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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.03528 |
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| _version_ | 1866908351934234624 |
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| author | Liu, Chenguang Chen, Jianjun Chen, Yunfei He, Yubei Wei, Zhuangkun Sun, Hongjian Lu, Haiyan Hao, Qi |
| author_facet | Liu, Chenguang Chen, Jianjun Chen, Yunfei He, Yubei Wei, Zhuangkun Sun, Hongjian Lu, Haiyan Hao, Qi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication Liu, Chenguang Chen, Jianjun Chen, Yunfei He, Yubei Wei, Zhuangkun Sun, Hongjian Lu, Haiyan Hao, Qi Computer Vision and Pattern Recognition 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. |
| title | Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.03528 |