Predicting Traffic Accident Risk based on Driver Abnormal Behavior and Gaze (운전자 이동행동 및 시선에 따른 교통사고 위험도 예측)

Ji-Woong Yang (양지웅); Hyeon-Jin Jung (정현진); Han-Jin Lee (이한진); Tae-Wook Kim (김태욱); Ellen J. Hong (홍정희)

doi:10.9708/jksci.2024.29.08.001

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.

KEYWORDS

Traffic accident prediction, Driver Behavior Analysis, Gaze Processing, Real-time Prediction, Traffic Accident Prevention

Journal Copyright Policy

No CCL information provided

Citation status

* References for papers published after 2025 are currently being built.

[report] World Health Organization / 2023 / Global status report on road safety 2023 / World Health Organization

[report] A. R. Quimby / 1986 / Perceptual abilities of accident involved drivers

[confproc] IS Kim / 2008 / The Study on Risk of Distracted Driving / 2008 Annual Conference Korean Psychologial Assocoaition : 136~137

[report] J. C. Stutts / 2002 / Cell phone use while driving in North Carolina: 2002 update report

[confproc] D. L. Strayer / 2003 / Fatal distraction? A comparison of the cell-phone driver and the drunk driver / Driving Assessment Conference / University of Iowa 2(2003)

[confproc] JH Cho / 2015 / Concept and Relationship of Driver’s Inattention and Distraction in traffic accident / Proceedings of the KOR-KST Conference (73) : 156~161

[journal] SC Lee / 2007 / 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) : 95~105

[journal] JS Cho / 2017 / The Hazardous Expressway Sections for Drowsy Driving Using Digital Tachograph in Truck / Journal of Korean Society of Transportation 35(2) : 160~168

[journal] SY Lee / 2007 / The effects of driving confidence level on drunken drivers: A path analysis study / Korean Journal of Industrial and Organizational Psychology 20(1) : 43~55

[thesis] YJ Song / 2021 / Real-time driver behavior recognition system using a CNN-LSTM model / Master / Hanyang University

[journal] Y. Xing / 2019 / Driver Activity Recognition for Intelligent Vehicles : A Deep Learning Approach / IEEE Transactions on Vehicular Technology 68(6) : 5379~5390

[journal] C. Zhang / 2020 / Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs / IEEE Access 8 : 191138~191151

[journal] L. Yang / 2024 / Video-Based Driver Drowsiness Detection With Optimised Utilization of Key Facial Features / IEEE Transactions on Intelligent Transportation Systems 25(7) : 6938~6950

[confproc] Z. Li / 2024 / Driver anomalous driving behavior detection based on supervised contrastive learning / 4th International Conference on Computer Vision and Data Mining (ICCVDM 2023) 13063 : 667~674

[journal] F. Tango / 2013 / Real-Time Detection System of Driver Distraction Using Machine Learning / IEEE Transactions on Intelligent Transportation Systems 14(2) : 894~905

[journal] HK Kim / 2022 / Study of MTCNN-based driver gaze information visualization / Journal of Digital Art Engineering and Multimedia 9(4) : 431~440

[journal] Y. Guan / 2023 / End-to-End Video Gaze Estimation via Capturing Head-Face-Eye Spatial-Temporal Interaction Context / IEEE Signal Processing Letters 30 : 1687~1691

[confproc] L. Wang / 2016 / Temporal Segment Networks : Towards Good Practices for Deep Action Recognition / European Conference on Computer Vision : 20~36

[journal] L. Wang / 2019 / Temporal Segment Networks for Action Recognition in Videos / IEEE Transactions on Pattern Analysis and Machine Intelligence 41(11) : 2740~2755

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

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2025 KCI Impact Factor : 1.01

Predicting Traffic Accident Risk based on Driver Abnormal Behavior and Gaze

ABSTRACT

KEYWORDS

Journal Copyright Policy

Citation status

* References for papers published after 2025 are currently being built.

Journal of The Korea Society of Computer and Information 2025 KCI Impact Factor : 1.01

Predicting Traffic Accident Risk based on Driver Abnormal Behavior and Gaze

ABSTRACT

KEYWORDS

Journal Copyright Policy

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (2)

REFERENCES (19) * References for papers published after 2025 are currently being built.

Search PDF

Citation

* References for papers published after 2025 are currently being built.