发表机构
School of Intelligent Systems Engineering, Sun Yat-sen University; School of Intelligent Imagery Engineering, Beijing Film Academy（中山大学智能工程学院; 北京电影学院智能影像工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于Unreal Engine 5和Project AirSim的MMUSV-Sim仿真与数据生成平台，用于多USV协同感知，实验表明其生成的数据集可使激光雷达协同BEV船舶检测的AP@0.5达72.74，显著优于单USV方案。
AI 中文摘要
多无人水面艇（USV）之间的协同感知可整合互补观测，将海上目标感知范围扩展至单平台的视距和视场之外。规模化开发此类系统需要统一的工作流，以支持可配置的多USV场景、多模态采集及共享标注。我们提出MMUSV-Sim，这是一款基于Unreal Engine 5和Project AirSim构建的感知导向型海上仿真与数据生成平台，提供岛屿、公海和港口环境，可配置的天气、时段和波浪条件，多样化的船舶资产库，以及基于样条曲线的多船舶运动。MMUSV-Sim可采集多USV的RGB、深度、语义、激光雷达（LiDAR）和雷达观测数据，并捕获统一的世界状态以导出各智能体的标注。实验验证，配置的波浪设置可使船舶产生预期的升沉、横摇和纵摇变化，同时评估投影标注与语义渲染之间的几何一致性。在基于生成的多USV数据集开展的激光雷达协同鸟瞰图（BEV）船舶检测实验中，早期融合方法的AP@0.5达72.74，而单USV的AP@0.5仅为45.54。
英文摘要
Cooperative perception among multiple unmanned surface vehicles (USVs) combines complementary observations to extend maritime target sensing beyond the view range and field of a single platform. Developing such systems at scale calls for a unified workflow for configurable multi-USV scenarios, multimodal acquisition, and shared annotations. We present MMUSV-Sim, a perception-oriented maritime simulation and data-generation platform built on Unreal Engine 5 and Project AirSim. It provides island, open-sea, and port environments; configurable weather, time of day, and wave conditions; a diverse vessel asset library; and spline-based multi-vessel motion. MMUSV-Sim acquires RGB, depth, semantic, LiDAR, and radar observations across multiple USVs and captures a common world state for per-agent annotation export. Experiments verify that the configured wave settings produce the intended changes in vessel heave, roll, and pitch, and evaluate the geometric consistency between projected annotations and semantic renderings. In LiDAR-based cooperative BEV vessel detection experiments on the generated multi-USV dataset, Early Fusion achieves an AP@0.5 of 72.74, compared with 45.54 using a single USV.
