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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.19283 |
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| _version_ | 1866912461564674048 |
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| author | Gao, Xiangbo Wu, Yuheng Yang, Fengze Luo, Xuewen Wu, Keshu Chen, Xinghao Wang, Yuping Liu, Chenxi Zhou, Yang Tu, Zhengzhong |
| author_facet | Gao, Xiangbo Wu, Yuheng Yang, Fengze Luo, Xuewen Wu, Keshu Chen, Xinghao Wang, Yuping Liu, Chenxi Zhou, Yang Tu, Zhengzhong |
| contents | While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substantial deployment costs and the creation of "uncovered danger zones" in rural and suburban areas. We present AirV2X-Perception, a large-scale dataset that leverages Unmanned Aerial Vehicles (UAVs) as a flexible alternative or complement to fixed Road-Side Units (RSUs). Drones offer unique advantages over ground-based perception: complementary bird's-eye-views that reduce occlusions, dynamic positioning capabilities that enable hovering, patrolling, and escorting navigation rules, and significantly lower deployment costs compared to fixed infrastructure. Our dataset comprises 6.73 hours of drone-assisted driving scenarios across urban, suburban, and rural environments with varied weather and lighting conditions. The AirV2X-Perception dataset facilitates the development and standardized evaluation of Vehicle-to-Drone (V2D) algorithms, addressing a critical gap in the rapidly expanding field of aerial-assisted autonomous driving systems. The dataset and development kits are open-sourced at https://github.com/taco-group/AirV2X-Perception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration Gao, Xiangbo Wu, Yuheng Yang, Fengze Luo, Xuewen Wu, Keshu Chen, Xinghao Wang, Yuping Liu, Chenxi Zhou, Yang Tu, Zhengzhong Computer Vision and Pattern Recognition Artificial Intelligence Robotics While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substantial deployment costs and the creation of "uncovered danger zones" in rural and suburban areas. We present AirV2X-Perception, a large-scale dataset that leverages Unmanned Aerial Vehicles (UAVs) as a flexible alternative or complement to fixed Road-Side Units (RSUs). Drones offer unique advantages over ground-based perception: complementary bird's-eye-views that reduce occlusions, dynamic positioning capabilities that enable hovering, patrolling, and escorting navigation rules, and significantly lower deployment costs compared to fixed infrastructure. Our dataset comprises 6.73 hours of drone-assisted driving scenarios across urban, suburban, and rural environments with varied weather and lighting conditions. The AirV2X-Perception dataset facilitates the development and standardized evaluation of Vehicle-to-Drone (V2D) algorithms, addressing a critical gap in the rapidly expanding field of aerial-assisted autonomous driving systems. The dataset and development kits are open-sourced at https://github.com/taco-group/AirV2X-Perception. |
| title | AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2506.19283 |