Collision Avoidance and Early Warning Method for Inland Bridge Areas Based on Enhanced Safety Potential Fields

To precisely quantify the dynamic evolution of vessel collision risks in inland bridge zones and enable tiered early warning, this study proposes an assessment method integrating vessel dynamic motion prediction with multi-dimensional potential field coupling. By adapting traditional safety potential field models to account for vessel navigation characteristics and bridge zone environmental constraints, the approach is applied to vessel-bridge collision risk evaluation. Based on risk causation theory, bridge zone risks are decomposed into four elements: static obstacles, channel constraints, human decision-making, and vessel kinetic energy. Static potential energy fields, boundary potential fields, behavioral potential fields, and time-varying kinetic energy fields are constructed respectively. Weighted allocation achieves coupling among these four potential fields. To address nonlinear vessel motion and prediction uncertainties caused by wind-induced flow disturbances, the Kalman filter algorithm processes automatic identification system (AIS) data in real time. This corrects process noise and observation noise to predict vessel dynamic deviations, which serve as correction parameters for the time-varying kinetic energy field, enhancing the potential field model's accuracy in representing dynamic risks. The time-varying kinetic energy field is superimposed with the improved potential field model to generate a comprehensive predicted field strength. This is combined with measured AIS data to produce an observed field strength, establishing a "prediction-observation" dual-field coupling early warning mechanism. Dynamic thresholds are set based on relevant regulations and historical cases to trigger graded warnings. Experimental validation conducted at the Chizhou Yangtze River Highway Bridge revealed: predicted field strengths at time points T2, T3, and T4 were 0.75, 0.64, and 0.45, respectively, while actual field strengths were 0.65, 0.59, and 0.40. The maximum relative error of 13.3% occurred at T2 during the bridge pier passage. The experiment confirmed the model's real-time capability and accuracy in collision risk warning for vessels passing under inland river bridges. The dual-field coupling mechanism enables controllable-error warnings in high-risk pier zones, providing dynamic risk quantification for vessel navigation decision-making.