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

Mayur S. Patil; Nataraj Sudharsan; Anthony S. Saaiby; JiaChang Xing; Keliang Pan; Veneela Ammula; Jude Tomdio; Jin Wang

cs.ROMar 3, 2026

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

Authors: Mayur S. Patil, Nataraj Sudharsan, Anthony S. Saaiby, JiaChang Xing, Keliang Pan, Veneela Ammula, Jude Tomdio, Jin Wang,

Organizations: Department of Mechanical Engineering, Texas A&M University, College Station, USA · Department of Ocean Engineering, Texas A&M University, College Station, USA · American Bureau of Shipping, Spring, TX, USA

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.

Figures & tables

Figure 1: Simulation Framework Architecture (adopted from [ 10 ] )

Figure 2: Bathymetry-based terrain generation in Unity

Figure 3: Enhanced modular simulation framework

Panel of Figure 4 — Figure 4: Unity visualizations (a)Weather severity (b) Bathymetry representation.

Panel of Figure 5 — Figure 5: Controller performance, vessel trajectories, and collision avoidance trigger from the situational awareness module under (a) ideal and (b) severe weather using nominal sensor.

Weather	MPD V1 [m]	MPD V2 [m]	$RMSE_{V}$ [m/s]	$RMSE_{\psi}$ [deg]
Ideal	606	701	1.1	0.343
Moderate	607	784	1.1	0.350
Severe	610	1137	1.4	0.355

Table 1: Performance Indicators under different weather conditions.

Panel of Figure 6 — Figure 6: Controller performance and trajectory under severe weather conditions for (a) high-power radar and (b) low-power radar configurations.

Radar	MPD V1 [m]	MPD V2 [m]	$RMSE_{V}$ [m/s]	$RMSE_{\psi}$ [deg]
High-power	606	771	1.3	0.347
Nominal Power	610	1137	1.4	0.355
Low-power	605	1089	1.5	0.412

Table 2: Performance indicators under varying radar sensor configurations.

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Mar 30, 2026J/K move · Enter open · S save

From Vessel Trajectories to Safety-Critical Encounter Scenarios: A Generative AI Framework for Autonomous Ship Digital Testing

Sijin Sun, Liangbin Zhao, Xiuju Fu

A*STAR IAIC Singapore

Trajectory Generation

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Digital testing has emerged as a key paradigm for the development and verification of autonomous maritime navigation systems, yet the availability of realistic and diverse safety-critical encounter scenarios remains limited. Existing approaches either rely on handcrafted templates, which lack realism, or extract cases directly from historical data, which cannot systematically expand rare high-risk situations. This paper proposes a data-driven framework that converts large-scale Automatic Identification System (AIS) trajectories into structured safety-critical encounter scenarios. The framework combines generative trajectory modeling with automated encounter pairing and temporal parameterization to enable scalable scenario construction while preserving real traffic characteristics. To enhance trajectory realism and robustness under noisy AIS observations, a multi-scale temporal variational autoencoder is introduced to capture vessel motion dynamics across different temporal resolutions. Experiments on real-world maritime traffic flows demonstrate that the proposed method improves trajectory fidelity and smoothness, maintains statistical consistency with observed data, and enables the generation of diverse safety-critical encounter scenarios beyond those directly recorded. The resulting framework provides a practical pathway for building scenario libraries to support digital testing, benchmarking, and safety assessment of autonomous navigation and intelligent maritime traffic management systems. Code is available at https://anonymous.4open.science/r/traj-gen-anonymous-review.

Abstract

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