mlsanityData to 14 Oct 2026

ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research

arXiv 2506.22174 ↗First posted 27 Jun 2025cs.RO cs.LGcited by 2

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Abstract (arXiv)

The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway transport. These systems can improve operational efficiency and safety, which is especially relevant in the European Union, where initiatives such as the Green Deal are driving a shift towards increased use of inland waterways. At the same time, a shortage of qualified personnel is accelerating the adoption of autonomous solutions. However, there is a notable lack of open-source, high-fidelity simulation frameworks and datasets for developing and evaluating such solutions. To address these challenges, we introduce AirSim for Surface Vehicles (ASVSim), an open-source simulation framework specifically designed for autonomous shipping research in inland and port environments. The framework combines simulated vessel dynamics with marine sensor simulation capabilities, including radar and camera systems and supports the generation of synthetic datasets for training computer vision models and reinforcement learning (RL) agents. Built upon Cosys-AirSim, ASVSim provides a comprehensive platform for developing autonomous navigation algorithms and generating synthetic datasets. The simulator supports research of both traditional control methods and deep learning-based approaches. Through experiments in waterway segmentation and autonomous navigation, we demonstrate the capabilities of the simulator in these research areas. ASVSim is provided as an open-source project under the MIT license, making autonomous navigation research accessible to a larger part of the ocean engineering community. See https://github.com/BavoLesy/ASVSim.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 69 references that are in the map. First posted on arXiv 27 Jun 2025.

Builds on

17 of 69 references.

References · 17

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  4. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
  5. 2019CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicitycited by 95
  6. 2023UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Frameworkcited by 17
  7. 2018PythonRobotics: a Python code collection of robotics algorithmscited by 12
  8. 2023The Second Monocular Depth Estimation Challengecited by 12
  9. 2025Stonefish: Supporting Machine Learning Research in Marine Roboticscited by 11
  10. 2022Temporal Context for Robust Maritime Obstacle Detectioncited by 10
  11. 2025OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Frameworkcited by 10
  12. 2023Cosys-AirSim: A Real-Time Simulation Framework Expanded for Complex Industrial Applicationscited by 8
5 more
  1. 2023Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learningcited by 2
  2. 2024A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehiclescited by 2
  3. 2022Physical LiDAR Simulation in Real-Time Enginecited by 1
  4. 2023A novel model-based parameters estimation combining local optimization and global optimization of nonlinear ship models with physical experiment datasetcited by 1
  5. 2024Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shippingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Sim-to-Real RL for ASVs using SysID
  2. 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation
  3. 2025Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
  4. 2024Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles
  5. 2024Digital Twin of Autonomous Surface Vessels for Safe Maritime Navigation Enabled through Predictive Modeling and Reinforcement Learning ↗
  6. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
  7. 2026World-Model-Grounded LLM Planning for AUV and ASV Navigation Near Offshore Wind Farms
  8. 2026Language-Grounded Semantic Target Navigation for Autonomous Surface Vehicles ↗