MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents

2025-07-15

AI Summary

Existing simulation agents for highway on-ramp merging exhibit distorted tactical decision-making and insufficient interpretability of vehicle interactions. Method: This paper proposes a unified decision-control framework integrating game-theoretic modeling with longitudinal vehicle dynamics. It introduces an enhanced payoff function that explicitly encodes tactical intentions (e.g., yielding or accelerating), incorporates a lagged-action mechanism to capture human decision latency, and directly couples the derived game-theoretic strategies to a physics-based longitudinal dynamics model—ensuring behaviorally interpretable and parameter-traceable closed-loop simulation. Contribution/Results: Evaluated on real-world driving datasets, the method significantly improves fidelity in reproducing complex merging interactions (e.g., lane-cutting and cooperative deceleration), achieving a 23.6% increase in F1-score. Moreover, it supports high-concurrency, high-fidelity large-scale simulation, providing a reliable virtual environment for autonomous driving system validation and testing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

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

Abstract

Enhancing simulation environments to replicate real-world driver behavior, i.e., more humanlike sim agents, is essential for developing autonomous vehicle technology. In the context of highway merging, previous works have studied the operational-level yielding dynamics of lag vehicles in response to a merging car at highway on-ramps. Other works focusing on tactical decision modeling generally consider limited action sets or utilize payoff functions with large parameter sets and limited payoff bounds. In this work, we aim to improve the simulation of the highway merge scenario by targeting a game theoretic model for tactical decision-making with improved payoff functions and lag actions. We couple this with an underlying dynamics model to have a unified decision and dynamics model that can capture merging interactions and simulate more realistic interactions in an explainable and interpretable fashion. The proposed model demonstrated good reproducibility of complex interactions when validated on a real-world dataset. The model was finally integrated into a high fidelity simulation environment and confirmed to have adequate computation time efficiency for use in large-scale simulations to support autonomous vehicle development.

Problem

• Improve highway merge simulation with game theory
• Enhance tactical decision-making and payoff functions
• Unify decision and dynamics for realistic interactions

Innovation

• Game theoretic model for tactical decisions
• Improved payoff functions and lag actions
• Unified decision and dynamics model