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Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).ORCID iD: 0000-0001-5998-9640
2019 (English)In: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com) , 2019, p. 113-118Conference paper, Published paper (Refereed)
Abstract [en]

Predicting future vehicle behaviour is an essential task to enable safe and situation-aware automated driving. In this paper, we propose to encapsulate spatial information of multiple objects in a semantic vector-representation. Assuming that future vehicle motion is influenced not only by past positions but also by the behaviour of other traffic participants, we use this representation as input for a Long Short-Term Memory (LSTM) network for sequence to sequence prediction of vehicle positions. We train and evaluate our system on real-world driving data collected mainly on highways in southern Germany and compare it to other models for reference.

Place, publisher, year, edition, pages
ESANN (i6doc.com) , 2019. p. 113-118
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-258187Scopus ID: 2-s2.0-85071314700ISBN: 9782875870650 (print)OAI: oai:DiVA.org:kth-258187DiVA, id: diva2:1349874
Conference
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019; Bruges; Belgium; 24 April 2019 through 26 April 2019
Note

QC 20190910

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10Bibliographically approved

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Conradt, Jörg

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf