DIT4BEARs Smart Roads Internship
The research internship at UiT - The Arctic University of Norway was offered for our team being the winner of the 'Smart Roads - Winter Road Maintenance 2021' Hackathon. The internship commenced on 3 May 2021 and ended on 21 May 2021 with meetings happening twice each week. In spite of hav...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2107.06755 https://arxiv.org/abs/2107.06755 |
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ftdatacite:10.48550/arxiv.2107.06755 2023-05-15T18:49:26+02:00 DIT4BEARs Smart Roads Internship Jahin, Md. Abrar Krutsylo, Andrii 2021 https://dx.doi.org/10.48550/arxiv.2107.06755 https://arxiv.org/abs/2107.06755 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Machine Learning cs.LG FOS Computer and information sciences Article CreativeWork article Preprint 2021 ftdatacite https://doi.org/10.48550/arxiv.2107.06755 2022-03-10T13:58:39Z The research internship at UiT - The Arctic University of Norway was offered for our team being the winner of the 'Smart Roads - Winter Road Maintenance 2021' Hackathon. The internship commenced on 3 May 2021 and ended on 21 May 2021 with meetings happening twice each week. In spite of having different nationalities and educational backgrounds, we both interns tried to collaborate as a team as much as possible. The most alluring part was working on this project made us realize the critical conditions faced by the arctic people, where it was hard to gain such a unique experience from our residence. We developed and implemented several deep learning models to classify the states (dry, moist, wet, icy, snowy, slushy). Depending upon the best model, the weather forecast app will predict the state taking the Ta, Tsurf, Height, Speed, Water, etc. into consideration. The crucial part was to define a safety metric which is the product of the accident rates based on friction and the accident rates based on states. We developed a regressor that will predict the safety metric depending upon the state obtained from the classifier and the friction obtained from the sensor data. A pathfinding algorithm has been designed using the sensor data, open street map data, weather data. : 6 pages Article in Journal/Newspaper Arctic University of Norway UiT The Arctic University of Norway DataCite Metadata Store (German National Library of Science and Technology) Arctic Norway |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
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unknown |
topic |
Machine Learning cs.LG FOS Computer and information sciences |
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Machine Learning cs.LG FOS Computer and information sciences Jahin, Md. Abrar Krutsylo, Andrii DIT4BEARs Smart Roads Internship |
topic_facet |
Machine Learning cs.LG FOS Computer and information sciences |
description |
The research internship at UiT - The Arctic University of Norway was offered for our team being the winner of the 'Smart Roads - Winter Road Maintenance 2021' Hackathon. The internship commenced on 3 May 2021 and ended on 21 May 2021 with meetings happening twice each week. In spite of having different nationalities and educational backgrounds, we both interns tried to collaborate as a team as much as possible. The most alluring part was working on this project made us realize the critical conditions faced by the arctic people, where it was hard to gain such a unique experience from our residence. We developed and implemented several deep learning models to classify the states (dry, moist, wet, icy, snowy, slushy). Depending upon the best model, the weather forecast app will predict the state taking the Ta, Tsurf, Height, Speed, Water, etc. into consideration. The crucial part was to define a safety metric which is the product of the accident rates based on friction and the accident rates based on states. We developed a regressor that will predict the safety metric depending upon the state obtained from the classifier and the friction obtained from the sensor data. A pathfinding algorithm has been designed using the sensor data, open street map data, weather data. : 6 pages |
format |
Article in Journal/Newspaper |
author |
Jahin, Md. Abrar Krutsylo, Andrii |
author_facet |
Jahin, Md. Abrar Krutsylo, Andrii |
author_sort |
Jahin, Md. Abrar |
title |
DIT4BEARs Smart Roads Internship |
title_short |
DIT4BEARs Smart Roads Internship |
title_full |
DIT4BEARs Smart Roads Internship |
title_fullStr |
DIT4BEARs Smart Roads Internship |
title_full_unstemmed |
DIT4BEARs Smart Roads Internship |
title_sort |
dit4bears smart roads internship |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2107.06755 https://arxiv.org/abs/2107.06755 |
geographic |
Arctic Norway |
geographic_facet |
Arctic Norway |
genre |
Arctic University of Norway UiT The Arctic University of Norway |
genre_facet |
Arctic University of Norway UiT The Arctic University of Norway |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2107.06755 |
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1766243024420995072 |