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

Scene 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 ve...

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Autores: Muhammad, Khan, Hussain, Tanveer, Ullah, Hayat, Del Ser Lorente, Javier, Rezaei, Mahdi, Kumar, Neeraj, Hijji, Mohammad, Bellavista, Paolo, C. de Albuquerque, Victor Hugo
Tipo de documento: artigo
Data de publicação:2022
País:España
Recursos:Universidad del País Vasco
Repositório:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/58811
Acesso em linha:http://hdl.handle.net/10810/58811
Access Level:Acceso aberto
Palavra-chave:feature extraction
automobiles
visualization
semantics
roads
image color analysis
computer architecture
autonomous driving
autonomous vehicles
context prediction
deep learning
scene understanding
semantic segmentation
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spelling Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and OutlooksMuhammad, KhanHussain, TanveerUllah, HayatDel Ser Lorente, JavierRezaei, MahdiKumar, NeerajHijji, MohammadBellavista, PaoloC. de Albuquerque, Victor Hugofeature extractionautomobilesvisualizationsemanticsroadsimage color analysiscomputer architectureautonomous drivingautonomous vehiclescontext predictiondeep learningscene understandingsemantic segmentationScene 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.This work was supported by the European Commission through European Union (EU) and Japan for Artificial Intelligence (AI) under Grant 957339.IEEEEuropean Commission202220222022info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/58811reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/EC/H2020/957339https://ieeexplore.ieee.org/document/9913352info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/Atribución 3.0 Españaoai:addi.ehu.eus:10810/588112026-06-18T09:23:17Z
dc.title.none.fl_str_mv Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
title Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
spellingShingle Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
Muhammad, Khan
feature extraction
automobiles
visualization
semantics
roads
image color analysis
computer architecture
autonomous driving
autonomous vehicles
context prediction
deep learning
scene understanding
semantic segmentation
title_short Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
title_full Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
title_fullStr Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
title_full_unstemmed Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
title_sort Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
dc.creator.none.fl_str_mv Muhammad, Khan
Hussain, Tanveer
Ullah, Hayat
Del Ser Lorente, Javier
Rezaei, Mahdi
Kumar, Neeraj
Hijji, Mohammad
Bellavista, Paolo
C. de Albuquerque, Victor Hugo
author Muhammad, Khan
author_facet Muhammad, Khan
Hussain, Tanveer
Ullah, Hayat
Del Ser Lorente, Javier
Rezaei, Mahdi
Kumar, Neeraj
Hijji, Mohammad
Bellavista, Paolo
C. de Albuquerque, Victor Hugo
author_role author
author2 Hussain, Tanveer
Ullah, Hayat
Del Ser Lorente, Javier
Rezaei, Mahdi
Kumar, Neeraj
Hijji, Mohammad
Bellavista, Paolo
C. de Albuquerque, Victor Hugo
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
dc.subject.none.fl_str_mv feature extraction
automobiles
visualization
semantics
roads
image color analysis
computer architecture
autonomous driving
autonomous vehicles
context prediction
deep learning
scene understanding
semantic segmentation
topic feature extraction
automobiles
visualization
semantics
roads
image color analysis
computer architecture
autonomous driving
autonomous vehicles
context prediction
deep learning
scene understanding
semantic segmentation
description Scene 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/58811
url http://hdl.handle.net/10810/58811
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/EC/H2020/957339
https://ieeexplore.ieee.org/document/9913352
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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