Risky lane-changing behavior recognition based on stacking ensemble learning on snowy and icy surfaces

Risky lane-changing (LC) behavior adversely affects traffic safety, especially on snowy and icy surfaces. However, due to the particularity of the snowy and icy surfaces and the scarcity of data, research on risky lane-changing behavior (RLCB) under extreme scenarios is insufficient. Therefore, this study presents a novel research framework aimed at selecting key risk characterization indicators (RCIs) and identifying RLCB on highways using driving simulation data on snowy and icy surfaces. A highway LC scenario was established on snowy and icy surfaces using a driving simulator, and 1200 sets of LC sample data were extracted. From the perspectives of parameter importance and correlation, 12 key RCIs with high importance and low inter-correlation were selected using the C4.5 decision tree algorithm and Pearson correlation analysis method. The RLCB recognition model was developed using the Stacking ensemble learning method and then compared with traditional recognition algorithms. The results show that the accuracy of the recognition model based on the Stacking ensemble learning model is significantly better than that of traditional algorithms, with a recognition accuracy of 98.33%. This finding can provide the basis for developing LC warning systems for intelligent connected vehicles on snowy and icy surfaces.