MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents
This work addresses the need for more humanlike simulation agents in autonomous vehicle technology, focusing on highway merging, but it is incremental as it builds on existing game-theoretic and dynamics modeling approaches.
The paper tackled the problem of simulating realistic human driver behavior in highway merging scenarios for autonomous vehicle development by proposing a game-theoretic model for tactical decision-making with improved payoff functions and lag actions, coupled with a dynamics model, which demonstrated good reproducibility of complex interactions on real-world data and adequate computational efficiency for large-scale simulations.
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.