Quantum-Inspired Modeling of Driving Behavior

Abstract:Driver behavior is heterogeneous, context-dependent, and changes over time, and these properties shape the traffic phenomena we observe. Most models, however, fix in advance which behavioral variables interact and how. Behavior outside that form is absorbed as noise, while models flexible enough to capture it tend to lose interpretability. We introduce a quantum-inspired representation of driver behavior that combines properties usually treated separately or in part: it is continuous, probabilistic, context-dependent, history-dependent, and represents interactions among behavioral variables as learned from data. Each driver is encoded as an evolving density matrix, providing a unified representation of behavioral uncertainty, temporal evolution, and context-dependent behavioral variation. Trained without supervision on the I-24 MOTION dataset, the framework recovers three interpretable driving profiles representing three regimes: free flow, transition, and congestion. The profiles capture the behavioral range of the data and the smooth transitions drivers make between regimes as conditions change. The same representation also reproduces known macroscopic phenomena, aligning with the fundamental diagram and reproducing hysteresis loops. We also show how the representation supports practical use: it supplies context-dependent parameters to classical car-following models, and gives an autonomous vehicle a live behavioral read of the surrounding drivers with a short-horizon forecast of their motion. The framework points toward models of traffic that are interpretable and trustworthy by construction. We release an open-source toolkit on GitHub (this https URL) spanning data processing, training, inference, and analysis.

44 pages (including Appendices), 27 figures. Submitted to Transportation Research Part B: Methodological. Code and toolkit: this https URL

Machine Learning (cs.LG); Systems and Control (eess.SY); Machine Learning (stat.ML)

arXiv:2608.25907 [cs.LG]

(or arXiv:2608.25907v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.25907 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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