LAUSR

Road rage is a serious psychological issue affecting traffic safety, which has attracted increasing concern regarding driving anger intervention. This study proposed a method for driving anger identification based on physiological features. First, 30 drivers were recruited to perform on-road experiments on a busy route in Wuhan, China. The drivers’ anger could be inducted on the study route by elicitation events, e.g. vehicles weaving/cutting in line, jaywalking, traffic congestion and waiting at red light if they want to finish the experiments ahead of basic time for extra pay. Subsequently, significance analysis was used to determine that five physiological features including heart rate, skin conductance, respiration rate, the relative energy spectrum of θ and β bands of electroencephalogram were effective for driving anger identification. Finally, a linear discriminant model was proposed to identify driving anger based on the optimal thresholds of the five features which were determined by receiver operating characteristic (ROC) curve analysis. The results show that the proposed model achieves an accuracy of 85.84% which is 7.95 and 5.71% higher than the models using back propagation neural network and support vector machine, respectively. The results can provide theoretical foundation for developing driving anger detection devices based on physiological features.