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Predicting Traffic Accident Risk based on Driver Abnormal Behavior and Gaze

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(8), pp.1-9
  • DOI : 10.9708/jksci.2024.29.08.001
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : June 11, 2024
  • Accepted : July 24, 2024
  • Published : August 30, 2024

Ji-Woong Yang 1 Hyeon-Jin Jung 2 Han-Jin Lee 2 Tae-Wook Kim 3 Ellen J. Hong 2

1한양대학교
2연세대학교
3연세대학교(미래캠퍼스)

Accredited

ABSTRACT

In this paper, we propose a new approach by analyzing driver behavior and gaze changes within the vehicle in real-time to assess and predict the risk of traffic accidents. Utilizing data analysis and machine learning algorithms, this research precisely measures drivers' abnormal behaviors and gaze movement patterns in real-time, and aggregates these into an overall Risk Score to evaluate the potential for traffic accidents. This research underscores the significance of internal factors, previously unexplored, providing a novel perspective in the field of traffic safety research. Such an innovative approach suggests the feasibility of developing real-time predictive models for traffic accident prevention and safety enhancement, expected to offer critical foundational data for future traffic accident prevention strategies and policy formulation.

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.