Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162460
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dc.contributor.authorYuhas, Michaelen_US
dc.contributor.authorEaswaran, Arvinden_US
dc.date.accessioned2023-01-30T04:29:29Z-
dc.date.available2023-01-30T04:29:29Z-
dc.date.issued2022-
dc.identifier.citationYuhas, M. & Easwaran, A. (2022). Demo abstract: real-time out-of-distribution detection on a mobile robot. IEEE Real-Time Systems Symposium - RTSS@Work 2022, 26-28.en_US
dc.identifier.urihttps://hdl.handle.net/10356/162460-
dc.description.abstractIn a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle’s operation. However, ML models are unlikely to make accurate decisions when presented with data outside their training distribution. Out-of-distribution (OOD) detection can act as a safety monitor for ML models by identifying such samples at run time. However, in safety critical systems like AVs, OOD detection needs to satisfy real-time constraints in addition to functional requirements. In this demonstration, we use a mobile robot as a surrogate for an AV and use an OOD detector to identify potentially hazardous samples. The robot navigates a miniature town using image data and a YOLO object detection network. We show that our OOD detector is capable of identifying OOD images in real-time on an embedded platform concurrently performing object detection and lane following. We also show that it can be used to successfully stop the vehicle in the presence of unknown, novel samples.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationMOE2019-T2-2-040en_US
dc.relation.uri10.21979/N9/INOCLVen_US
dc.rights© 2022 The Author(s). Published by RTSS. All rights reserved.This paper was published in Proceedings of IEEE Real-Time Systems Symposium - RTSS@Work 2022 and is made available with permission of The Author(s).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDemo abstract: real-time out-of-distribution detection on a mobile roboten_US
dc.typeConference Paperen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conferenceIEEE Real-Time Systems Symposium - RTSS@Work 2022en_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.description.versionSubmitted/Accepted versionen_US
dc.identifier.urlhttp://2022.rtss.org/call-for-demo/-
dc.identifier.spage26en_US
dc.identifier.epage28en_US
dc.subject.keywordsDuckietownen_US
dc.subject.keywordsOut-of-Distribution Detectionen_US
dc.citation.conferencelocationHouston, USAen_US
dc.description.acknowledgementThis research was funded in part by MOE, Singapore, Tier-2 grant number MOE2019-T2-2-040. This research is part of the programme DesCartes and is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.en_US
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SCSE Conference Papers
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