Study on recognizing distracted driving behavior based on an improved visual self-attention model
To address the issue of recognizing distracted driving behavior,this study proposes a method that utilizes an improved visual self-attention model.The ViT_CR model is first constructed to estimate the driver's head pose.Multi-task learning is employed to improve the accuracy of angle prediction,resulting in a prediction error MAE of 4.61 on the dataset AFLW.Subsequently,ViT_CR is used to process continuous video frames.Safety thresholds and auxiliary parameters are set based on the distracted driving recognition principle to determine whether the driver is in a distracted state or not.The experiments demonstrate that the method can effectively utilize temporal information of head pose for recognition on the real driving dataset Dimags.This provides a new idea for monitoring and warning against distracted driving.
NETL
NSTL
万方数据
