The classifier algorithm for recognition of basic driving scenarios

Rafał Doniec; Szymon Sieciński; Natalia Piaseczna; Katarzyna Mocny-Pachońska; Marta Lang; Jacek Szymczyk

doi:10.1007/978-3-030-49666-1_28

The classifier algorithm for recognition of basic driving scenarios

Rafał Doniec, Szymon Sieciński^*, Natalia Piaseczna, Katarzyna Mocny-Pachońska, Marta Lang, Jacek Szymczyk

^*Corresponding author for this work

Institute of Medical Informatics

5 Citations (Scopus)

Abstract

This paper concerns three common but not recognizable driving practices based on sequences of signals from accelerometers, gyroscopes and electrooculograms obtained using state-of-the-art smart eyeglasses. As a result we obtained recordings from twenty volunteers with a set of flagged data assigned to specific actions taken while driving a car during driving lessons. Proposed method achieved a high level of accuracy based on data from smart eyeglasses, predicting appropriate activity classes in approximately 85% of cases without prior knowledge.

Original language	English
Title of host publication	Advances in Intelligent Systems and Computing
Number of pages	9
Volume	1186
Publisher	Springer, Cham
Publication date	03.09.2020
Pages	359-367
ISBN (Print)	978-3-030-49665-4
ISBN (Electronic)	978-3-030-49666-1
DOIs	https://doi.org/10.1007/978-3-030-49666-1_28
Publication status	Published - 03.09.2020
Event	8th International Conference on Information Technology in Biomedicine - Kamień Śląski, Poland Duration: 15.06.2020 → 17.06.2020

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10.1007/978-3-030-49666-1_28

Cite this

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title = "The classifier algorithm for recognition of basic driving scenarios",

abstract = "This paper concerns three common but not recognizable driving practices based on sequences of signals from accelerometers, gyroscopes and electrooculograms obtained using state-of-the-art smart eyeglasses. As a result we obtained recordings from twenty volunteers with a set of flagged data assigned to specific actions taken while driving a car during driving lessons. Proposed method achieved a high level of accuracy based on data from smart eyeglasses, predicting appropriate activity classes in approximately 85% of cases without prior knowledge.",

author = "Rafa{\l} Doniec and Szymon Sieci{\'n}ski and Natalia Piaseczna and Katarzyna Mocny-Pacho{\'n}ska and Marta Lang and Jacek Szymczyk",

note = "Publisher Copyright: {\textcopyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.; 8th International Conference on Information Technology in Biomedicine, ITIB 2020 ; Conference date: 15-06-2020 Through 17-06-2020",

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Doniec, R, Sieciński, S, Piaseczna, N, Mocny-Pachońska, K, Lang, M & Szymczyk, J 2020, The classifier algorithm for recognition of basic driving scenarios. in Advances in Intelligent Systems and Computing. vol. 1186, Advances in Intelligent Systems and Computing, vol. 1186, Springer, Cham, pp. 359-367, 8th International Conference on Information Technology in Biomedicine, Kamień Śląski, Poland, 15.06.20. https://doi.org/10.1007/978-3-030-49666-1_28

The classifier algorithm for recognition of basic driving scenarios. / Doniec, Rafał; Sieciński, Szymon; Piaseczna, Natalia et al.
Advances in Intelligent Systems and Computing. Vol. 1186 Springer, Cham, 2020. p. 359-367 (Advances in Intelligent Systems and Computing; Vol. 1186).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

T1 - The classifier algorithm for recognition of basic driving scenarios

AU - Doniec, Rafał

AU - Sieciński, Szymon

AU - Piaseczna, Natalia

AU - Mocny-Pachońska, Katarzyna

AU - Lang, Marta

AU - Szymczyk, Jacek

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

PY - 2020/9/3

Y1 - 2020/9/3

N2 - This paper concerns three common but not recognizable driving practices based on sequences of signals from accelerometers, gyroscopes and electrooculograms obtained using state-of-the-art smart eyeglasses. As a result we obtained recordings from twenty volunteers with a set of flagged data assigned to specific actions taken while driving a car during driving lessons. Proposed method achieved a high level of accuracy based on data from smart eyeglasses, predicting appropriate activity classes in approximately 85% of cases without prior knowledge.

AB - This paper concerns three common but not recognizable driving practices based on sequences of signals from accelerometers, gyroscopes and electrooculograms obtained using state-of-the-art smart eyeglasses. As a result we obtained recordings from twenty volunteers with a set of flagged data assigned to specific actions taken while driving a car during driving lessons. Proposed method achieved a high level of accuracy based on data from smart eyeglasses, predicting appropriate activity classes in approximately 85% of cases without prior knowledge.

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U2 - 10.1007/978-3-030-49666-1_28

DO - 10.1007/978-3-030-49666-1_28

M3 - Conference contribution

AN - SCOPUS:85090999587

SN - 978-3-030-49665-4

VL - 1186

T3 - Advances in Intelligent Systems and Computing

SP - 359

EP - 367

BT - Advances in Intelligent Systems and Computing

PB - Springer, Cham

T2 - 8th International Conference on Information Technology in Biomedicine

Y2 - 15 June 2020 through 17 June 2020

ER -

The classifier algorithm for recognition of basic driving scenarios

Abstract

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