TY - GEN
T1 - Semi-Supervised Estimation of Driving Behaviors Using Robust Time-Contrastive Learning
AU - Kuroki, Takuma
AU - Shouno, Osamu
AU - Yoshimoto, Junichiro
N1 - Publisher Copyright:
© 2021 APSIPA.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85126680846
T3 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
SP - 1363
EP - 1366
BT - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Y2 - 14 December 2021 through 17 December 2021
ER -