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.

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Title Cyclist trajectory prediction using bidirectional recurrent neural networks
Author(s) Saleh, Khaled
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Nahavandi, SaeidORCID iD for 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: Centre for Intelligent Systems Research
GTP Research
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