Situation assessment (SA) supports maneuver decisions in dynamic three-dimensional robotic interactions, yet explicit methods depend on subjective factors and weights, whereas implicit learning-based methods are difficult to interpret. We propose a traceable benchmarking framework that standardizes motion dynamics, scenarios, capture-tagging rules, round-score metrics, and the decision carrier. Unified-Dimensional Situation Assessment (UDSA) converts heterogeneous advantages to a common altitude-equivalent scale, and Situation Assessment-Based Balanced Expert Decision-Making (SABED) enables controlled closed-loop comparison. Across five independent seeds, UDSA achieved mean round scores of 0.790 ± 0.010 against expert weighting and 0.710 ± 0.016 against Criteria Importance Through Intercriteria Correlation (CRITIC). Scenario-stratified, stress, and symmetric self-play summaries further supported robustness and label invariance. The released package enables reproduction of UDSA scoring, equivalence conversion, trajectory assessment, and the reported statistical summaries.