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

TL;DR

This paper introduces Collective PV-RCNN, a new fusion method that enhances local LiDAR-based perception in autonomous vehicles by effectively integrating collective detections through a late fusion approach, improving environmental understanding.

Contribution

It proposes a novel fusion technique extending PV-RCNN++ to better utilize collective vehicle detections in local perception pipelines.

Findings

Improved detection accuracy with collective data integration

Enhanced perception robustness in occluded or challenging environments

Open-source code available for reproducibility

Abstract

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