Behavioral Heterogeneity as Quantum-Inspired Representation
arXiv cs.LG / 3/25/2026
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Key Points
- The paper argues that treating driver heterogeneity as fixed labels or discrete regimes oversimplifies inherently dynamic driving behavior.
- It proposes a quantum-inspired representation where each driver is modeled as an evolving latent state expressed as a density matrix with structured mathematical properties.
- Behavioral observations are incorporated using non-linear Random Fourier Features to map driving data into the model’s representation space.
- The state evolution mechanism combines temporal persistence with context-dependent activation of behavioral profiles, enabling profiles to change over time.
- Experiments on empirical driving data and TGSIM (Third Generation Simulation Data) demonstrate extraction and analysis of driving profiles using the proposed method.
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