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.

翻译：交通运输行业近期对无人水面航行器（USV）展现出显著兴趣，尤其针对港口及内河航道运输领域。这些系统可提升运营效率与安全性，这与欧盟《绿色新政》等倡议推动内河航道利用率提升的趋势高度契合。与此同时，合格人员短缺问题正加速自主解决方案的推广。然而，目前明显缺乏用于开发与评估此类解决方案的开源高保真仿真框架及数据集。为应对这些挑战，我们提出面向内河及港口环境的自主航运研究专用开源仿真框架——AirSim for Surface Vehicles（ASVSim）。该框架将船舶动力学仿真与海洋传感器模拟能力（包括雷达与摄像头系统）相结合，并支持生成用于训练计算机视觉模型及强化学习（RL）智能体的合成数据集。基于Cosys-AirSim构建的ASVSim为自主导航算法开发及合成数据集生成提供了综合平台。该仿真器支持传统控制方法与基于深度学习的路线研究。通过水路分割与自主导航实验，我们展示了仿真器在这些研究领域的应用能力。ASVSim作为MIT许可协议下的开源项目发布，使海洋工程学界能够更广泛地开展自主导航研究。详见https://github.com/BavoLesy/ASVSim。