  Accurate detection of objects in 3D point clouds is a key problem in
autonomous driving systems. Collaborative perception can incorporate
information from spatially diverse sensors and provide significant benefits for
improving the perception accuracy of autonomous driving systems. In this work,
we consider that the autonomous vehicle uses local point cloud data and
combines information from neighboring infrastructures through wireless links
for cooperative 3D object detection. However, information sharing among vehicle
and infrastructures in predefined communication schemes may result in
communication congestion and/or bring limited performance improvement. To this
end, we propose a novel collaborative 3D object detection framework that
consists of three components: feature learning networks that map point clouds
into feature maps; an efficient communication block that propagates compact and
fine-grained query feature maps from vehicle to support infrastructures and
optimizes attention weights between query and key to refine support feature
maps; a region proposal network that fuses local feature maps and weighted
support feature maps for 3D object detection. We evaluate the performance of
the proposed framework using a synthetic cooperative dataset created in two
complex driving scenarios: a roundabout and a T-junction. Experiment results
and bandwidth usage analysis demonstrate that our approach can save
communication and computation costs and significantly improve detection
performance under different detection difficulties in all scenarios.
