Cyclist trajectory prediction using bidirectional recurrent neural networks Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2018, Cyclist trajectory prediction using bidirectional recurrent neural networks, in AI 2018 : Proceedings of the 31st Australian Joint Conference on Artificial Intelligence 2018, Springer, Cham, Switzerland, pp. 284-295, doi: 10.1007/978-3-030-03991-2_28. Attached Files Name Description MIMEType Size Downloads Title Cyclist trajectory prediction using bidirectional recurrent neural networks Author(s) Saleh, Khaled Hossny, Mohammed orcid.org/0000-0002-1593-6296 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name Australian Computer Society. Conference (31st : 2018 : Wellington, N.Z.) Conference location Wellington, N.Z. Conference dates 2018/12/11 - 2018/12/14 Title of proceedings AI 2018 : Proceedings of the 31st Australian Joint Conference on Artificial Intelligence 2018 Editor(s) Mitrovic, Tanja Xue, Bing Li, Xiaodong Publication date 2018 Start page 284 End page 295 Total pages 12 Publisher Springer Place of publication Cham, Switzerland Keyword(s) Cyclist trajectory prediction Bidirectional recurrent neural networks Vulnerable road users’ Cyclists ISBN 9783030039905 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-030-03991-2_28 Field of Research 080101 Adaptive Agents and Intelligent Robotics 080108 Neural, Evolutionary and Fuzzy Computation 080104 Computer Vision 08 Information and Computing Sciences Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2018, Springer Nature Switzerland AG Persistent URL http://hdl.handle.net/10536/DRO/DU:30122015 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 21 May 2019, 14:59:50 EST Wed, 22 May 2019, 05:01:06 EST Sat, 22 Jun 2019, 06:02:27 EST Thu, 27 Jun 2019, 15:49:41 EST Thu, 27 Jun 2019, 15:52:29 EST Thu, 27 Jun 2019, 15:53:17 EST Fri, 28 Jun 2019, 08:34:59 EST Sat, 09 Jan 2021, 01:34:21 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 247 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 21 May 2019, 14:59:50 EST

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