Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2020, Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users, IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 8, pp. 3398-3408, doi: 10.1109/tits.2019.2927770. Attached Files Name Description MIMEType Size Downloads Title Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users Author(s) Saleh, Khaled Hossny, Mohammed orcid.org/0000-0002-1593-6296 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name IEEE Transactions on Intelligent Transportation Systems Volume number 21 Issue number 8 Start page 3398 End page 3408 Total pages 11 Publisher IEEE Place of publication Piscataway, NJ Publication date 2020-08 ISSN 1524-9050 1558-0016 Keyword(s) Trajectory Predictive models Roads Reinforcement learning Recurrent neural networks Forecasting Kalman filters Intent prediction vulnerable road users (VRU) autonomous vehicles motion trajectory forecasting reinforcement learning (IRL) Science & Technology Technology Engineering, Civil Engineering, Electrical & Electronic Transportation Science & Technology Engineering Transportation Language eng DOI 10.1109/tits.2019.2927770 Indigenous content off Field of Research 0801 Artificial Intelligence and Image Processing 0905 Civil Engineering 1507 Transportation and Freight Services HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2019, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30142030 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version (restricted access) 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 Wed, 16 Sep 2020, 15:04:15 EST Thu, 17 Sep 2020, 05:02:11 EST Thu, 17 Sep 2020, 07:31:22 EST Sat, 03 Oct 2020, 06:04:35 EST Mon, 05 Oct 2020, 14:42:02 EST Mon, 05 Oct 2020, 14:44:02 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 49 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 16 Sep 2020, 15:04:15 EST

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