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

TL;DR

This paper introduces Coop-WD, a novel framework that enhances cooperative perception in autonomous driving by mitigating V2V communication impairments through hierarchical feature enhancement and an efficient denoising variant.

Contribution

The paper presents a joint weighting and denoising framework with hierarchical models and an efficient variant, improving perception robustness under diverse V2V channel impairments.

Findings

Outperforms benchmarks in various channel conditions

Achieves up to 50% reduction in computational cost with maintained accuracy

Effectively handles Rician fading and time-varying distortions

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.