# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 7 Conclusions  
This paper proposes a collision avoidance method for ships in inland river bridge areas based on the safety potential field theory. This method assesses various risk factors affecting ship-bridge collision prevention and provides real-time warnings. By integrating Kalman filtering with the safety potential field, the method combines physical and virtual forces in the bridge area to predict the next change in field strength for early warning. The risks associated with each element are classified into four categories: static potential fields, dynamic kinetic fields, channel boundary fields, and behavioural fields based on human behaviour. Different potential field functions are established in the model and coupled into the superimposed fields. The method is applied to a case study by simulating the model with a grid of the bridge-area water and analysing the transient risk in the area through the superimposed fields in order to derive the transient hazardous situation of ships navigating in the bridge area. By combining the predicted ship states using Kalman filtering, the overall superimposed field strength of the bridge area at the next moment is obtained. Early warnings are issued by comparing the predicted field strength values at different moments and validating these predictions against the field strength calculated from real-time AIS data to ensure warning accuracy.  
The main contributions of this paper are as follows: Firstly, a new collision warning method for bridge protection is proposed, one which analyses the potential causes of ship-bridge collisions from a holistic perspective. Secondly, the factors that may lead to ship collisions in the bridge area are quantified so that the specific risk value of each influencing factor is clearly visible, thus providing bridge monitors with specific risk indicators. Finally, the ship state predicted by the Kalman filter is combined with the potential field to achieve the fusion of virtual and physical forces, which provides theoretical guidance for further research on collision prevention between ships and bridges. In future research, the proposed model should take more factors into consideration, such as the crew's driving state during navigation, the effect of inertia when the ship is turning or passing through a curved channel, and the method of ship-bridge early warning in special scenarios such as at sea and in curved river sections. In the future, this can be combined with a ship manoeuvring motion model, a neural network and other methods to achieve high-precision ship position prediction and further improve the accuracy of the warning.  
Conceptualization, X.H. and L.H.; methodology, T.W.; software, H.L. and T.W.; validation, C.F., H.L. and L.H.; formal analysis, T.W.; investigation, C.F. and H.L.; resources, L.H.; data curation, C.F. and T.W.; writing--original draft preparation, H.L. and T.W.; writing--review and editing, C.F.; visualization, X.H.; supervision, L.H.; project administration, X.H.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52071248, and the Research Program of Hubei Key Laboratory of Inland Shipping Technology, grant number NHHY2023004.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.