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

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.