Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process. (February 2023)

• Record Type: Journal Article
• Authors: Gao, Dawei; Zhu, Yongsheng; Guedes Soares, C.
• Is Part Of: Reliability engineering & system safety. Volume 230(2023)
• Publication Date: 2023-02
• Subjects: Dynamic risk assessment -- Multi-step prediction -- Gaussian process -- Long-sequence trajectory -- Feature fusion; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108963
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A long-sequence multi-step prediction method based on multivariate Gaussian hypothesis and Gaussian process is proposed. The reference trajectory prediction model is developed, and the uncertainty intervals are predicted. The two parts are fused for a more accurate prediction to calculate the dynamic collision probability. The Gaussian process and a Laplacian Eigenmaps-Self-Organizing Maps model are adopted for fast batch processing. The experimental results demonstrate that the proposed model can achieve a more accurate dynamic risk assessment. Abstract: A long-sequence multi-step prediction method based on multivariate Gaussian hypothesis and Gaussian process is proposed to model the uncertainty in the future ship path. This is a necessary step to predict the area where the ship is likely to be located at each future moment and to perform a dynamic risk assessment. Through data fusion, the uncertainty of the prediction is reduced, and more accurate support can be achieved for risk assessment. Firstly, from the current trajectory, the initial uncertainty intervals for the future trajectory are predicted based on the Gaussian process. Then, from the historical data, a reference trajectory set suitable for predicting the future path is generated based on a feature extracting process, named the reference trajectory prediction model in this paper, and the uncertainty intervals are also predicted. After that, the two parts are fused for a more accurate prediction to calculate the dynamic collision probability. The Gaussian process and a Laplacian Eigenmaps-Self-Organizing Maps model are adopted for fast batch processing. The experimental results demonstrate that the proposed model can combine the advantages of both and achieve a more accurate dynamic risk assessment.