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Development of a driver's emotion detection model using auto-encoder on driving behavior and psychological data

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(3), pp.35-43
  • DOI : 10.9708/jksci.2023.28.03.035
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 14, 2023
  • Accepted : March 16, 2023
  • Published : March 31, 2023

Eun-Seo Jung 1 Seo-Hee Kim 1 Yun Jung Hong 1 In-Beom Yang 1 Jiyoung Woo 1

1순천향대학교

Accredited

ABSTRACT

Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.