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Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data
Driver’s hands on/off detection is very important in current autonomous vehicles for safety. Several studies have been conducted to create a precise algorithm. Although many studies have proposed various approaches, they have some limitations, such as robustness and reliability. Therefore, we propos...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920238/ https://www.ncbi.nlm.nih.gov/pubmed/36772481 http://dx.doi.org/10.3390/s23031442 |
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author | Pyeon, Hyeongoo Kim, Hanwul Kim, Rak Chul Oh, Geesung Lim, Sejoon |
author_facet | Pyeon, Hyeongoo Kim, Hanwul Kim, Rak Chul Oh, Geesung Lim, Sejoon |
author_sort | Pyeon, Hyeongoo |
collection | PubMed |
description | Driver’s hands on/off detection is very important in current autonomous vehicles for safety. Several studies have been conducted to create a precise algorithm. Although many studies have proposed various approaches, they have some limitations, such as robustness and reliability. Therefore, we propose a deep learning model that utilizes in-vehicle data. We also established a data collection system, which collects in-vehicle data that are auto-labeled for efficient and reliable data acquisition. For a robust system, we devised a confidence logic that prevents outliers’ sway. To evaluate our model in more detail, we suggested a new metric to explain the events, considering state transitions. In addition, we conducted an extensive experiment on the new drivers to demonstrate our model’s generalization ability. We verified that the proposed system achieved a better performance than in previous studies, by resolving their drawbacks. Our model detected hands on/off transitions in 0.37 s on average, with an accuracy of 95.7%. |
format | Online Article Text |
id | pubmed-9920238 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99202382023-02-12 Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data Pyeon, Hyeongoo Kim, Hanwul Kim, Rak Chul Oh, Geesung Lim, Sejoon Sensors (Basel) Article Driver’s hands on/off detection is very important in current autonomous vehicles for safety. Several studies have been conducted to create a precise algorithm. Although many studies have proposed various approaches, they have some limitations, such as robustness and reliability. Therefore, we propose a deep learning model that utilizes in-vehicle data. We also established a data collection system, which collects in-vehicle data that are auto-labeled for efficient and reliable data acquisition. For a robust system, we devised a confidence logic that prevents outliers’ sway. To evaluate our model in more detail, we suggested a new metric to explain the events, considering state transitions. In addition, we conducted an extensive experiment on the new drivers to demonstrate our model’s generalization ability. We verified that the proposed system achieved a better performance than in previous studies, by resolving their drawbacks. Our model detected hands on/off transitions in 0.37 s on average, with an accuracy of 95.7%. MDPI 2023-01-28 /pmc/articles/PMC9920238/ /pubmed/36772481 http://dx.doi.org/10.3390/s23031442 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pyeon, Hyeongoo Kim, Hanwul Kim, Rak Chul Oh, Geesung Lim, Sejoon Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data |
title | Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data |
title_full | Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data |
title_fullStr | Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data |
title_full_unstemmed | Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data |
title_short | Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data |
title_sort | deep learning-based driver’s hands on/off prediction system using in-vehicle data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920238/ https://www.ncbi.nlm.nih.gov/pubmed/36772481 http://dx.doi.org/10.3390/s23031442 |
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