Cooperative Perception with Learning-Based V2V communications
Chenguang Liu Yunfei Chen Jianjun Chen Ryan Payton Michael Riley Shuang-Hua Yang
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.