Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression
This work addresses the need for interpretable models in microscopic traffic simulation and autonomous driving systems, though it is incremental as it builds on existing data-driven methods.
The paper tackled the problem of learning car-following behaviors by introducing a Bayesian Matrix Normal Mixture Regression model to capture feature correlations and temporal dynamics, with results showing adeptness in capturing intricate correlations and outperforming interpretability in identifying distinct operational conditions.
Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation. Traditional CF models, though simple, often lack generalization capabilities, while many data-driven methods, despite their robustness, operate as "black boxes" with limited interpretability. To bridge this gap, this work introduces a Bayesian Matrix Normal Mixture Regression (MNMR) model that simultaneously captures feature correlations and temporal dynamics inherent in CF behaviors. This approach is distinguished by its separate learning of row and column covariance matrices within the model framework, offering an insightful perspective into the human driver decision-making processes. Through extensive experiments, we assess the model's performance across various historical steps of inputs, predictive steps of outputs, and model complexities. The results consistently demonstrate our model's adeptness in effectively capturing the intricate correlations and temporal dynamics present during CF. A focused case study further illustrates the model's outperforming interpretability of identifying distinct operational conditions through the learned mean and covariance matrices. This not only underlines our model's effectiveness in understanding complex human driving behaviors in CF scenarios but also highlights its potential as a tool for enhancing the interpretability of CF behaviors in traffic simulations and autonomous driving systems.