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dc.contributor.authorMuhammad, Khan
dc.contributor.authorHussain, Tanveer
dc.contributor.authorUllah, Hayat
dc.contributor.authorDel Ser Lorente, Javier ORCID
dc.contributor.authorRezaei, Mahdi
dc.contributor.authorKumar, Neeraj
dc.contributor.authorHijji, Mohammad
dc.contributor.authorBellavista, Paolo
dc.contributor.authorC. de Albuquerque, Victor Hugo
dc.date.accessioned2022-12-14T17:43:38Z
dc.date.available2022-12-14T17:43:38Z
dc.date.issued2022-12
dc.identifier.citationIEEE Transactions on Intelligent Transportation Systems 23(12) : 22694-22715 (2022)es_ES
dc.identifier.issn1524-9050
dc.identifier.issn1558-0016
dc.identifier.urihttp://hdl.handle.net/10810/58811
dc.description.abstractScene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research directions for scene understanding pursued by related studies include person/vehicle detection and segmentation, their transition analysis, lane change, and turns detection, among many others Unfortunately, these tasks seem insufficient to completely develop fully-autonomous vehicles i.e. achieving level-5 autonomy, travelling just like human-controlled cars. This latter statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. With this motivation, this survey defines, analyzes, and reviews the current achievements of the scene understanding research area that mostly rely on computationally complex deep learning models. Furthermore, it covers the generic scene understanding pipeline, investigates the performance reported by the state-of-the-art, informs about the time complexity analysis of avant garde modeling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The survey also includes a comprehensive discussion on the available datasets, and the challenges that, even if lately confronted by researchers, still remain open to date. Finally, our work outlines future research directions to welcome researchers and practitioners to this exciting domain.es_ES
dc.description.sponsorshipThis work was supported by the European Commission through European Union (EU) and Japan for Artificial Intelligence (AI) under Grant 957339.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/957339es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectfeature extractiones_ES
dc.subjectautomobileses_ES
dc.subjectvisualizationes_ES
dc.subjectsemanticses_ES
dc.subjectroadses_ES
dc.subjectimage color analysises_ES
dc.subjectcomputer architecturees_ES
dc.subjectautonomous drivinges_ES
dc.subjectautonomous vehicleses_ES
dc.subjectcontext predictiones_ES
dc.subjectdeep learninges_ES
dc.subjectscene understandinges_ES
dc.subjectsemantic segmentationes_ES
dc.titleVision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlookses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9913352es_ES
dc.identifier.doi10.1109/TITS.2022.3207665
dc.contributor.funderEuropean Commission
dc.departamentoesIngeniería de comunicacioneses_ES
dc.departamentoeuKomunikazioen ingeniaritzaes_ES


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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/