Multi-Player, Multi-Strategy Quantum Game Model for Interaction-Aware Decision-Making in Autonomous Driving
Karim Essalmi, Fernando Garrido, Fawzi Nashashibi
TL;DR
The paper tackles interaction-aware maneuver planning for autonomous driving by introducing Quantum Game Decision-Making (QGDM), a framework that fuses classical game theory with quantum game theory to handle multi-player, multi-strategy settings in real time on conventional hardware. It formalizes the driving scenario as a finite normal-form game with COR-MP payoffs and solves it via a three-step process (dominant strategies, Nash equilibria, then expected utility under quantum probabilities), supported by three quantum circuit variants that encode different player/strategy configurations. The approach leverages entanglement and quantum gates to expand the strategy space and capture correlated behaviors, and it is validated in simulation across merging, roundabouts, and highway scenarios, showing improved success rates and reduced collision rates, particularly in high-interaction contexts. The results suggest QGDM’s potential to offer scalable, robust decision-making for AVs under uncertainty and irrational agent behavior, with broader applicability to other robotic decision tasks and future integration with learning-based adaptation of quantum parameters.
Abstract
Although significant progress has been made in decision-making for automated driving, challenges remain for deployment in the real world. One challenge lies in addressing interaction-awareness. Most existing approaches oversimplify interactions between the ego vehicle and surrounding agents, and often neglect interactions among the agents themselves. A common solution is to model these interactions using classical game theory. However, its formulation assumes rational players, whereas human behavior is frequently uncertain or irrational. To address these challenges, we propose the Quantum Game Decision-Making (QGDM) model, a novel framework that combines classical game theory with quantum mechanics principles (such as superposition, entanglement, and interference) to tackle multi-player, multi-strategy decision-making problems. To the best of our knowledge, this is one of the first studies to apply quantum game theory to decision-making for automated driving. QGDM runs in real time on a standard computer, without requiring quantum hardware. We evaluate QGDM in simulation across various scenarios, including roundabouts, merging, and highways, and compare its performance with multiple baseline methods. Results show that QGDM significantly improves success rates and reduces collision rates compared to classical approaches, particularly in scenarios with high interaction.