Multi-Player, Multi-Strategy Quantum Game Model for Interaction-Aware Decision-Making in Automated Driving

2026-02-03

AI Summary

This work proposes a Quantum Game-Theoretic Decision Model (QGDM) to address the limitations of existing autonomous driving decision-making approaches, which struggle to effectively model complex multi-agent interactions and rely on idealized rationality assumptions that fail to capture the uncertainty and irrationality inherent in human driving behavior. By introducing quantum game theory into autonomous driving for the first time, QGDM integrates quantum mechanisms—such as superposition, entanglement, and interference—into a multi-player, interaction-aware decision framework that operates in real time on standard computing platforms. The approach transcends the rationality constraints of classical game theory and demonstrates superior performance in high-interaction simulation scenarios, including roundabouts, merging zones, and highways, significantly improving task success rates and reducing collision rates compared to multiple established baseline methods.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Machine Learning: Quantum Machine Learning
• Multiagent Systems: Mechanism Design

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• interaction-awareness
• autonomous driving
• multi-player decision-making
• human irrationality
• classical game theory

Innovation

• Quantum Game Theory
• Interaction-Aware Decision-Making
• Autonomous Driving
• Multi-Player Multi-Strategy
• Real-Time Quantum-Inspired Model