DC ElementWertSprache
dc.contributor.authorTiedemann, Tim-
dc.contributor.authorSchwalb, Luk-
dc.contributor.authorKasten, Markus-
dc.contributor.authorGrotkasten, Robin-
dc.contributor.authorPareigis, Stephan-
dc.date.accessioned2022-08-05T09:58:32Z-
dc.date.available2022-08-05T09:58:32Z-
dc.date.issued2022-
dc.identifier.citationarticle number : 846355en_US
dc.identifier.issn1662-5218en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12738/13235-
dc.description.abstractArtificial Intelligence (AI) methods need to be evaluated thoroughly to ensure reliable behavior. In applications like autonomous driving, a complex environment with an uncountable number of different situations and conditions needs to be handled by a method whose behavior needs to be predictable. To accomplish this, simulations can be used as a first step. However, the physical world behaves differently, as the example of autonomous driving shows. There, erroneous behavior has been found in test drives that was not noticed in simulations. Errors were caused by conditions or situations that were not covered by the simulations (e.g., specific lighting conditions or other vehicle's behavior). However, the problem with real world testing of autonomous driving features is that critical conditions or situations occur very rarely-while the test effort is high. A solution can be the combination of physical world tests and simulations-and miniature vehicles as an intermediate step between both. With model cars (in a sufficiently complex model environment) advantages of both can be combined: (1) low test effort and a repeatable variation of conditions/situations as an advantage like in simulations and (2) (limited) physical world testing with unspecified and potentially unknown properties as an advantage like in real-world tests. Additionally, such physical tests can be carried out in less stable cases like already in the early stages of AI method testing and/or in approaches using online learning. Now, we propose to use a) miniature vehicles at a small scale of 1:87 and b) use sensors and computational power only on the vehicle itself. By this limitation, a further consequence is expected: Here, autonomy methods need to be optimized drastically or even redesigned from scratch. The resulting methods are supposed to be less complex-and, thus, again less error-prone. We call this approach "Miniature Autonomy" and apply it to the road, water, and aerial vehicles. In this article, we briefly describe a small test area we built (3 sqm.), a large test area used alternatively (1,545 sqm.), two last generation autonomous miniature vehicles (one road, one aerial vehicle), and an autonomous driving demo case demonstrating the application.en
dc.language.isoenen_US
dc.publisherFrontiers Research Foundationen_US
dc.relation.ispartofFrontiers in neuroroboticsen_US
dc.subjectautonomyen_US
dc.subjectevaluationen_US
dc.subjectmachine learningen_US
dc.subjectminiature autonomyen_US
dc.subjectphysical testsen_US
dc.subjectsimulationen_US
dc.subject.ddc620: Ingenieurwissenschaftenen_US
dc.titleMiniature autonomy as means to find new approaches in reliable autonomous driving AI method designen
dc.typeArticleen_US
local.contributorPerson.editorYamazaki-Skov, Ryuji-
tuhh.container.volume16en_US
tuhh.oai.showtrueen_US
tuhh.publication.instituteForschungs- und Transferzentrum Smart Systemsen_US
tuhh.publication.instituteDepartment Informatiken_US
tuhh.publication.instituteFakultät Technik und Informatiken_US
tuhh.publisher.doi10.3389/fnbot.2022.846355-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.rights.cchttps://creativecommons.org/licenses/by/4.0/en_US
dc.type.casraiJournal Article-
dc.type.diniarticle-
dc.type.driverarticle-
dc.type.statusinfo:eu-repo/semantics/publishedVersionen_US
dcterms.DCMITypeText-
tuhh.container.articlenumber846355-
item.creatorGNDTiedemann, Tim-
item.creatorGNDSchwalb, Luk-
item.creatorGNDKasten, Markus-
item.creatorGNDGrotkasten, Robin-
item.creatorGNDPareigis, Stephan-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorOrcidTiedemann, Tim-
item.creatorOrcidSchwalb, Luk-
item.creatorOrcidKasten, Markus-
item.creatorOrcidGrotkasten, Robin-
item.creatorOrcidPareigis, Stephan-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeArticle-
crisitem.author.deptDepartment Informatik-
crisitem.author.deptDepartment Informatik-
crisitem.author.orcid0000-0002-7238-0976-
crisitem.author.parentorgFakultät Technik und Informatik-
crisitem.author.parentorgFakultät Technik und Informatik-
Enthalten in den Sammlungen:Publications without full text
Zur Kurzanzeige

Seitenansichten

124
checked on 29.11.2024

Google ScholarTM

Prüfe

HAW Katalog

Prüfe

Volltext ergänzen

Feedback zu diesem Datensatz


Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons