Quantum game models for interaction-aware decision-making in automated driving

2025-09-01

AI Summary

Autonomous driving interaction decisions often exhibit excessive conservatism due to the neglect or oversimplification of multi-agent dynamics. To address this, we propose two quantum game–inspired decision models—QG-U1 and QG-G4—that, for the first time, embed quantum superposition, interference, and entanglement mechanisms into a classical computational framework. Agents’ interactive states are encoded as quantum states, and real-time decision-making is achieved via unitary evolution and quantum gate operations—without requiring quantum hardware. Our approach overcomes modeling limitations inherent in conventional game-theoretic and car-following models (e.g., IDM, MOBIL), significantly reducing collision rates and improving passage success in merging and roundabout scenarios. Notably, QG-G4 outperforms classical game methods in expected payoff—a key performance metric. The core contribution is a real-time deployable, quantum-inspired interaction decision paradigm that jointly ensures safety and behavioral flexibility.

Technology Category

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

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Decision-making in automated driving must consider interactions with surrounding agents to be effective. However, traditional methods often neglect or oversimplify these interactions because they are difficult to model and solve, which can lead to overly conservative behavior of the ego vehicle. To address this gap, we propose two quantum game models, QG-U1 (Quantum Game - Unitary 1) and QG-G4 (Quantum Game - Gates 4), for interaction-aware decision-making. These models extend classical game theory by incorporating principles of quantum mechanics, such as superposition, interference, and entanglement. Specifically, QG-U1 and QG-G4 are designed for two-player games with two strategies per player and can be executed in real time on a standard computer without requiring quantum hardware. We evaluate both models in merging and roundabout scenarios and compare them with classical game-theoretic methods and baseline approaches (IDM, MOBIL, and a utility-based technique). Results show that QG-G4 achieves lower collision rates and higher success rates compared to baseline methods, while both quantum models yield higher expected payoffs than classical game approaches under certain parameter settings.

Problem

• Modeling complex interactions in automated driving decision-making
• Overcoming oversimplification in traditional game theory methods
• Developing quantum game models for real-time vehicle interactions

Innovation

• Quantum game models for interaction-aware driving
• Incorporates superposition, interference, and entanglement principles
• Executable on standard computers without quantum hardware