Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication

• URL: http://arxiv.org/abs/2505.03528v1
• Date: Tue, 06 May 2025 13:38:35 GMT
• Title: Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication
• Authors: Chenguang Liu, Jianjun Chen, Yunfei Chen, Yubei He, Zhuangkun Wei, Hongjian Sun, Haiyan Lu, Qi Hao,
• Abstract summary: We propose a joint weighting and denoising framework, Coop-WD, to enhance cooperative perception subject to V2V channel impairments.<n>An efficient variant model, Coop-WD-eco, is proposed to selectively deactivate denoising to reduce processing overhead.
• Score: 23.039742419070805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cooperative perception, leveraging shared information from multiple vehicles via vehicle-to-vehicle (V2V) communication, plays a vital role in autonomous driving to alleviate the limitation of single-vehicle perception. Existing works have explored the effects of V2V communication impairments on perception precision, but they lack generalization to different levels of impairments. In this work, we propose a joint weighting and denoising framework, Coop-WD, to enhance cooperative perception subject to V2V channel impairments. In this framework, the self-supervised contrastive model and the conditional diffusion probabilistic model are adopted hierarchically for vehicle-level and pixel-level feature enhancement. An efficient variant model, Coop-WD-eco, is proposed to selectively deactivate denoising to reduce processing overhead. Rician fading, non-stationarity, and time-varying distortion are considered. Simulation results demonstrate that the proposed Coop-WD outperforms conventional benchmarks in all types of channels. Qualitative analysis with visual examples further proves the superiority of our proposed method. The proposed Coop-WD-eco achieves up to 50% reduction in computational cost under severe distortion while maintaining comparable accuracy as channel conditions improve.

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