Evolution of Lane-Changing Behavior in Mixed Traffic: A Quantum Game Theory Approach
This work addresses the challenge of anticipating human behavior evolution in mixed traffic for stakeholders like AV developers and policymakers, offering a proactive simulation tool, though it is incremental as it builds on existing game theory with quantum enhancements.
The study tackled the problem of predicting human lane-changing behavior in mixed traffic with automated vehicles by introducing a Quantum Game Theory framework, which identified a human entanglement parameter of approximately 0.52 that accurately reproduced the observed 42% cooperation rate, and simulations showed that AV deployment strategies significantly influence human adaptation, with cooperative AVs maximizing cooperation at high penetration rates and defective AVs increasing cooperation at low rates.
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.