Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/162460
Title: | Demo abstract: real-time out-of-distribution detection on a mobile robot | Authors: | Yuhas, Michael Easwaran, Arvind |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2022 | Source: | Yuhas, 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. | Project: | MOE2019-T2-2-040 | Conference: | IEEE Real-Time Systems Symposium - RTSS@Work 2022 | Abstract: | In 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. | URI: | https://hdl.handle.net/10356/162460 | URL: | http://2022.rtss.org/call-for-demo/ | DOI (Related Dataset): | 10.21979/N9/INOCLV | Schools: | Interdisciplinary Graduate School (IGS) School of Computer Science and Engineering |
Research Centres: | Energy Research Institute @ NTU (ERI@N) | 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). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | ERI@N Conference Papers IGS Conference Papers SCSE Conference Papers |
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RTSS_Demo___Duckietown.pdf | 9 MB | Adobe PDF | View/Open |
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