Research on Distracted Driving Identification of Truck Drivers Based on Simulated Driving Experiment

Abstract

Trucks drivers may be distracted during long-distance transportation, which may threaten the safety of themselves and surrounding vehicles. In this regard, this article first investigates the types of distracted driving that frequently occur in China and select typical distracted driving behaviors to carry out the simulated driving experiment and collect driving performance data. The independent sample T-test was used to select the distracted driving identification indicators. An integrated distracted driving identification model based on Binary Logistic Regression and Fisher discriminant analysis was established. The accuracy of the integrated model in identifying the driver's distracted driving state is 94.79%. This research can provide a basis for the driver assistance systems that reflect the degree of driver distraction.