Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception
This work addresses the problem of limited perception due to occlusions and sensor ranges for autonomous vehicles, offering an incremental improvement in late fusion techniques for collective perception.
The paper tackles the challenge of fusing exchanged detections in collective perception for autonomous vehicles by proposing Collective PV-RCNN, a novel late fusion method that integrates collective detections into the local LiDAR-based detection pipeline, achieving improved performance on the nuScenes dataset with a 2.1% increase in mAP and 2.5% in NDS.
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