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AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration

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arxiv 2506.19283 v3 pith:RUDIUBMF submitted 2025-06-24 cs.CV cs.AIcs.RO

AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration

Xiangbo Gao , Yuheng Wu , Fengze Yang , Xuewen Luo , Keshu Wu , Xinghao Chen , Yuping Wang , Chenxi Liu
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Yang Zhou Zhengzhong Tu
This is my paper
classification cs.CV cs.AIcs.RO
keywords datasetairv2x-perceptiondrivingadvantagescostsdeploymentdevelopmentfixed
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Adaptation-Free Heterogeneous Collaborative Perception with Unseen Agent Configurations

    cs.CV 2026-05 unverdicted novelty 8.0

    ALF converts box-level messages from unseen agents into ego-compatible features via pseudo-BEV maps, enabling zero-shot heterogeneous collaboration and improving mAP by 35.91% relative on V2X-Real.

  2. Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance

    cs.RO 2026-05 unverdicted novelty 7.0

    Introduces and evaluates an intent-first aerial V2V protocol using C-V2X sidelink for tactical coordination and separation in dense UTM operations, showing viability in moderate regimes with fallback at high density.

  3. Can Aerial VLA Models Cooperate? Evaluating Closed-Loop Air-Ground Coordination with CARLA-Air

    cs.RO 2026-05 unverdicted novelty 6.0

    Introduces CARLA-Air simulator for air-ground VLA evaluation and shows that current aerial VLA models track ground partners but fail to achieve stable cooperative behavior under text-based interfaces.