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Lane following learning based on semantic segmentation with chroma key and image superposition

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorCorrochano Jiménez, Javier
dc.contributor.authorAlonso Weber, Juan Manuel
dc.contributor.authorSesmero Lorente, María Paz
dc.contributor.authorSanchis de Miguel, María Araceli
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2022-05-09T10:51:36Z
dc.date.available2022-05-09T10:51:36Z
dc.date.issued2021-12
dc.description.abstractThere are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.en
dc.description.sponsorshipThis work was supported by the Spanish Government under projects PID2019-104793RBC31/ AEI/10.13039/501100011033, RTI2018-096036-B-C22/AEI/10.13039/501100011033, TRA2016- 78886-C3-1-R/AEI/10.13039/501100011033, and PEAVAUTO-CM-UC3M and by the Region of Madrid’s Excellence Program (EPUC3M17).en
dc.format.extent24
dc.identifier.bibliographicCitationCorrochano, J., Alonso-Weber, J. M., Sesmero, M. P., & Sanchis, A. (2021). Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition. In Electronics (Vol. 10, Issue 24, p. 3113). MDPI AG.en
dc.identifier.doihttps://doi.org/10.3390/electronics10243113
dc.identifier.issn2079-9292
dc.identifier.publicationfirstpage3113
dc.identifier.publicationissue24
dc.identifier.publicationlastpage3137
dc.identifier.publicationtitleElectronics (Switzerland)en
dc.identifier.publicationvolume10
dc.identifier.urihttps://hdl.handle.net/10016/34737
dc.identifier.uxxiAR/0000030516
dc.language.isoengen
dc.publisherMDPI AGen
dc.relation.projectIDComunidad de Madrid. PEAVAUTO-CM-UC3Mes
dc.relation.projectIDGobierno de España. PID2019-104793RBC31/ AEI/10.13039/501100011033es
dc.relation.projectIDGobierno de España. TRA2016- 78886-C3-1-R/AEI/10.13039/501100011033es
dc.relation.projectIDGobierno de España. RTI2018-096036-B-C22/AEI/10.13039/501100011033es
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherAutomated drivingen
dc.subject.otherChroma keyen
dc.subject.otherFeature extractionen
dc.subject.otherImitation learningen
dc.subject.otherLane followingen
dc.subject.otherNoise additionen
dc.subject.otherSemantic segmentationen
dc.titleLane following learning based on semantic segmentation with chroma key and image superpositionen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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