Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

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dc.contributor.authorMuhammad, Khan
dc.contributor.authorHussain, Tanveer
dc.contributor.authorUllah, Hayat
dc.contributor.authorSer, Javier Del
dc.contributor.authorRezaei, Mahdi
dc.contributor.authorKumar, Neeraj
dc.contributor.authorHijji, Mohammad
dc.contributor.authorBellavista, Paolo
dc.contributor.authorDe Albuquerque, Victor Hugo C.
dc.contributor.institutionIA
dc.date.accessioned2024-09-10T14:00:03Z
dc.date.available2024-09-10T14:00:03Z
dc.date.issued2022-12-01
dc.descriptionPublisher Copyright: © 2000-2011 IEEE.
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.en
dc.description.statusPeer reviewed
dc.format.extent22
dc.identifier.citationMuhammad , K , Hussain , T , Ullah , H , Ser , J D , Rezaei , M , Kumar , N , Hijji , M , Bellavista , P & De Albuquerque , V H C 2022 , ' Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving : Recent Achievements, Challenges, and Outlooks ' , IEEE Transactions on Intelligent Transportation Systems , vol. 23 , no. 12 , pp. 22694-22715 . https://doi.org/10.1109/TITS.2022.3207665
dc.identifier.doi10.1109/TITS.2022.3207665
dc.identifier.issn1524-9050
dc.identifier.urihttps://hdl.handle.net/11556/5144
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85139852479&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systems
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsAutonomous driving
dc.subject.keywordsautonomous vehicles
dc.subject.keywordscontext prediction
dc.subject.keywordsdeep learning
dc.subject.keywordsscene understanding
dc.subject.keywordssemantic segmentation
dc.subject.keywordsAutomotive Engineering
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsComputer Science Applications
dc.titleVision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooksen
dc.typejournal article
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