Citation

Zhang H, Ni D, Ding N, Sun Y, Zhang Q, Li X. Transp. Res. Interdiscip. Persp. 2023; 21: e100865.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trip.2023.100865

PMID

unavailable

Abstract

Fatigue is always accompany with the driving task, which have been extensively investigated for driver monitoring and traffic safety. While many scholars dedicate to the study of fatigue detection methods with higher accuracy, but the basic correlation between detection methods and fatigue cause or prevention receive relatively little attention. This study systematically reviews the authors' studies of fatigue influential factors, fatigue identification and measurement, and fatigue prediction; and then structurally and comparably describes the research of driver fatigue behavior from the above three components within the literature of interest. Time-related indicators are usually considered as the main driver fatigue influential factors, and driving environment and vehicle performance are also found to be contributive to driver fatigue. The elastic control of driving time and rest time is an effective measure for the prevention of driver fatigue. Sensitivity analysis can test the correlation between measurements of fatigue identification and fatigue level, and then ensure the performance of measurements. Models that consider time-related factors based on bio-mathematic model theory can be used for real-time fatigue level prediction and characterizing the fatigue dynamics in the planned travel time. Driver individual differences should be considered for the fatigue behavior research as the performance of fatigue detection model and prediction models could vary greatly within drivers from different population. This review described the structure of driver fatigue behavior studies, and the link between fatigue influential indicators, fatigue identification, prediction. The effect of fatigue identification should be further explored, not just for detection or high accuracy.

Language: en

Keywords

Driver fatigue; Fatigue identification and prediction; Individual difference; Structural analysis