Cooperative perception with learning-based V2V communications

Liu, C., Chen, Y., Chen, J., Payton, R., Riley, M. and Yang, S.-H. ORCID: https://orcid.org/0000-0003-0717-5009 (2023) Cooperative perception with learning-based V2V communications. IEEE Wireless Communications Letters, 12 (11). pp. 1831-1835. ISSN 2162-2345 doi: 10.1109/LWC.2023.3295612

Abstract/Summary

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 letter 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/112658
Identification Number/DOI	10.1109/LWC.2023.3295612
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	IEEE
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