疲劳驾驶检测研究综述OA

Fatigue driving is a major factor contributing to traffic accidents,making fatigue detection research crucial for ensuring road traffic safety.In recent years,advances in deep learning and intelligent vehicle technologies have improved fatigue detection techniques in terms of accuracy,real-time performance,and adaptability.This paper summarizes commonly used public fatigue driving datasets and their performance evaluation metrics.It categorizes fatigue detection methods by data acquisition approach and the evolution of discrimination dimensions,ranging from subjective to objective and from indirect to direct,covering physiological signals,observable behaviors,subjective evaluations,and expert assessments,and defines relevant discrimination criteria and calculation formulas.According to signal acquisition approach,fatigue detection methods are further divided into contact-based,non-contact-based,and hybrid approaches.Representative studies from the past five years using physiological signals,facial features,driver operational behaviors,and vehicle operational states are reviewed.A tabular summary outlines each method's classification,contact type,applied datasets,and respective strengths and limitations.Finally,this paper analyzes the challenges of deploying fatigue detection systems in real-world scenarios,including computational constraints,insufficient cross-scenario generalization,and privacy compliance,providing valuable references for understanding the research trajectory and promoting practical applications.