Entangled Traffic: A Quantum Game-Theoretic Framework for Lane-Changing Cooperation in Mixed Traffic

Abstract

As automated vehicles (AVs) enter mixed traffic, proactively anticipating human driving behavior during interactions like lane changes is essential. Classical Evolutionary Game Theory (EGT) assumes driver independence, predicting unrealistic full cooperation that contradicts the stable 42% cooperation rate observed in real-world Waymo Open Motion Dataset (WOMD) trajectories. To resolve this discrepancy, this study introduces a Quantum Game Theory (QGT) framework utilizing the Marinatto-Weber quantization scheme. By embedding latent correlations between drivers into a single interaction’s payoff structure via an entanglement parameter (|b|2HDV ≈ 0.52), our model accurately reproduces the observed mixed equilibrium. Furthermore, simulations of various AV deployment strategies reveal that human adaptation depends critically on the underlying AV algorithm. This framework enables stakeholders to simulate repeated interactions and anticipate how human drivers will evolve in response to specific AV software designs, ensuring safer mixedtraffic integration.