V2V Cooperative Perception With Adaptive Communication Loss for Autonomous Driving

Engineering notes

The results demonstrate that AccBEV has state-of-the-art performance in both single- and multi-vehicle collaborative perception, which demonstrates AccBEV’s superiority in real-world traffic scenarios and different communication states.

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

Bird’s eye view (BEV) perception provides autonomous vehicles with a unified space closer to the actual physical world by projecting sensor data onto a unified two-dimensional plane. However, occlusion can impede the perceptual range and ability of single-vehicle in complex traffic scenarios. Moreover, most collaborative perception methods assume ideal vehicle-to-vehicle (V2V) communication, ignoring feature loss during sharing. To alleviate these problems, we propose a multi-vehicle collaborative BEV perception network with adaptive communication loss, called AccBEV, based on conditional variational inference. Specifically, the AccBEV backbone is a transformer-based variable flow transformation network designed to reduce environmental characteristic loss due to occlusion in single-vehicle perception. Additionally, an adaptive lossy channel module is designed to repair feature loss caused by varying signal-to-noise ratio (SNR) communication. The performance of AccBEV is evaluated on the OPV2V and NuScenes datasets. The results demonstrate that AccBEV has state-of-the-art performance in both single- and multi-vehicle collaborative perception, which demonstrates AccBEV’s superiority in real-world traffic scenarios and different communication states.

Links and sources

• Official / arXiv page

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