Detection of Driver Cognitive Distraction Using Driver Performance Measures, Eye-Tracking Data and a D-FFNN Model

The issue of cognitive distraction during driving has been identified as a major cause of road accidents. Detecting cognitive distraction in real-time can be a valuable strategy for preventing accidents. In this study, a novel approach is presented for the purpose of detecting cognitive distraction in real-time using artificial intelligence while taking into account eye-tracking and head movement data, combined with driving performance measures. This methodology involved collecting data from participants in a driving simulator, on a motion platform, while they performed a cognitive task as well as a control driving scenario. The data collected included eye-tracking data, head movement data, driving performance measures, and subjective ratings of distraction. To develop an accurate model for detecting cognitive distraction, a Deep Feedforward Neural Network (D-FFNN) model was employed while considering binocular gaze direction, pupil diameter, orientation of each eye, head rotational velocities, and head acceleration. The developed model was trained using the collected data and achieved an accuracy of 96.09% in detecting cognitive distraction. The results of our study demonstrate the effectiveness of the proposed method in identifying cognitive distraction in real-time. Also, the accuracy of this model was compared with other AI based classification algorithms. The proposed method has significant implications for preventing vehicle accidents caused by cognitive distraction. The proposed method can be integrated into existing driver-assistance systems to alert drivers and assist them in returning their focus to the road.