Agentic discovery of interpretable car-following laws from multi-source trajectory data

Key points

• Analytical CF models offer interpretable representations of longitudinal vehicle interactions, but their prescribed functional structures may limit the representation of heterogeneous driving behavior.1
• This study proposes an agent-centered, physics-aware framework for the empirical discovery of CF laws from multi-source trajectory data.1
• The framework iteratively combines agent-guided hypothesis generation, informed by traffic flow knowledge and state-dependent reconstruction errors, with evolutionary symbolic search and physics-aware multi-objective evaluation.1
• AERM admits analytical equilibrium speed-spacing and flow-density relationships; its stability conditions are derived for the zero-delay formulation, and platoon simulations examine disturbance propagation.1

Sources (1)

• [1]Agentic discovery of interpretable car-following laws from multi-source trajectory data
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 08:25 AM

  Car-following (CF) models is a fundamental component of microscopic traffic simulation and underpin the analysis of collective traffic dynamics, while its modeling becomes more challenging due to diverse human driving behaviors and the increasing presence of automated vehicle.

  Analytical CF models offer interpretable representations of longitudinal vehicle interactions, but their prescribed functional structures may limit the representation of heterogeneous driving behavior.

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