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

翻译：环境的全面感知对于自动驾驶车辆的安全运行至关重要。然而，由于遮挡、传感器范围有限或环境影响，自动驾驶车辆的感知能力受到限制。集体感知（CP）旨在通过实现车辆间信息交换来缓解这些问题。CP中的一个主要挑战是所交换信息的融合。由于早期融合方法需要巨大的带宽需求以及中间融合方法存在可互换性问题，只有共享检测的后期融合是实用的。当前的后期融合方法忽视了局部检测中的有价值信息，因此我们提出了一种新型融合方法，将协作车辆的检测结果融合到基于局部激光雷达的检测流程中。为此，我们推出了集体PV-RCNN（CPV-RCNN），该方法扩展了PV-RCNN++框架以融合集体检测。代码可在 https://github.com/ekut-es 获取。