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

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2018 IEEE Intelligent Vehicles Symposium, IV 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2009-2014
ページ数6
ISBN(電子版)9781538644522
DOI
出版ステータスPublished - 2018 10 18
イベント2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
継続期間: 2018 9 262018 9 30

出版物シリーズ

名前IEEE Intelligent Vehicles Symposium, Proceedings
2018-June

Other

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation

フィンガープリント 「Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル