Citation

Vagale A. J. Marine Sci. Eng. 2022; 10(5): e705.

Copyright

(Copyright © 2022, Australia New Zealand Marine Biotechnology Society, Publisher MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/jmse10050705

PMID

unavailable

Abstract

Autonomous surface vehicles need to be at least as safe as conventional vessels, if not safer, when navigating on waters. With a great deal of navigation algorithms for surface vessels out there, the safety of their produced paths is questionable, and, in most cases, complicated to assess and compare. Hence, this paper proposes a method for extended collision risk assessment for paths generated by autonomous navigation algorithms as follows: (1) static, dynamic, and historic risk factors are calculated; (2) individual risk value is determined using a fuzzy inference system; (3) the extended collision risk assessment (ECRA) score is acquired using a root-mean-square method. Finally, a comparison of the ECRA score of each path determines the path with the lowest risk. The validation results show that the proposed method is able to detect lower/higher risk scenarios and assign an adequate risk value in most cases. Risk reduction for cautious paths varies up to 8.43%, while risk increases for incautious paths--up to 57.98%. The results indicate that the method could be used for navigation algorithm evaluation and comparison with some improvements. This research also reveals several promising future directions and applications of the method.

Language: en

Keywords

autonomous surface vehicles; collision risk; expert rule base; fuzzy inference system; safety evaluation; simulation-based testing