Sequential multi-ship encounter scenarios for testing automatic collision avoidance systems

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Sequential multi-ship encounter scenarios for testing automatic collision avoidance systems
Title:
Sequential multi-ship encounter scenarios for testing automatic collision avoidance systems
Journal Title:
6th International Conference on Maritime Autonomous Surface Ship (ICMASS) 2024
DOI:
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Publication Date:
01 January 1970
Citation:
L. Zhao and X. Fu, “Sequential multi-ship encounter scenarios for testing automatic collision avoidance systems [Abstract],” in Proc. 6th Int. Conf. Maritime Autonomous Surface Ship (ICMASS), 2024.
Abstract:
Simulation-based digital testing is considered a fundamental step and approach for safety testing of autonomous vessel collision avoidance systems. It provides a cost-effective means to simulate a wide range of scenarios, facilitating quantitative performance assessments and enabling traceable detection of failures. In the construction of safety-critical scenarios for testing, besides basic single-ship encounter scenarios, attention has increasingly turned to the complex encounter scenarios involving multiple ships and their systematic construction. Existing solutions primarily focus on situations where the tested vessel encounters multiple other vessels simultaneously. However, in scenarios occurring within high-density traffic areas, such as intersection areas in narrow waterways, the maneuvering space of the tested vessel may be constrained by surrounding vessels or result in other close-quarters situations. The background vessels in these scenarios, although not directly encountered in the initial setup with the tested vessel, are still vital components of safety-critical scenarios. Their systematic construction has yet to be fully discussed. This article categorizes such scenarios as Sequential Multi-Ship Encounter Scenarios and proposes a scalable automated scenario generation method, aiming to enhance the diversity of digital collision avoidance testing scenarios
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Singapore Maritime Institute - Programme of Maritime AI Research
Grant Reference no. : SMI-2022-MTP-06
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