Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication
Researchers have introduced Coop-WD, a novel framework designed to improve cooperative perception in autonomous vehicles by mitigating the effects of vehicle-to-vehicle (V2V) communication impairments. The system employs a hierarchical approach, utilizing self-supervised contrastive and conditional diffusion models for feature enhancement at both vehicle and pixel levels. An efficient variant, Coop-WD-eco, is also proposed to reduce computational costs by selectively disabling denoising, demonstrating comparable accuracy under improving channel conditions. AI
IMPACT Enhances robustness of perception systems in autonomous vehicles by addressing V2V communication challenges.