Deep learning has been widely used in the perception (e.g., 3D object
detection) of intelligent vehicle driving. Due to the beneficial
Vehicle-to-Vehicle (V2V) communication, the deep learning based features from
other agents can be shared to the ego vehicle so as to improve the perception
of the ego vehicle. It is named as Cooperative Perception in the V2V research,
whose algorithms have been dramatically advanced recently. However, all the
existing cooperative perception algorithms assume the ideal V2V communication
without considering the possible lossy shared features because of the Lossy
Communication (LC) which is common in the complex real-world driving scenarios.
In this paper, we first study the side effect (e.g., detection performance
drop) by the lossy communication in the V2V Cooperative Perception, and then we
propose a novel intermediate LC-aware feature fusion method to relieve the side
effect of lossy communication by a LC-aware Repair Network (LCRN) and enhance
the interaction between the ego vehicle and other vehicles by a specially
designed V2V Attention Module (V2VAM) including intra-vehicle attention of ego
vehicle and uncertainty-aware inter-vehicle attention. The extensive experiment
on the public cooperative perception dataset OPV2V (based on digital-twin CARLA
simulator) demonstrates that the proposed method is quite effective for the
cooperative point cloud based 3D object detection under lossy V2V
communication.

本文研究了基于深度学习的车辆感知中 “合作感知” 环节中由于通信质量差而带来的性能损失，并提出了一种新颖的方法，利用 “LC-aware Repair Network” 和 “V2V Attention Module” 来增强车辆之间的互动，并在公共数据集 OPV2V 上进行了广泛实验，展示了在 “失真 V2V 通信” 环境下提高识别性能的有效性。