AVCPNet: An AAV-Vehicle Collaborative Perception Network for 3-D Object Detection

Engineering notes

Our code will be released at https://github.com/wyccoo/uvcp.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

With the advancement of collaborative perception, the role of autonomous aerial vehicle (AAV)–vehicle collaborative perception has become increasingly significant. The demand for collaborative perception from various perspectives to construct comprehensive perceptual information is rising. However, challenges emerge due to differences in the field of view (FOV) between cross-domain agents and their varying sensitivities to image information. Furthermore, accurate depth information is essential for collaboration to transform image features into bird’s eye view (BEV) features. To address these challenges, we propose a framework specifically designed for aerial-ground collaboration. First, to address the deficiency of datasets for aerial-ground collaboration, we have developed a virtual dataset named V2U-COO for our research. Second, we design a cross-domain cross-adaptation (CDCA) module to align the target information obtained from different domains, thereby achieving more accurate perception results. Finally, we introduce a collaborative depth optimization (CDO) module to obtain more precise depth estimation results, leading to more accurate perception results. We conduct extensive experiments on both our virtual dataset and a public dataset to validate the effectiveness of our framework. Our method resolves the feature fusion issue under significant height differences, a challenge that previous BEV generation methods struggled to address effectively. Our experiments on the V2U-COO and DAIR-V2X datasets demonstrate improvements in detection accuracy of 6.1% and 2.7%, respectively. Our code will be released at https://github.com/wyccoo/uvcp.

Links and sources

• Official / arXiv page

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