Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention

Collaborative perception (CP) leverages visual data from connected and autonomous vehicles (CAV) to enhance an ego vehicle's field of view (FoV). Despite recent progress, current CP methods expand the ego vehicle's 360-degree perceptual range almost equally, which faces two key challenges. Firstly, in areas with uneven traffic distribution, focusing on directions with little traffic offers limited benefits. Secondly, under limited communication budgets, allocating excessive bandwidth to less critical directions lowers the perception accuracy in more vital areas. To address these issues, we propose Direct-CP, a proactive and direction-aware CP system aiming at improving CP in specific directions. Our key idea is to enable an ego vehicle to proactively signal its interested directions and readjust its attention to enhance local directional CP performance. To achieve this, we first propose an RSU-aided direction masking mechanism that assists an ego vehicle in identifying vital directions. Additionally, we design a direction-aware selective attention module to wisely aggregate pertinent features based on ego vehicle's directional priorities, communication budget, and the positional data of CAVs. Moreover, we introduce a direction-weighted detection loss (DWLoss) to capture the divergence between directional CP outcomes and the ground truth, facilitating effective model training. Extensive experiments on the V2X-Sim 2.0 dataset demonstrate that our approach achieves 19.8% higher local perception accuracy in interested directions and 2.5% higher overall perception accuracy than the state-of-the-art methods in collaborative 3D object detection tasks. Codes are available at https://github.com/yihangtao/Directed-CP.git.