# Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression

arXiv: https://arxiv.org/abs/2404.16023 · HTML: https://mlsanity.com/paper/2404.16023/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [Social Interactions for Autonomous Driving: A Review and Perspectives](https://mlsanity.com/paper/2208.07541/) (2022)
- [Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study](https://mlsanity.com/paper/1811.06395/) (2018)
- [Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios](https://mlsanity.com/paper/2003.00759/) (2020)
- [Microscopic Calibration and Validation of Car-Following Models -- A Systematic Approach](https://mlsanity.com/paper/1403.4990/) (2014)
- [Learning and Inferring a Driver's Braking Action in Car-Following Scenarios](https://mlsanity.com/paper/1801.03905/) (2018)
- [Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach](https://mlsanity.com/paper/2010.08495/) (2020)
- [Interactive Car-Following: Matters but NOT Always](https://mlsanity.com/paper/2307.16127/) (2023)

## Built on by (most cited first, 2 of 2)

- [Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior](https://mlsanity.com/paper/2506.14762/) (2025)
- [When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior](https://mlsanity.com/paper/2507.07012/) (2025)

## Abstract (arXiv)

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.