Cooperative Perception With Learning-Based V2V Communications

• Published in: IEEE wireless communications letters 2023-11, Vol.12 (11), p.1-1
• Main Authors: Liu, Chenguang, Chen, Yunfei, Chen, Jianjun, Payton, Ryan, Riley, Michael, Yang, Shuang-Hua
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Convolution; Cooperation; Cooperative perception; Fading channels; Feature extraction; machine learning; Perception; Point cloud compression; Robustness (mathematics); Signal to noise ratio; Three-dimensional displays; Training; V2V communications
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2023.3295612

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.