A Calculation Method for Automated Driving Test Scenario Complexity

Scenario-based validation requires efficient screening of large test libraries, but existing complexity metrics often combine traffic hazard and automated-driving-system workload into a single construct. This study proposes a two-dimensional framework that separates scenario hazardousness from system processing difficulty. Hazardousness combines normalized acceleration and time-to-collision measures. Processing difficulty aggregates perception, decision-making, and execution demands, with weights obtained from a triangular fuzzy analytic hierarchy process. The framework was compared with four baseline metrics using 2000 Latin-hypercube-sampled cut-in scenarios and was explored in 22 closed-field scenarios tested with three production Level 2 vehicles. The proposed metric produced a broad and less concentrated distribution of cases across the normalized score range. However, total complexity was not significantly correlated with any vehicle score in the 22-case dataset; therefore, the field experiment supports only preliminary discriminant analysis, not predictive validity. A 10% hierarchical-weight perturbation analysis showed that the three largest global weights remained the same set in 82.3% of 10,000 simulations. The model is currently limited by empirically assigned perception constants, a small field sample, few ultra-high-complexity cases, and omission of road curvature, crosswinds, tire condition, and actuator delay. The additive aggregation assumes that interactions among criteria can be neglected for the intended comparison; this assumption has not been validated. The framework is best viewed as an interpretable scenario-screening tool requiring further calibration and validation.