Cooperative Perception with Learning-Based V2V communications

• URL: http://arxiv.org/abs/2311.10336v1
• Date: Fri, 17 Nov 2023 05:41:23 GMT
• Title: Cooperative Perception with Learning-Based V2V communications
• Authors: Chenguang Liu, Yunfei Chen, Jianjun Chen, Ryan Payton, Michael Riley and Shuang-Hua Yang
• Abstract summary: This work analyzes the performance of cooperative perception accounting for communications channel impairments. 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.
• Score: 11.772899644895281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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