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Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

Niemeijer, Joshua and Schwonberg, Manuel and Termöhlen, Jan-Aike and Schäfer, Jörg P. and Schmidt, Nico M. and Gottschalk, Hanno and Fingscheidt, Tim (2023) Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving. IEEE Access. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/ACCESS.2023.3277785. ISSN 2169-3536.

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Abstract

Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. This task is termed unsupervised domain adaptation (UDA). While several different domain shifts challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. We present an overview of the current state of the art in this research field. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. We also go far beyond the description of the UDA state-of-the-art, as we present a quantitative comparison of approaches and point out the latest trends in this field. We conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions.

Item URL in elib:https://elib.dlr.de/198544/
Document Type:Article
Title:Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Niemeijer, JoshuaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schwonberg, ManuelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Termöhlen, Jan-AikeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schäfer, Jörg P.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schmidt, Nico M.CARIAD SE, 38440 WolfsburgUNSPECIFIEDUNSPECIFIED
Gottschalk, HannoInstitute of Mathematics, Technical University Berlin,UNSPECIFIEDUNSPECIFIED
Fingscheidt, TimUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:7 July 2023
Journal or Publication Title:IEEE Access
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1109/ACCESS.2023.3277785
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:2169-3536
Status:Published
Keywords:Computer Vision, Deep Neural Networks, Unsupervised Domain Adaptation, Semantic Segmentation, Automated Driving
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz
Location: Berlin-Adlershof , Braunschweig
Institutes and Institutions:Institute of Transportation Systems
Institute of Transportation Systems > Cooperative Systems, BS
Institute of Transportation Systems > Cooperative Systems, BA
Deposited By: Niemeijer, Joshua
Deposited On:05 Dec 2023 14:27
Last Modified:07 Mar 2024 11:26

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