Understanding the Mechanism of Discretionary Lane-Changing Behavior Based on Cumulative Prospect Theory and Decision Tree Model

Discretionary lane changing is one of the most important behaviors in vehicle dynamics. The analysis of discretionary lane changing can provide support for human-like autonomous driving system and microscopic traffic simulation. The rule based discretionary lane change decision-making model has the advantages of good interpretability and being in line with human intuitiveness, but the performance of the rule based models are relatively poor. The learning-based discretionary lane changing decision-making models perform better in lane changing analysis than that of the rule based models, while they require a large amount of training data and have poor interpretability. This study proposes a framework for discretionary lane changing decision-making based on cumulative prospect theory and decision tree model, and uses genetic algorithm to calibrate the parameters of the framework. The framework proposed in this study has good interpretability for discretionary lane changing behavior similar to rule-based models, and it can achieve good lane changing prediction performance. The Next Generation Simulation (NGSIM) dataset is used to validate the framework proposed in this study. The results show that the framework proposed in this study can achieve better performance of discretionary lane change prediction than the analysis model based on decision tree and the analysis model based on cumulative prospect theory. When predicting discretionary lane changing behavior 2–0 seconds in advance, the accuracy of the framework in the test set can reach 85.6%.