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ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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CNKI
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etc
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Acceptance Rate:
19%
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500 USD
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Impact Factor 2022: 1.0
3.1
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CiteScore
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
2024-01-26
JAIT Vol. 14, No. 5 has been indexed by Scopus.
Home
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2020
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Volume 11, No. 1, February 2020
>
Driving Style Analysis Using Recurrent Neural Networks with LSTM Cells
Samuel Würtz and Ulrich Göhner
University of Applied Sciences Kempten, Germany
Abstract
—Many publications work on optimization of driving styles in motor vehicles. Most conclude that they can improve energy efficiency through training. In recent years the tools to address those problems evolved towards machine learning. To get appropriate data for learning algorithms we developed a method to judge a driving style with respect to energy efficiency. This approach leveraged handpicked criteria like acceleration extracted from GPS. Like related works, this method does not scale, since it requires substantial preprocessing. The goal of this evaluation was to reduce the resistance energy of a driven trip, while maintaining a natural traffic flow. This was accomplished by mimicking a low-pass filter on the speed profile. On top excessive speeding gets punished. It was possible to use our data with over 1 million kilometers for training a Recurrent Neural Network. In respect to the RNN the training data was used, to let it map the obtained function. The provided data was adjusted in different stages, until it was only the raw GPS data. The RNN learned to handle most GPS errors, only in initial phases the results are mixed. A RNN Network is well suited to handle GPS data and learn higher level features on its own. The result is a NN which judges the driving style using only raw GPS data.
Index Terms
—neural network, deep learning, RNN, LSTM, machine learning, GPS data, sensor data, driving style, driver behavior, intelligent vehicle control, energy efficiency, driving safety
Cite: Samuel Würtz and Ulrich Göhner, "Driving Style Analysis Using Recurrent Neural Networks with LSTM Cells," Journal of Advances in Information Technology, Vol. 11, No. 1, pp. 1-9, February 2020. doi: 10.12720/jait.11.1.1-9
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
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