운전자 이동행동 및 시선에 따른 교통사고 위험도 예측
Predicting Traffic Accident Risk based on Driver Abnormal Behavior and Gaze
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참고문헌
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초록
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 이상행동과시선 이동 패턴을 정밀하게 측정하고, 이를 통해 도출된 각각의 위험 점수를 합산하여 교통사고위험도를 평가한다. 본 연구는 기존 연구에서 다루지 않았던 내재적 요인의 중요성을 강조하며교통안전 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 정책수립에 있어 중요한 기초 자료를 제공할 수 있을 것으로 기대된다.
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.
참고문헌 (19)
- Global status report on road safety 2023, "World Health Organization", World Health Organization, ~ (2023)
- Perceptual abilities of accident involved drivers, "A. R. Quimby", ~, pp.No. RR 27 (1986)
- IS Kim, "The Study on Risk of Distracted Driving", 2008 Annual Conference Korean Psychologial Assocoaition, pp.136~137 (2008)
- Cell phone use while driving in North Carolina: 2002 update report, "J. C. Stutts", ~ (2002)
- D. L. Strayer, "Fatal distraction? A comparison of the cell-phone driver and the drunk driver", Driving Assessment Conference, 2(2003) (2003), University of Iowa
- JH Cho, "Concept and Relationship of Driver’s Inattention and Distraction in traffic accident", Proceedings of the KOR-KST Conference, (73), pp.156~161 (2015)
- SC Lee, "The Effects of Driving Behavior Determinants on Dangerous Driving and Traffic Accidents in the Reckless Drivers Group : A Path Analysis Study", Journal of Korean Society of Transportation, 25(2), pp.95~105 (2007)
- JS Cho, "The Hazardous Expressway Sections for Drowsy Driving Using Digital Tachograph in Truck", Journal of Korean Society of Transportation, 35(2), pp.160~168 (2017)
- SY Lee, "The effects of driving confidence level on drunken drivers : A path analysis study", Korean Journal of Industrial and Organizational Psychology, 20(1), pp.43~55 (2007)
- YJ Song, "Real-time driver behavior recognition system using a CNN-LSTM model", Master, pp.2021
- Y. Xing, "Driver Activity Recognition for Intelligent Vehicles : A Deep Learning Approach", IEEE Transactions on Vehicular Technology, 68(6), pp.5379~5390 (2019)[DOI]
- C. Zhang, "Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs", IEEE Access, 8, pp.191138~191151 (2020)[DOI]
- L. Yang, "Video-Based Driver Drowsiness Detection With Optimised Utilization of Key Facial Features", IEEE Transactions on Intelligent Transportation Systems, 25(7), pp.6938~6950 (2024)[DOI]
- Z. Li, "Driver anomalous driving behavior detection based on supervised contrastive learning", 4th International Conference on Computer Vision and Data Mining (ICCVDM 2023), 13063, pp.667~674 (2024)
- F. Tango, "Real-Time Detection System of Driver Distraction Using Machine Learning", IEEE Transactions on Intelligent Transportation Systems, 14(2), pp.894~905 (2013)[DOI]
- HK Kim, "Study of MTCNN-based driver gaze information visualization", Journal of Digital Art Engineering and Multimedia, 9(4), pp.431~440 (2022)[DOI]
- Y. Guan, "End-to-End Video Gaze Estimation via Capturing Head-Face-Eye Spatial-Temporal Interaction Context", IEEE Signal Processing Letters, 30, pp.1687~1691 (2023)[DOI]
- L. Wang, "Temporal Segment Networks : Towards Good Practices for Deep Action Recognition", European Conference on Computer Vision, pp.20~36 (2016)
- L. Wang, "Temporal Segment Networks for Action Recognition in Videos", IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11), pp.2740~2755 (2019)