Evolution of Lane-Changing Behavior in Mixed Traffic: A Quantum Game Theory Approach

2026-04-16

AI Summary

Classical evolutionary game theory struggles to accurately capture the decision-making complexity of human drivers during lane-changing interactions in mixed traffic and fails to reproduce the empirically observed 42% cooperation rate. This work proposes the first application of quantum game theory to mixed-traffic modeling, calibrating the payoff matrix using the Waymo Open Motion Dataset and incorporating entanglement via the Marinatto–Weber quantization scheme to represent implicit correlations between drivers. By integrating Quantal Response Equilibrium to model adaptive behavioral evolution, the framework identifies a human entanglement parameter of approximately 0.52, successfully replicating the observed cooperative equilibrium. Simulations further demonstrate that certain autonomous driving strategies can significantly enhance system-wide cooperation even at low penetration rates, offering a forward-looking behavioral prediction framework for autonomous vehicle deployment.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• 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: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

As automated vehicles (AVs) enter mixed traffic, proactively anticipating the evolution of human driving behavior during critical interactions, such as lane changes, is essential. However, classical Evolutionary Game Theory (EGT) fails to capture the complexity of human decision-making during lane changes. Specifically, by strictly assuming independence between agents, classical models calibrated on empirical payoffs predict a convergence to unrealistic full cooperation, contradicting the stable 42% cooperation rate observed in real-world data. To resolve this discrepancy, this study introduces a Quantum Game Theory (QGT) framework. We analyze 7,636 lane-changing interactions from the Waymo Open Motion Dataset (WOMD) to derive empirical payoff matrices via a Quantal Response Equilibrium (QRE) model. Utilizing the Marinatto-Weber (MW) quantization scheme, we introduce an entanglement parameter to mathematically embed latent correlations directly into the payoff structure of a single interaction. Our results identify a human entanglement parameter of $|b|^2_{HDV} \approx 0.52$ that accurately reproduces the observed mixed equilibrium. Furthermore, simulations of three AV deployment strategies (classical, entangled, and inverted) reveal that human adaptation depends critically on the underlying AV algorithm: while cooperative classical AVs maximize system-wide cooperation at high market penetration rates, defective inverted AVs paradoxically yield higher overall cooperation at low penetration rates by prompting more cooperative behaviors from human drivers. Consequently, rather than waiting for large scale deployment to observe these effects, stakeholders can utilize this framework to simulate repeated interactions and proactively anticipate how human driver behavior will evolve in response to specific AV software designs.

Problem

• mixed traffic
• lane-changing behavior
• human driving behavior
• evolutionary game theory
• cooperation rate

Innovation

• Quantum Game Theory
• Entanglement Parameter
• Mixed Traffic
• Lane-Changing Behavior
• Human-AV Interaction