The Induction and Detection Method of Angry Driving: Evidences from EEG and Physiological Signals

Abstract

Introduction. Angry driving has been a significant road safety issue worldwide. This study focuses on the problem of inducing and detecting driving anger based on the simulation and on-road experiments. Methods. First, three typical scenarios (including waiting for the red light frequently, traffic congestion, and the surrounding vehicle interference) which could cause driving anger were developed and applied in a driving simulator experimental study. The self-reported, biosignals, and brain signals of driving anger data were collected from the driving anger induction experiment. Second, in order to examine the difference of driving anger between simulation driving and real-life driving, 22 groups of on-road experiments were conducted. The typical scenes and self-reported data were recorded to distinguish normal driving from angry driving. Finally, a Hidden Naïve Bayes classifier was employed to detect angry driving during the on-road driving according to the four features (namely, BVP, SC, δ%, and β%) from driver’s biosignals and brain signals. Results. The evaluation of emotional differentiation degrees and emotional intensity indicates that the developed scenarios based on virtual reality were useful and effective in inducing driving anger. Meanwhile, the proposed angry driving detection approach achieves an accuracy of 85.0%. Conclusions and Applications. Due to possible crash and injury from the on-road experiments, the proposed approach of driving anger induction using a driving simulator is effective in exploring the causal relationship between angry driving, unsafe driving behavior, and traffic accident. In addition, angry driving detection approach can provide theoretical foundation for the development of driving anger warning products.