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.
@article{arxiv.2311.10336,
title = {Cooperative Perception with Learning-Based V2V communications},
author = {Chenguang Liu and Yunfei Chen and Jianjun Chen and Ryan Payton and Michael Riley and Shuang-Hua Yang},
journal= {arXiv preprint arXiv:2311.10336},
year = {2023}
}