arXiv Machine Learning August 27, 2026 By Mohammad Elayan, Omid Armantalab, Wissam Kontar

Quantum-Inspired Modeling of Driving Behavior

Read the original on arXiv Machine Learning →

The paper introduces a quantum-inspired representation of driver behavior that models drivers as evolving density matrices, capturing continuous, probabilistic, context-dependent, and history-dependent interactions among behavioral variables. Trained unsupervised on the I‑24 MOTION dataset, the framework identifies three interpretable driving regimes—free flow, transition, and congestion—and reproduces macroscopic traffic phenomena such as the fundamental diagram and hysteresis loops. The representation also enhances practical applications by providing context-dependent parameters for classical car‑following models and enabling autonomous vehicles to forecast nearby drivers’ motion in real time.

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