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dc.contributor.advisorJensen, Matthew
dc.contributor.authorPerson, Michael
dc.date.accessioned2018-05-04T14:47:54Z
dc.date.available2018-05-04T14:47:54Z
dc.date.created2018-05
dc.date.issued2018-04
dc.date.submittedMay 2018
dc.identifier.urihttp://hdl.handle.net/11141/2438
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2018en_US
dc.description.abstractIn order for autonomous vehicles to safely navigate the road ways, accurate object detection must take place before safe path planning can occur. Currently there is a gap between models that are fast enough and models that are accurate enough for deployment. We propose Multimodal Fusion Detection System (MDFS), a sensor fusion system that combines the speed of a fast image detection CNN model along with the accuracy of a LiDAR point cloud data through a decision tree approach. The primary objective is to bridge the trade-off between performance and accuracy. The motivation for MDFS is to reduce the computational complexity associated with using a CNN model to extract features from an image. To improve efficiency, MDFS extracts complimentary features from the LIDAR point cloud in order to obtain comparable detection accuracy. MFDS achieves 3.7% higher accuracy than the base CNN detection model and is able to operate at 10 Hz. Additionally, the memory requirement for MFDS is small enough to fit on the Nvidia Tx1 when deployed on an embedded device.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCC BY 4.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleMultimodal Fusion Detection System for Autonomous Vehiclesen_US
dc.typeThesisen_US
dc.date.updated2018-04-26T14:27:06Z
thesis.degree.nameMaster of Science in Mechanical Engineeringen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineMechanical Engineeringen_US
thesis.degree.departmentMechanical and Aerospace Engineeringen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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