  To reduce the amount of transmitted data, feature map based fusion is
recently proposed as a practical solution to cooperative 3D object detection by
autonomous vehicles. The precision of object detection, however, may require
significant improvement, especially for objects that are far away or occluded.
To address this critical issue for the safety of autonomous vehicles and human
beings, we propose a cooperative spatial feature fusion (CoFF) method for
autonomous vehicles to effectively fuse feature maps for achieving a higher 3D
object detection performance. Specially, CoFF differentiates weights among
feature maps for a more guided fusion, based on how much new semantic
information is provided by the received feature maps. It also enhances the
inconspicuous features corresponding to far/occluded objects to improve their
detection precision. Experimental results show that CoFF achieves a significant
improvement in terms of both detection precision and effective detection range
for autonomous vehicles, compared to previous feature fusion solutions.
