Studying the predictability of crash risk caused by manual takeover of autonomous vehicles in mixed traffic flow

Abstract

ABSTRACT This study explores how to reduce the cost of prediction as much as possible while ensuring the prediction accuracy of a real-time crash risk model. The extreme gradient enhancement (XGBoost) algorithm was used to predict the crash risk of autonomous vehicles in different sections of highway. The results show that the prediction performance of the model is the best when the threshold value is 0.05. Choosing two variables to predict can ensure high accuracy and simultaneously reduce the cost of prediction when the accuracy of crash risk prediction of the three sections can reach 73%, 62%, and 70%. However, when only one variable can be selected due to sensor or system failure, the speed difference between the takeover car and the front car can be chosen to achieve the greatest benefit. These findings could provide a reference for technicians to design safer and more economical autonomous vehicles.