Fatigue driving is one of the main causes of truck accidents. Existing fatigue driving studies are mostly based on the vehicle running data or simulation data, which cause defects in the validity and reliability of the model. The paper extracts the sample data under different fatigue levels of drivers with naturalistic driving data of vehicle and drivers’ facial video. Based on that, indicators of vehicle running states are selected. The BP neural network is used to establish the detection model of fatigue driving behaviors, considering the influence of the number of model training samples and other parameters on the accuracy of fatigue driving behavior detection. The status data of 50 freight vehicles is used to test the detection model. Results show that the accuracy of the model can reach more than 80%. The model provides a new scheme for detecting fatigue driving behavior and a theoretical basis for fatigue warning.
Detection Model on Fatigue Driving Behaviors Based on the Running State of Freight Vehicles
20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)
CICTP 2020 ; 3680-3691
12.08.2020
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Sloshing effects and running safety in railway freight vehicles
Taylor & Francis Verlag | 2013
|Sloshing effects and running safety in railway freight vehicles
Online Contents | 2013
|Sloshing effects and running safety in railway freight vehicles
Kraftfahrwesen | 2013
|