The relative wind is a significant but underexplored influencing factor on the tradeoff between propulsion efficiency and pollutant emissions for ships. In this study, full-scale measurements obtained from four voyages of the training ship of Baekkyung were used to quantify the effects of relative wind on ship propulsion efficiency and pollutant emissions. The collected navigational, engine performance, and emission data—including parameters such as shaft power, engine load, specific fuel oil consumption (SFOC), and NO_x and SO_x concentrations—were synchronized and then analyzed using statistical methods and a generalized additive model (GAM). Statistical correlation analysis and a GAM were applied to capture nonlinear relationships between variables. Compared with linear models, the GAM achieved higher predictive accuracy (R² = 0.98) and effectively identified threshold and interaction effects. The results showed that headwind conditions increased the engine load by ~12% and SFOC by 8.4 g/kWh while tailwind conditions reduced SFOC by up to 6.7 g/kWh. NO_x emissions peaked under headwind conditions and exhibited nonlinear escalation beyond a relative wind speed of 12 kn. An operational window was identified for simultaneous improvement of the propulsion efficiency and reduction in pollutant emissions under beam wind and tailwind conditions at moderate relative wind speeds of 6–10 kn and an engine load of 30–40%. These findings can serve as a guide for incorporating relative wind into operational strategies for maritime autonomous surface ships.

Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (μ) and standard deviation (σ) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement.