Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network

Udara E. Manawadu, Takahiro Kawano, Shingo Murata, Mitsuhiro Kamezaki, Junya Muramatsu, Shigeki Sugano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Human sensing enables intelligent vehicles to provide driver-adaptive support by classifying perceived workload into multiple levels. Objective of this study is to classify driver workload associated with traffic complexity into five levels. We conducted driving experiments in systematically varied traffic complexity levels in a simulator. We recorded driver physiological signals including electrocardiography, electrodermal activity, and electroencephalography. In addition, we integrated driver performance and subjective workload measures. Deep learning based models outperform statistical machine learning methods when dealing with dynamic time-series data with variable sequence lengths. We show that our long short-term memory based recurrent neural network model can classify driver perceived-workload into five classes with an accuracy of 74.5%. Since perceived workload differ between individual drivers for the same traffic situation, our results further highlight the significance of including driver characteristics such as driving style and workload sensitivity to achieve higher classification accuracy.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2009-2014
Number of pages6
Volume2018-June
ISBN (Electronic)9781538644522
DOIs
Publication statusPublished - 2018 Oct 18
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 2018 Sep 262018 Sep 30

Other

Other2018 IEEE Intelligent Vehicles Symposium, IV 2018
CountryChina
CityChangshu, Suzhou
Period18/9/2618/9/30

Fingerprint

Intelligent vehicle highway systems
Multi-class Classification
Memory Term
Recurrent neural networks
Recurrent Neural Networks
Electroencephalography
Electrocardiography
Workload
Driver
Learning systems
Time series
Simulators
Experiments
Traffic
Classify
Intelligent Vehicle
Statistical Learning
Time Series Data
Statistical Models
Long short-term memory

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation

Cite this

Manawadu, U. E., Kawano, T., Murata, S., Kamezaki, M., Muramatsu, J., & Sugano, S. (2018). Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network. In 2018 IEEE Intelligent Vehicles Symposium, IV 2018 (Vol. 2018-June, pp. 2009-2014). [8500410] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2018.8500410

Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network. / Manawadu, Udara E.; Kawano, Takahiro; Murata, Shingo; Kamezaki, Mitsuhiro; Muramatsu, Junya; Sugano, Shigeki.

2018 IEEE Intelligent Vehicles Symposium, IV 2018. Vol. 2018-June Institute of Electrical and Electronics Engineers Inc., 2018. p. 2009-2014 8500410.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Manawadu, UE, Kawano, T, Murata, S, Kamezaki, M, Muramatsu, J & Sugano, S 2018, Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network. in 2018 IEEE Intelligent Vehicles Symposium, IV 2018. vol. 2018-June, 8500410, Institute of Electrical and Electronics Engineers Inc., pp. 2009-2014, 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, 18/9/26. https://doi.org/10.1109/IVS.2018.8500410
Manawadu UE, Kawano T, Murata S, Kamezaki M, Muramatsu J, Sugano S. Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network. In 2018 IEEE Intelligent Vehicles Symposium, IV 2018. Vol. 2018-June. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2009-2014. 8500410 https://doi.org/10.1109/IVS.2018.8500410
Manawadu, Udara E. ; Kawano, Takahiro ; Murata, Shingo ; Kamezaki, Mitsuhiro ; Muramatsu, Junya ; Sugano, Shigeki. / Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network. 2018 IEEE Intelligent Vehicles Symposium, IV 2018. Vol. 2018-June Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2009-2014
@inproceedings{ab2ae62f94a547d6954d5f97cf9a2fe3,
title = "Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network",
abstract = "Human sensing enables intelligent vehicles to provide driver-adaptive support by classifying perceived workload into multiple levels. Objective of this study is to classify driver workload associated with traffic complexity into five levels. We conducted driving experiments in systematically varied traffic complexity levels in a simulator. We recorded driver physiological signals including electrocardiography, electrodermal activity, and electroencephalography. In addition, we integrated driver performance and subjective workload measures. Deep learning based models outperform statistical machine learning methods when dealing with dynamic time-series data with variable sequence lengths. We show that our long short-term memory based recurrent neural network model can classify driver perceived-workload into five classes with an accuracy of 74.5{\%}. Since perceived workload differ between individual drivers for the same traffic situation, our results further highlight the significance of including driver characteristics such as driving style and workload sensitivity to achieve higher classification accuracy.",
author = "Manawadu, {Udara E.} and Takahiro Kawano and Shingo Murata and Mitsuhiro Kamezaki and Junya Muramatsu and Shigeki Sugano",
year = "2018",
month = "10",
day = "18",
doi = "10.1109/IVS.2018.8500410",
language = "English",
volume = "2018-June",
pages = "2009--2014",
booktitle = "2018 IEEE Intelligent Vehicles Symposium, IV 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network

AU - Manawadu, Udara E.

AU - Kawano, Takahiro

AU - Murata, Shingo

AU - Kamezaki, Mitsuhiro

AU - Muramatsu, Junya

AU - Sugano, Shigeki

PY - 2018/10/18

Y1 - 2018/10/18

N2 - Human sensing enables intelligent vehicles to provide driver-adaptive support by classifying perceived workload into multiple levels. Objective of this study is to classify driver workload associated with traffic complexity into five levels. We conducted driving experiments in systematically varied traffic complexity levels in a simulator. We recorded driver physiological signals including electrocardiography, electrodermal activity, and electroencephalography. In addition, we integrated driver performance and subjective workload measures. Deep learning based models outperform statistical machine learning methods when dealing with dynamic time-series data with variable sequence lengths. We show that our long short-term memory based recurrent neural network model can classify driver perceived-workload into five classes with an accuracy of 74.5%. Since perceived workload differ between individual drivers for the same traffic situation, our results further highlight the significance of including driver characteristics such as driving style and workload sensitivity to achieve higher classification accuracy.

AB - Human sensing enables intelligent vehicles to provide driver-adaptive support by classifying perceived workload into multiple levels. Objective of this study is to classify driver workload associated with traffic complexity into five levels. We conducted driving experiments in systematically varied traffic complexity levels in a simulator. We recorded driver physiological signals including electrocardiography, electrodermal activity, and electroencephalography. In addition, we integrated driver performance and subjective workload measures. Deep learning based models outperform statistical machine learning methods when dealing with dynamic time-series data with variable sequence lengths. We show that our long short-term memory based recurrent neural network model can classify driver perceived-workload into five classes with an accuracy of 74.5%. Since perceived workload differ between individual drivers for the same traffic situation, our results further highlight the significance of including driver characteristics such as driving style and workload sensitivity to achieve higher classification accuracy.

UR - http://www.scopus.com/inward/record.url?scp=85056765504&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056765504&partnerID=8YFLogxK

U2 - 10.1109/IVS.2018.8500410

DO - 10.1109/IVS.2018.8500410

M3 - Conference contribution

AN - SCOPUS:85056765504

VL - 2018-June

SP - 2009

EP - 2014

BT - 2018 IEEE Intelligent Vehicles Symposium, IV 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -