<div class="csl-bib-body">
<div class="csl-entry">Wei, S., Pfeffer, P., & Edelmann, J. (2023). State of the Art: Ongoing Research in Assessment Methods for Lane Keeping Assistance Systems. <i>IEEE Transactions on Intelligent Vehicles</i>. https://doi.org/10.34726/4241</div>
</div>
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dc.identifier.issn
2379-8858
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/177399
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dc.identifier.uri
https://doi.org/10.34726/4241
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dc.description.abstract
A safe, comfortable and intelligent Lane Keeping Assistance System (LKAS) is fundamental on the way to realizing full automated driving. In spite of the current high market penetration of LKAS, vehicle manufacturers, suppliers and researchers are still working on improving the system to reach a higher customer acceptance. To achieve this goal more efficiently, a systematic and ideally objective assessment procedure of the system needs to be established. Till date, a thorough review study of the assessment procedures of LKAS does not exist, especially of those which also take the subjective impression of the driver into consideration. The motivation of this paper is to benchmark the state-of-the-art assessment methods of LKAS and identify where problems and research potentials lie. The paper summarizes the relevant characteristics of LKAS based on representative research in the past decades. It also compares the characterization of LKAS with the field of steering feel and vehicle handling in the aspects of procedure, strategy, as well as their advantages and disadvantages. This paper contributes to transferring the know-hows and valuable experiences from other well-developed fields of vehicle dynamics into the rapid-evolving branch of Advanced Driver Assistance System (ADAS), in order to help standardizing its assessment procedure and give insights on system design from a human-centered perspective in the early development phase, thus shortening the ADAS development cycles.
en
dc.language.iso
en
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dc.publisher
IEEE
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dc.relation.ispartof
IEEE Transactions on Intelligent Vehicles
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Accidents
en
dc.subject
ADAS
en
dc.subject
Automation
en
dc.subject
Intelligent vehicles
en
dc.subject
LKAS
en
dc.subject
Vehicle dynamics
en
dc.title
State of the Art: Ongoing Research in Assessment Methods for Lane Keeping Assistance Systems
en
dc.type
Article
en
dc.type
Artikel
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/4241
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dc.contributor.affiliation
TU Wien, Austria
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dc.contributor.affiliation
Munich University of Applied Sciences, Germany
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dc.rights.holder
IEEE is not the copyright holder of this material.
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dc.type.category
Review Article
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tuw.journal.peerreviewed
true
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tuw.peerreviewed
true
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wb.publication.intCoWork
International Co-publication
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tuw.researchTopic.id
E2
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tuw.researchTopic.id
C6
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tuw.researchTopic.name
Sustainable and Low Emission Mobility
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.value
70
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tuw.researchTopic.value
30
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dcterms.isPartOf.title
IEEE Transactions on Intelligent Vehicles
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tuw.publication.orgunit
E325-01 - Forschungsbereich Technische Dynamik und Fahrzeugdynamik
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tuw.publication.orgunit
E325 - Institut für Mechanik und Mechatronik
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tuw.publisher.doi
10.1109/TIV.2023.3269156
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dc.date.onlinefirst
2023-04-21
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dc.identifier.eissn
2379-8904
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dc.identifier.libraryid
AC17203340
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dc.description.numberOfPages
28
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tuw.author.orcid
0000-0002-7026-4170
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dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
wb.sci
true
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wb.sciencebranch
Maschinenbau
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wb.sciencebranch.oefos
2030
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100
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open
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http://purl.org/coar/resource_type/c_dcae04bc
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item.openaccessfulltext
Open Access
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item.openairetype
review article
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Publications
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item.fulltext
with Fulltext
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application/pdf
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item.languageiso639-1
en
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crisitem.author.dept
TU Wien
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crisitem.author.dept
Munich University of Applied Sciences, Germany
-
crisitem.author.dept
E325-01 - Forschungsbereich Technische Dynamik und Fahrzeugdynamik