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

2025-06-17

AI Summary

Traditional Intelligent Driver Model (IDM) frameworks suffer from a single-state assumption, limiting their ability to capture the multimodality of human driving behavior—i.e., multiple plausible actions under identical observable conditions—resulting in uninterpretable parameters and low prediction fidelity. To address this, we propose the Factorized Hidden Markov Model–IDM (FHMM-IDM) coupling framework, the first to rigorously decouple endogenous driver intent (e.g., aggressive vs. conservative) from exogenous traffic context (e.g., congested vs. free-flow) in car-following modeling, enabling dynamic behavioral mode switching. FHMM governs latent state evolution, IDM enforces physics-informed longitudinal dynamics, and Bayesian MCMC enables joint inference. Evaluated on the HighD dataset, FHMM-IDM identifies highly interpretable driving modes and their transition规律 with显著 improved trajectory prediction accuracy and simulation realism. This work establishes a new paradigm for safety-critical analysis and human-centered ADAS development.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Enhance car-following model accuracy and interpretability
• Capture multi-modal human driving behavior dynamics
• Separate intrinsic driving regimes from external traffic scenarios

Innovation

• Regime-switching framework for multi-modal driving behavior
• Factorial Hidden Markov Model with IDM dynamics
• Bayesian inference via MCMC for parameter estimation