditioning the networks on auxiliary information. These changes allows the model to learn and be able to generate realistic real-valued multi-dimensional time series.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256175
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectFysik
dc.subjectPhysical Sciences
dc.titleSensor Modelling with Recurrent Conditional GANs
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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