Off-road terrain classification

dc.contributor.authorFritz, Lafras
dc.contributor.authorHamersma, Herman Adendorff
dc.contributor.authorBotha, T.R. (Theunis)
dc.contributor.emailhermanh@up.ac.zaen_US
dc.date.accessioned2023-11-08T09:03:13Z
dc.date.issued2023-04
dc.description.abstractRoad traffic accidents place a burden on the global economy. This impact is reduced by the development of safer vehicles. Advanced Driver Assist Systems (ADAS) aim to reduce the frequency and severity of accidents. ADASs are designed to operate in well-defined environments, such as first world urban areas. However, 93% of fatal accidents occur in developing countries; areas often without properly maintained roads. ADAS regularly fail to perform as intended in these challenging environments. Terrain classification may improve the performance of ADAS. A lot of research has been conducted on on-road terrain classification, but few studies focus on off-road terrain classification. This study classifies several off-road terrains, based on road roughness using the ISO8608:2016 standard, using a convolutional neural network (CNN). A database of images over different terrains with known road roughness was created using forward and downward facing cameras. Two different classification models were built: one is brand new and the other made use of transfer learning on pretrained model. Terrain data was captured on several on-road and off-road tracks. Results indicate that off-road terrain classification with cameras can be done with high accuracy before a vehicle drives over a specific part of a road.en_US
dc.description.departmentMechanical and Aeronautical Engineeringen_US
dc.description.embargo2024-12-08
dc.description.librarianhj2023en_US
dc.description.urihttp://www.elsevier.com/locate/jterraen_US
dc.identifier.citationFritz, L., Hamersma, H.A. & Botha, T.R. 2023, 'Off-road terrain classification', Journal of Terramechanics, vol. 106, pp. 1-11, doi : 10.1016/j.jterra.2022.11.003.en_US
dc.identifier.issn0022-4898 (print)
dc.identifier.issn1879-1204 (online)
dc.identifier.other10.1016/j.jterra.2022.11.003
dc.identifier.urihttp://hdl.handle.net/2263/93202
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2022 ISTVS. Published by Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Journal of Terramechanics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Journal of Terramechanics, vol. 106, pp. 1-11, 2023, doi : 10.1016/j.jterra.2022.11.003en_US
dc.subjectOff-road terrain classificationen_US
dc.subjectRoad profilesen_US
dc.subjectOff-road vehicle dynamicsen_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectSupervised learningen_US
dc.subjectImage dataen_US
dc.titleOff-road terrain classificationen_US
dc.typePostprint Articleen_US

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