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

TL;DR

This paper introduces MR-LDM, a game-theoretic model for simulating human-like highway merging behavior, improving decision-making realism and interpretability in autonomous vehicle simulations.

Contribution

It develops a unified decision and dynamics model with enhanced payoff functions and lag actions for more realistic and explainable highway merge simulations.

Findings

Reproduces complex merging interactions accurately

Validated on real-world data with good fidelity

Efficient enough for large-scale autonomous vehicle simulations

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.