计算机科学与探索2026，Vol.20Issue(5)：1279-1293,15.DOI:10.3778/j.issn.1673-9418.2509031
疲劳驾驶检测研究综述
Review of Fatigue Driving Detection Research
摘要
Abstract
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.关键词
疲劳检测/疲劳驾驶/接触式检测方法/非接触式检测方法Key words
fatigue detection/fatigue driving/contact-based detection methods/non-contact detection methods分类
信息技术与安全科学引用本文复制引用
赵娅,江流洋,贾迪,姚文达,黄世旺..疲劳驾驶检测研究综述[J].计算机科学与探索,2026,20(5):1279-1293,15.基金项目
国家自然科学基金(62471124) （62471124）
黑龙江省自然科学基金(LH2022F006) （LH2022F006）
黑龙江省哲学社会科学规划项目(24EDE003) （24EDE003）
黑龙江省教育科学规划重点课题(GJB1425341).This work was supported by the National Natural Science Foundation of China(62471124),the Natural Science Foundation of Hei-longjiang Province(LH2022F006),the Philosophy and Social Sciences Planning Project of Heilongjiang Province(24EDE003),and the Key Project of Heilongjiang Provincial Education Science Planning(GJB1425341). （GJB1425341）
