VVSim: A Large-Scale Aerial-Ground Dataset and Benchmark for Cooperative Perception

The increasing diversity and density of traffic participantshave made modern driving environments highly complex, posing greatchallenges to reliable perception in autonomous driving. Single-vehicleperception is fundamentally limited by sensor range and occlusions, whilevehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) cooperationonly partially mitigate these issues due to their fixed or ground-levelviewpoints. In contrast, aerial platforms offer global and flexible sens-ing perspectives that can dynamically adjust their positions to providebroader spatial coverage and reduce blind zones. These advantages mo-tivate the exploration of aerial-ground cooperative perception (AGCP).However, progress in this area is hindered by the lack of public datasetsand standardized benchmarks. To bridge this gap, we introduce VVSim,a large-scale dataset for AGCP that provides synchronized multimodaldata and state information from both vehicles and unmanned aerial ve-hicles (UAVs). VVSim consists of about 61k fully annotated frames span-ning diverse interaction scenarios (e.g., cut-in, lane change), 5 weatherconditions (sunny, foggy, rainy, cloudy, snowy), and 11 scenes (e.g., city,town, university, highway, mountain). In total, it comprises 255k Li-DAR sweeps and 3.5M images (RGB, semantic segmentation, and depth)collected from vehicles and UAVs, along with detailed annotations for2D/3D bounding boxes, object trajectories, and agent states. To supportunified evaluation, we further propose VVFormer, a novel architecturethat integrates multi-agent and multimodal features from UAVs and ve-hicles. Extensive experiments on VVSim demonstrate that VVFormerachieves superior performance, significantly outperforming strong V2Vand V2I cooperative baselines on 3D perception tasks. The code is avail-able at https://github.com/LOTEAT/vvdetection3d.