Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method

Abstract

ABSTRACT A lane-changing process is complicated due to multiple factors in the driving environment, and unsafe lane-changing behaviour may lead to a severe crash. This study proposes a method for the driving angle prediction of lane changes based on extremely randomized decision trees. First, the harmonic potential is defined to characterize the interaction between the lane-changing vehicle and the surrounding vehicles. Next, we construct extremely randomized decision trees to predict driving angles considering relative velocity, relative acceleration, and potential as input variables. Then, the NGSIM dataset is used to verify the method proposed, and the lane-changing process is divided into two stages by different environments. Furthermore, a comparison of prediction performance with several traditional machine learning methods further demonstrates the superior learning ability of the proposed method. Finally, we conduct a sensitivity analysis on the significant variables and discuss the effects of these variables on the prediction results.