Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2018, Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning, in DICTA 2018 : Digital Image Computing: Techniques and Applications (DICTA), IEEE, Piscataway, N.J., doi: 10.1109/DICTA.2018.8615854. Attached Files Name Description MIMEType Size Downloads Title Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning Author(s) Saleh, Khaled Hossny, Mohammed orcid.org/0000-0002-1593-6296 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name Digital Image Computing: Techniques and Applications. International Conference (2018 : Canberra, A.C.T.) Conference location Canberra, A.C.T. Conference dates 2018/12/10 - 2018/12/13 Title of proceedings DICTA 2018 : Digital Image Computing: Techniques and Applications (DICTA) Publication date 2018 Total pages 8 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) Science & Technology Technology Life Sciences & Biomedicine Engineering, Electrical & Electronic Imaging Science & Photographic Technology Radiology, Nuclear Medicine & Medical Imaging Engineering ISBN 9781538666029 Language eng DOI 10.1109/DICTA.2018.8615854 Field of Research 080101 Adaptive Agents and Intelligent Robotics 080108 Neural, Evolutionary and Fuzzy Computation 080104 Computer Vision Socio Economic Objective 810104 Emerging Defence Technologies HERDC Research category E1 Full written paper - refereed Copyright notice ©2018, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30122012 Document type: Conference Paper Collections: Centre for Intelligent Systems Research 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, 14:45:26 EST Tue, 21 May 2019, 14:46:20 EST Wed, 22 May 2019, 05:01:00 EST Mon, 24 Jun 2019, 10:52:30 EST Tue, 25 Jun 2019, 06:03:02 EST Thu, 04 Jul 2019, 09:40:34 EST Thu, 04 Jul 2019, 09:42:01 EST Thu, 04 Jul 2019, 09:43:13 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 35 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 24 Jun 2019, 10:52:30 EST

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