Collective PV-RCNN: A Novel Fusion Technique using Collective Detections
  for Enhanced Local LiDAR-Based Perception
Sven Teufel Jörg Gamerdinger Georg Volk Oliver Bringmann
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