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Comprehensive perception of the environment is crucial for the safe operationof autonomous vehicles. However, the perception capabilities of autonomousvehicles are limited due to occlusions, limited sensor ranges, or environmentalinfluences. Collective Perception (CP) aims to mitigate these problems byenabling the exchange of information between vehicles. A major challenge in CPis the fusion of the exchanged information. Due to the enormous bandwidthrequirement of early fusion approaches and the interchangeability issues ofintermediate fusion approaches, only the late fusion of shared detections ispractical. Current late fusion approaches neglect valuable information forlocal detection, this is why we propose a novel fusion method to fuse thedetections of cooperative vehicles within the local LiDAR-based detectionpipeline. Therefore, we present Collective PV-RCNN (CPV-RCNN), which extendsthe PV-RCNN++ framework to fuse collective detections. Code is available athttps://github.com/ekut-es

Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception