Spatiotemporal Prediction of Vehicle Movement Using Artificial Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00359444" target="_blank" >RIV/68407700:21240/22:00359444 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/IV51971.2022.9827089" target="_blank" >https://doi.org/10.1109/IV51971.2022.9827089</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IV51971.2022.9827089" target="_blank" >10.1109/IV51971.2022.9827089</a>
Alternative languages
Result language
angličtina
Original language name
Spatiotemporal Prediction of Vehicle Movement Using Artificial Neural Networks
Original language description
Prediction of the movement of all traffic participants is a very important task in autonomous driving. Well-predicted behavior of other cars and actors is crucial for safety. A sequence of bird’s-eye view artificially rasterized frames are used as input to neural networks which are trained to predict the future behavior of the participants. The Lyft Motion Prediction for Autonomous Vehicles dataset is explored and adapted for this task. We developed and applied a novel approach where the prediction problem is viewed as a problem of spatiotemporal prediction and we use methods based on convolutional recurrent neural networks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2022 IEEE Intelligent Vehicles Symposium (IV)
ISBN
978-1-6654-8821-1
ISSN
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e-ISSN
1931-0587
Number of pages
6
Pages from-to
734-739
Publisher name
IEEE
Place of publication
Piscataway
Event location
Aachen
Event date
Jun 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000854106700103