Driving distraction detection based on gaze activity

Abstract

Driving distraction detection can effectively prevent the occurrence of traffic accidents. Thus, monitoring a driver's state is very important for road safety. At present, most driving distraction detection methods focus on singular aspects, such as gaze distraction or hand distraction, and rarely focus on cognitive distraction as it is difficult to detect. This study proposed a driving distraction detection method based on gaze activity. A multi-channel convolutional neural network was used to classify the driver's gaze area and calculate the gaze activity. In cognitive distraction, the driver's gaze activity is significantly lower than that in the normal driving state. The activity thresholds via experiments have been obtained and used to determine whether drivers were in a cognitively distracted state. Through experiments, the accuracy of this method reached 92.36%. To identify driving distractions more comprehensively, a gaze distraction algorithm based on the two-second rule has been added to the method. The experiment demonstrated that our method in combination with this algorithm improved all of the indicators (when compared to only the gaze activity algorithm), and the accuracy rate increased to 95%.