Developing a Ship Collision Risk Index estimation model based on Dempster-Shafer theory

A reliable and accurate evaluation of the risk of a ship collision with other vessels is crucial for the avoidance of maritime accidents. The ship operators use the collision risk index (CRI) to detect the risk of a collision and take the necessary action. However, CRI can be assessed differently depending on various operating conditions or other vessels or encounter conditions, making it difficult to calculate such risk accurately and efficiently. In this study, a new method for calculating the CRI by combining machine learning with D-S theory is proposed to increase the efficiency of the computations while preserving the prediction accuracy of the CRI. Different machine learning models have been investigated and compared based on model accuracy and computational time, and the results showed that the gradient boosting regression (GBR) model efficiently estimates the collision risk and increases the calculation speed compared to the D-S theory. Further, the effectiveness of this approach was examined by collision avoidance simulation while simultaneously satisfying the COLREG rule and making early proper decisions to avoid collisions, which shows the advantages of the proposed risk assessment model in practical application.

Automated summary

This study proposes a new method that combines machine learning with D-S theory to enhance the efficiency and accuracy of calculating Collision Risk Index (CRI). The findings suggest that the gradient boosting regression (GBR) model outperforms D-S theory in estimating collision risk and speeding up calculations. Simulation results demonstrate the effectiveness of this approach in making early decisions to avoid collisions while complying with COLREG rules.