# AGC-Drive: A Large-Scale Dataset for Real-World Aerial-Ground Collaboration in Driving Scenarios

arXiv: https://arxiv.org/abs/2506.16371 · HTML: https://mlsanity.com/paper/2506.16371/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [LiDAR-Camera Calibration using 3D-3D Point correspondences](https://mlsanity.com/paper/1705.09785/) (2017)

## Built on by (most cited first, 11 of 11)

- [AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration](https://mlsanity.com/paper/2506.19283/) (2025)
- [Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition](https://mlsanity.com/paper/2507.21610/) (2025)
- [OpenCOOD-Air: Prompting Heterogeneous Ground-Air Collaborative Perception with Spatial Conversion and Offset Prediction](https://mlsanity.com/paper/2603.13919/) (2026)
- [VL-UniTrack: A Unified Framework with Visual-Language Prompts for UAV-Ground Visual Tracking](https://mlsanity.com/paper/2605.04574/) (2026)
- [AeroGround: A Comprehensive Benchmark for Aerial-Ground Collaborative Reasoning](https://mlsanity.com/paper/2608.14721/) (2026)
- [G-ray: Ray-Level Relative Geometric Position Encoding in Multi-View Vision Transformers under Camera Heterogeneity](https://mlsanity.com/paper/2609.15018/) (2026)
- [GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes](https://mlsanity.com/paper/2604.19411/) (2026)
- [Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement](https://mlsanity.com/paper/2605.00634/) (2026)
- [Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework](https://mlsanity.com/paper/2606.18841/) (2026)
- [COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts](https://mlsanity.com/paper/2608.18479/) (2026)
- [CERF: Communication-Efficient and Retraining-Free Collaborative Perception](https://mlsanity.com/paper/2609.00951/) (2026)

## Abstract (arXiv)

By sharing information across multiple agents, collaborative perception helps autonomous vehicles mitigate occlusions and improve overall perception accuracy. While most previous work focus on vehicle-to-vehicle and vehicle-to-infrastructure collaboration, with limited attention to aerial perspectives provided by UAVs, which uniquely offer dynamic, top-down views to alleviate occlusions and monitor large-scale interactive environments. A major reason for this is the lack of high-quality datasets for aerial-ground collaborative scenarios. To bridge this gap, we present AGC-Drive, the first large-scale real-world dataset for Aerial-Ground Cooperative 3D perception. The data collection platform consists of two vehicles, each equipped with five cameras and one LiDAR sensor, and one UAV carrying a forward-facing camera and a LiDAR sensor, enabling comprehensive multi-view and multi-agent perception. Consisting of approximately 80K LiDAR frames and 360K images, the dataset covers 14 diverse real-world driving scenarios, including urban roundabouts, highway tunnels, and on/off ramps. Notably, 17% of the data comprises dynamic interaction events, including vehicle cut-ins, cut-outs, and frequent lane changes. AGC-Drive contains 350 scenes, each with approximately 100 frames and fully annotated 3D bounding boxes covering 13 object categories. We provide benchmarks for two 3D perception tasks: vehicle-to-vehicle collaborative perception and vehicle-to-UAV collaborative perception. Additionally, we release an open-source toolkit, including spatiotemporal alignment verification tools, multi-agent visualization systems, and collaborative annotation utilities. The dataset and code are available at https://github.com/PercepX/AGC-Drive.