Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process

February 2023

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.

Authors in the community:

• 

  C

  Carlos Guedes Soares

  ist11869

  Affiliations and sources

  IST > CENTEC

  Centro de Engenharia e Tecnologia Naval e Oceânica

  In: cienciavitae

  ID: 3187277

VoR - Version of Record

Elsevier

https://www.sciencedirect.com/science/article/pii/S0951832022005786?via%3Dihub

Reliability Engineering and System Safety

108963

230

0951-8320

10.1016/j.ress.2022.108963

other-engineering-and-technologies - Other engineering and technologies

Keywords

• Industrial and Manufacturing Engineering
• Safety, Risk, Reliability and Quality
• Dynamic risk assessment
• Multi-step prediction
• Gaussian process
• Long-sequence trajectory
• Feature fusion

eng - English

http://dx.doi.org/10.1016/j.ress.2022.108963