Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Predicting Traffic Accident Risk based on Driver Abnormal Behavior and Gaze

  • Ji-Woong Yang (Dept. of Artificial Intelligence Semiconductor Engineering, Hanyang University) ;
  • Hyeon-Jin Jung (Dept. of Computer Science, Yonsei University) ;
  • Han-Jin Lee (Dept. of Computer Science, Yonsei University) ;
  • Tae-Wook Kim (Division of Software, Yonsei University) ;
  • Ellen J. Hong (Division of Software, Yonsei University)
  • Received : 2024.06.11
  • Accepted : 2024.07.24
  • Published : 2024.08.30
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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.

본 연구는 기존 연구가 주로 도로의 물리적 상태 및 도로 환경 등 외부 요인에 초점을 맞춘 것에 반해, 차량 내부에서 발생하는 운전자의 행동 및 시선 변화를 실시간으로 분석함으로써 교통사고 위험도를 측정하고 예측하는 새로운 접근법을 제시한다. 실시간으로 운전자의 이상행동과 시선 이동 패턴을 정밀하게 측정하고, 이를 통해 도출된 각각의 위험 점수를 합산하여 교통사고 위험도를 평가한다. 본 연구는 기존 연구에서 다루지 않았던 내재적 요인의 중요성을 강조하며 교통안전 연구 분야에 새로운 시각을 제공한다. 이러한 혁신적 접근 방식은 교통사고 예방 및 안전 개선을 위한 실시간 예측 모델의 개발 가능성을 제시하며, 향후 교통사고 예방 전략 및 정책 수립에 있어 중요한 기초 자료를 제공할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (2022R1F1A1074273).

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