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

2025-05-06

AI Summary

To address the insufficient robustness of multi-vehicle cooperative perception in vehicle-to-vehicle (V2V) communications under Rician fading and time-varying non-stationary channel distortions, this paper proposes a Joint Weighting and Denoising (Coop-WD) framework. Methodologically, it innovatively integrates hierarchical self-supervised contrastive learning with conditional diffusion probabilistic modeling to jointly optimize channel-aware feature weighting and distortion-resilient feature reconstruction. Furthermore, a lightweight variant, Coop-WD-eco, is introduced, enabling dynamic activation or deactivation of the denoising module based on real-time channel quality estimation. Experimental results demonstrate that Coop-WD significantly outperforms baseline methods across diverse channel distortion scenarios. Coop-WD-eco reduces computational overhead by 50% under severe distortions while maintaining near-baseline perception accuracy when channel conditions improve—achieving superior robustness, generalization, and adaptive energy efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Enhances cooperative perception under V2V communication impairments
• Addresses generalization to varying levels of V2V channel distortions
• Reduces computational cost while maintaining perception accuracy

Innovation

• Joint weighting and denoising framework for V2V
• Hierarchical self-supervised and diffusion models
• Selective denoising to reduce computational cost