# Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior

arXiv: https://arxiv.org/abs/2506.14762 · HTML: https://mlsanity.com/paper/2506.14762/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 17 Jun 2025.

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

- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [A Physics-Informed Deep Learning Paradigm for Car-Following Models](https://mlsanity.com/paper/2012.13376/) (2020)
- [Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches](https://mlsanity.com/paper/1708.08986/) (2017)
- [Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios](https://mlsanity.com/paper/2003.00759/) (2020)
- [Learning and Inferring a Driver's Braking Action in Car-Following Scenarios](https://mlsanity.com/paper/1801.03905/) (2018)
- [Assessing Markov Property in Driving Behaviors: Insights from Statistical Tests](https://mlsanity.com/paper/2501.10625/) (2025)
- [Interactive Car-Following: Matters but NOT Always](https://mlsanity.com/paper/2307.16127/) (2023)
- [Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression](https://mlsanity.com/paper/2404.16023/) (2024)
- [A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics](https://mlsanity.com/paper/2506.05902/) (2025)

## Built on by (most cited first, 3 of 3)

- [When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior](https://mlsanity.com/paper/2507.07012/) (2025)
- [Behavioral Heterogeneity as Quantum-Inspired Representation](https://mlsanity.com/paper/2603.22729/) (2026)
- [An Empirical Markov Chain Car-Following (MC-CF) Model](https://mlsanity.com/paper/2603.27909/) (2026)

## Abstract (arXiv)

Accurate and interpretable car-following models are essential for traffic simulation and autonomous vehicle development. However, classical models like the Intelligent Driver Model (IDM) are fundamentally limited by their parsimonious and single-regime structure. They fail to capture the multi-modal nature of human driving, where a single driving state (e.g., speed, relative speed, and gap) can elicit many different driver actions. This forces the model to average across distinct behaviors, reducing its fidelity and making its parameters difficult to interpret. To overcome this, we introduce a regime-switching framework that allows driving behavior to be governed by different IDM parameter sets, each corresponding to an interpretable behavioral mode. This design enables the model to dynamically switch between interpretable behavioral modes, rather than averaging across diverse driving contexts. We instantiate the framework using a Factorial Hidden Markov Model with IDM dynamics (FHMM-IDM), which explicitly separates intrinsic driving regimes (e.g., aggressive acceleration, steady-state following) from external traffic scenarios (e.g., free-flow, congestion, stop-and-go) through two independent latent Markov processes. Bayesian inference via Markov chain Monte Carlo (MCMC) is used to jointly estimate the regime-specific parameters, transition dynamics, and latent state trajectories. Experiments on the HighD dataset demonstrate that FHMM-IDM uncovers interpretable structure in human driving, effectively disentangling internal driver actions from contextual traffic conditions and revealing dynamic regime-switching patterns. This framework provides a tractable and principled solution to modeling context-dependent driving behavior under uncertainty, offering improvements in the fidelity of traffic simulations, the efficacy of safety analyses, and the development of more human-centric ADAS.