A Robust Simulation Framework for Verification and Validation of Autonomous Maritime Navigation in Adverse Weather and Constrained Environments

2026-03-02

AI Summary

This study addresses the lack of reliable safety validation methods for autonomous vessels operating in adverse weather and confined waters, where comprehensive assessment of navigational performance remains challenging. To this end, the authors propose a high-fidelity virtual simulation framework that, for the first time, integrates parameterized adverse weather effects—such as rain, fog, and waves—with high-resolution bathymetric data of port environments. This integration enables holistic modeling of perception degradation, localization uncertainty, and shallow-water maneuverability. The framework facilitates configurable and systematic verification and validation (V&V) testing under extreme conditions that are either difficult to reproduce or pose high risks in real-world scenarios, thereby substantially enhancing test coverage and operational safety.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Maritime Autonomous Surface Ships (MASS) have emerged as a promising solution to enhance navigational safety, operational efficiency, and long-term cost effectiveness. However, their reliable deployment requires rigorous verification and validation (V\&V) under various environmental conditions, including extreme and safety-critical scenarios. This paper presents an enhanced virtual simulation framework to support the V\&V of MASS in realistic maritime environments, with particular emphasis on the influence of weather and bathymetry on autonomous navigation performance. The framework incorporates a high-fidelity environmental modeling suite capable of simulating adverse weather conditions such as rain, fog, and wave dynamics. The key factors that affect weather, such as rain and visibility, are parameterized to affect sea-state characteristics, perception, and sensing systems, resulting in position and velocity uncertainty, reduced visibility, and degraded situational awareness. Furthermore, high-resolution bathymetric data from major U.S. ports are integrated to enable depth-aware navigation, grounding prevention capabilities, and evaluation of vessel controllability in shallow or confined waterways. The proposed framework offers extensive configurability, enabling systematic testing in a wide spectrum of maritime conditions, including scenarios that are impractical or unsafe to replicate in real-world trials, thus supporting the V\&V of MASS.

Problem

• Maritime Autonomous Surface Ships
• Verification and Validation
• Adverse Weather
• Constrained Environments
• Autonomous Navigation

Innovation

• Maritime Autonomous Surface Ships
• verification and validation
• high-fidelity simulation
• adverse weather modeling
• bathymetry-aware navigation