Comprehensive perception of the environment is crucial for the safe operation of autonomous vehicles. However, the perception capabilities of autonomous vehicles are limited due to occlusions, limited sensor ranges, or environmental influences. Collective Perception (CP) aims to mitigate these problems by enabling the exchange of information between vehicles. A major challenge in CP is the fusion of the exchanged information. Due to the enormous bandwidth requirement of early fusion approaches and the interchangeability issues of intermediate fusion approaches, only the late fusion of shared detections is practical. Current late fusion approaches neglect valuable information for local detection, this is why we propose a novel fusion method to fuse the detections of cooperative vehicles within the local LiDAR-based detection pipeline. Therefore, we present Collective PV-RCNN (CPV-RCNN), which extends the PV-RCNN++ framework to fuse collective detections. Code is available at https://github.com/ekut-es

自动驾驶汽车的安全操作需要对环境进行全面感知，而其感知能力受到遮挡、传感器范围限制或环境影响的限制。集体感知旨在通过车辆间的信息交换来减轻这些问题，其中融合交换信息是一个主要挑战。由于早期融合方法对带宽需求巨大以及中间融合方法的可替代性问题，只有共享检测的后期融合是可行的。然而，现有的后期融合方法忽视了本地检测的有价值信息，因此我们提出了一种新颖的融合方法来融合协作车辆在本地基于激光雷达的检测中的检测结果，即集体PV-RCNN（CPV-RCNN），该方法扩展了PV-RCNN++框架来融合集体检测。此外，代码可在此 https URL 中找到。

集体PV-RCNN：利用集体检测的新型融合技术增强基于局部LiDAR的感知