Modeling Discretionary Lane-Changing Decisions: A Multi-Vehicle Information Enhanced Machine Learning Approach

Accurately predicting human lane-changing (LC) decisions is critical for enhancing the safety and efficiency of autonomous driving. Most existing machine learning-based LC decision models rely on immediate neighboring vehicle interaction features, which may fail to capture drivers’ consideration of long-term traffic conditions in the target lane. Using discretionary LC trajectory data from the US101 dataset, this paper first qualitatively identifies key latent variables influencing LC decisions, then quantitatively ranks these factors using feature importance analysis, and finally constructs a prediction model based on ensemble learning. The analysis reveals that drivers consider not only neighboring vehicles but also multi-vehicle information further ahead, particularly the average speed and average spacing of multiple preceding vehicles. Feature importance ranking shows that safety-related features, especially the spacing with the following vehicle in the target lane (dLag, 0.187), rank significantly higher than benefit-related features such as the average speed of the target lane (v¯T, 0.091), suggesting that safety considerations play a dominant role in the observed LC decisions. Among five imbalanced processing methods, SMOTE+Tomek achieves the best balance (F1 = 0.68). When the Full Feature Set is used, the KNN model achieves the best performance (F1 = 0.79, AUC = 0.97) among six baseline models. This study contributes to the understanding of LC behavior and provides insights that could inform future development of LC prediction models for autonomous vehicles.