Multi-agent collaborative perception enhances the perception coverage and robustness of intelligent agents in complex traffic environments by sharing and fusing information from diverse viewpoints. However, in vehicle-to-everything (V2X) scenarios, existing collaborative perception methods still face two major challenges: (i) an excessive reliance on raw sensor data to construct a global environmental representation, which causes communication bandwidth to surge dramatically as the perception range expands; and (ii) a disjoint treatment of traffic participants' trajectory and location information from sensor observations, neglecting the dual role of agents as both "perception sources" and "perception targets," thereby limiting further improvements in collaborative perception performance. To address these issues, this paper proposes a novel position prior–driven cooperative perception frame-work, Posicooper. The proposed method fully leverages widely distributed positioning information and introduces two key designs: in terms of communication strategy, Posicooper constructs a global confidence map based on position priors to identify critical perception regions and selectively transmit their feature information, significantly reducing communication overhead; and in terms of perception modeling, it introduces a critical region guidance module that generates guidance maps from position priors to direct the network's attention to important regions, thereby enhancing feature representation and improving detection accuracy. Experiments on two representative V2X collaborative perception data sets, OPV2V and V2XSet, demonstrate that Posicooper reduces communication costs by 28.6% while achieving an approximately 11.0% improvement in object detection accuracy, highlighting its strong potential for deployment in real-world traffic scenarios.
Qiu et al. (Mon,) studied this question.