Cooperative perception has been widely used in autonomous driving to
alleviate the inherent limitation of single automated vehicle perception. To
enable cooperation, vehicle-to-vehicle (V2V) communication plays an
indispensable role. This work analyzes the performance of cooperative
perception accounting for communications channel impairments. Different fusion
methods and channel impairments are evaluated. A new late fusion scheme is
proposed to leverage the robustness of intermediate features. In order to
compress the data size incurred by cooperation, a convolution neural
network-based autoencoder is adopted. Numerical results demonstrate that
intermediate fusion is more robust to channel impairments than early fusion and
late fusion, when the SNR is greater than 0 dB. Also, the proposed fusion
scheme outperforms the conventional late fusion using detection outputs, and
autoencoder provides a good compromise between detection accuracy and bandwidth
usage.

合作感知在自动驾驶中得到广泛应用，本研究分析了考虑通信信道损伤的合作感知性能。通过评估不同的融合方法和通道损伤，提出了一种新的后期融合方案，并采用基于卷积神经网络的自动编码器来压缩合作造成的数据大小。数值结果表明，当信噪比大于 0 dB 时，中期融合对信道损伤更具鲁棒性，提出的融合方案优于常规后期融合，并且自动编码器在检测精度和带宽使用之间提供了很好的折中。