An Analysis of Driving Behavior Model and Safety Assessment Under Risky Scenarios Based on an XGBoost Algorithm

Hazard perception is a critical factor of a driving behavior model. A simulator-based method and an extreme gradient boosting tree(XGBoost)algorithm are proposed, in order to study the impacts of hazard perception on driving behaviors and improve the accuracy of hazard perception. Three typical scenarios of traffic conflicts are simulated, and a large amount of driving behavior data are collected. The correlation between hazard perception and driving behavior models is discussed under the three scenarios. The correlation analysis reveals that when hazard perception(e.g., dangerous behaviors of pedestrians)is weak, and the vehicle speed(p=0.01), braking reaction position(p < 0.01), and reaction time(p < 0.01)are significantly negatively correlated with the drivers'hazard perception. Based on the correlation analysis, the XGBoost algorithm is used to identify important features determining the capability of hazard perception of drivers. Then, a discriminant model of hazard perception is proposed with following the indicators, such as braking reaction position, reaction time, vehicle speed, braking depth, and acceleration. Compared the proposed method with Light Gradient Boosting Machine(LightGBM), Support Vector Machine(SVM), and Logistic Regression(LR)algorithms, it is found that the accuracy of the XGBoost-based method is 84.8%, its F1-score is 83.4%, and the area under the receiver operating characteristic Curve(AUC)is 0.959, which is better than the LightGBM(accuracy is 78.8%, F1-score is 76.7%, and AUC is 0.924), SVM(accuracy is 57.6%, F1-score is 42.2%, and AUC is 0.859)and LR algorithm(accuracy is 69.7%, F1-score is 65.5%, and AUC is 0.836). In conclusion, the proposed method can provide a more reliable way for understanding the capability of hazard perception of drivers and its impacts on driving behavior models.