Estimation of vessel collision risk index based on support vector machine

Abstract

Collision risk index is important for assessing vessel collision risk and is one of the key problems in the research field of vessel collision avoidance. With accurate collision risk index obtained through vessel movement parameters and encounter situation analysis, the pilot can adopt correct avoidance action. In this article, a collision risk index estimation model based on support vector machine is proposed. The proposed method comprises two units, that is, support vector machine-based unit for predicting the collision risk index and the genetic algorithm-based unit for optimizing the parameters of support vector machine. The model and algorithm are illustrated in the empirical analysis phase, and the comparison results show that genetic algorithm-support vector machine model can generally provide a better performance for collision risk index estimation. Meanwhile, the result also indicates that the model may be not so good when we take a higher value of collision risk index. So, the distinguishing threshold of collision risk level should be adjusted according to actual situation when applying this model in practical application.

Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors

“…is spectrum ranges from the safest situation (a near-zero chance of collision) to the riskiest situation during encounters (both ships need to take evasive actions to avoid collision). A collision risk index (CRI) [28] was employed to calculate the risk spectrum. In terms of collision avoidance, the CRI is essential for a ship officer to evaluate the risk of a ship encounter as well as for performing an evasion strategy [29].…”

Prediction of ship collision risk based on CART

“…In addition, in order to investigate the advantages of our proposed CART risk prediction model, the results of the GA‐BP [4], GA‐SVM [7], BP, RBF, and RF models for ship collision risk prediction were compared with that of the CART model using the same training and test sample sets. In our experiment, the parameters of each prediction model are selected to achieve the best prediction effect.…”

“…However, their model considers only a few factors, and therefore, it is not applicable to such calculations in complex waters. Furthermore, Gang et al [7] used the support vector machine (SVM) method to predict the ship collision risk. In particular, the six original parameters that affect the risk of collision are used as inputs of the SVM, while the collision risk derived using the previously mentioned fuzzy comprehensive evaluation method is used as the output.…”

A Collision Risk Identification Method for Autonomous Ships Based on Field Theory

“…Moreover, some methods based on intelligent algorithms are proposed. Gang et al [14] proposed a collision risk estimation model based on Support Vector Machines (SVM), and optimized the corresponding parameters with genetic algorithm. Zhen et al [15] proposed a real-time based on Spatial Clustering analysis of ship collision risk assessment method, and the collision risk model with DCPA and TCPA is constructed by using the negative exponential function.…”