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 [email protected] of 72.74, compared with 45.54 using a single USV.
Figures & tables
Fig. 1: Motivating cooperative perception scenario in MMUSV-Sim. The target vessel is occluded from USV 1 by the island, while USV 2 and USV 3 observe it from complementary viewpoints. Their observations can be expressed in a shared BEV frame for cooperative perception.
Fig. 2: Overall architecture of MMUSV-Sim. Manual and batch-generated scenario configurations instantiate the UE5 and Project AirSim environment. The control and acquisition interface coordinates vessel motion, multimodal recording while scene evolution is paused, world-state capture, and offline generation of per-agent annotations and structured exports.
Fig. 3: Visual diversity and environmental controllability of MMUSV-Sim. Top row: representative island, port, and open sea topologies, together with the vessel asset library. Middle row: time-of-day variations under a fixed scene configuration. Bottom row: weather variations under a fixed scene configuration.
Fig. 4: Manual scenario configuration and runtime instantiation in MMUSV-Sim. (a) The BEV interface specifies vessel assets, spline trajectories, and environment settings. (b) The corresponding UE5 scene is instantiated from the stored scenario configuration.
Fig. 5: Sensor and annotation visualizations generated by MMUSV-Sim, including surround-view RGB images, depth maps, semantic segmentation, radar PPI, top-view semantic maps, and BEV-projected LiDAR points.
Wave Setting
Heave RMS (m)
Roll RMS ( ∘ )
Pitch RMS ( ∘ )
W0 ( H=0.5 m)
0.0274
0.2054
0.0684
W1 ( H=1.5 m)
0.0818
0.6134
0.2062
W2 ( H=2.5 m)
0.1379
1.0345
0.3395
W3 ( H=3.5 m)
0.1923
1.4421
0.4774
TABLE I: Vessel-pose perturbations under four simulator wave settings.
Scene
Valid Samples
Med. Center Err. (px)
Mask Contain.
Hull Occup.
Island
4,835
2.26
1.000
0.428
Open Sea
1,663
2.07
0.999
0.420
Port
3,856
1.85
0.999
0.420
Overall
10,354
2.08
0.999
0.424
TABLE II: Projected-box coverage and center alignment with top-view semantic renderings.
TABLE III: LiDAR-based cooperative BEV vessel detection on the evaluation subset. AP@ τ uses a BEV IoU threshold of τ ; all metrics are percentages.
Perturbation
Setting
Late Fusion
Early Fusion
Clean
–
55.47
72.74
Delay
200 ms
52.25
69.26
Delay
400 ms
51.33
65.21
Delay
600 ms
46.25
58.06
Pose noise
(0.5m,0.5∘)
48.11
63.64
Pose noise
(1.0m,1.0∘)
40.46
46.94
TABLE IV: Sensitivity of cooperative BEV detection to pose noise and temporal delay. [email protected] values are percentages; pose settings denote (σxyz,σrpy) .
Fig. 6: Qualitative comparison of No Fusion, Late Fusion, and Early Fusion in a port scene. Red and blue boxes denote ground truth and predictions, respectively. In this port scene, Early Fusion better aligns the partially observed large-vessel box.