Predicting autonomous vehicle navigation parameters via image and image-and-point cloud fusion-based end-to-end methods

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dc.contributor.authorBeycimen, Semih
dc.contributor.authorIgnatyev, Dmitry
dc.contributor.authorZolotas, Argyrios
dc.date.accessioned2022-12-13T14:18:37Z
dc.date.available2022-12-13T14:18:37Z
dc.date.issued2022-10-13
dc.description.abstractThis paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. Image-based and Image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained Image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.en_UK
dc.identifier.citationBeycimen S, Ignatyev D, Zolotas A. (2022) Predicting autonomous vehicle navigation parameters via image and image-and-point cloud fusion-based end-to-end methods. In: 2022 IEEE International Conference on Multisensor Fusion and Integration (MFI 2022), 20-22 September 2022, Cranfield, UKen_UK
dc.identifier.eisbn978-1-6654-6026-2
dc.identifier.isbn978-1-6654-6027-9
dc.identifier.urihttps://doi.org/10.1109/MFI55806.2022.9913844
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18794
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titlePredicting autonomous vehicle navigation parameters via image and image-and-point cloud fusion-based end-to-end methodsen_UK
dc.typeConference paperen_UK

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