Semi-Supervised Estimation of Driving Behaviors Using Robust Time-Contrastive Learning

Takuma Kuroki, Osamu Shouno, Junichiro Yoshimoto

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

Abstract

Estimation of driving behaviors is an elemental technology in a driving support system for a vehicle. For realizing intelligent estimation of driver behaviors, many studies have explored the use of machine learning methods mainly in a supervised fashion that require a large amount of labeled driving data. In this study, we hypothesize that the time-contrastive learning (TCL) could be helpful for reducing the number of labeled data for the supervised learning and numerically tested it using a public data set. For this purpose, we constructed three models to estimate driving behaviors from vehicle dynamics: 1) a naive linear classifier implemented by linear discriminant analysis (LDA) model; 2) an LDA classifier combined with a feature extraction process by the original TCL; 3) the same as 2) except the robust version of TCL was employed instead of the original TCL. The results were not supportive to our hypothesis: Model 1) showed better performance than the other models when very few labeled data was available; and two models with TCL outperformed the other without TCL for a considerable number of labeled data. We conclude discussions on some limitations of this study and open issues for the future.

Original languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1363-1366
Number of pages4
ISBN (Electronic)9789881476890
Publication statusPublished - 2021
Externally publishedYes
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: 14-12-202117-12-2021

Publication series

Name2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period14-12-2117-12-21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Instrumentation

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