Identification of Driver Distraction Based on SHRP2 Naturalistic Driving Study

Abundant evidence shows that driver distraction is one of the fundamental causes of traffic accidents. Current detected methods of driver distraction are mostly based on intrusive or semi-intrusive. The methods not only interfere with the driving task but also are restricted by various environmental factors, resulting in a high false positive rate. This paper only considers noninvasive vehicle kinematics indicators and proposes a recognition method based on deep learning. Firstly, some car following segments are obtained from the naturalistic driving database, and typical distracted segments are extracted by using situation awareness. Then, distracted recognition indexes’ set is established and only contains vehicle kinematic features. Thirdly, the gradient boosted decision tree recursive feature elimination (GBDT-RFE) and random forest recursive feature elimination (RF-RFE) are used to rank the importance of features. The indexes with higher importance are obtained. Finally, the long short-term memory neural network (LSTM-NN) is utilized to realize the classification and recognition of distracted driving, and the results are compared with SVM and AdaBoost. The results show that the F1-scores of LSTM-NN are 89% and 91% in distracted and normal driving, which are higher than SVM and AdaBoost. The average F1-score of distracted recognition (12% and 7%) is higher than SVM and AdaBoost. The false positive rate of different distracted types is less than 15%. LSTM-NN can effectively learn the information before and after the distracted sequence, which is conducive to accurately estimate the driver’s attention state. The study provides a method for vehicle distraction warning system and driving risk propensity assessment.