  3D object detection is a common function within the perception system of an
autonomous vehicle and outputs a list of 3D bounding boxes around objects of
interest. Various 3D object detection methods have relied on fusion of
different sensor modalities to overcome limitations of individual sensors.
However, occlusion, limited field-of-view and low-point density of the sensor
data cannot be reliably and cost-effectively addressed by multi-modal sensing
from a single point of view. Alternatively, cooperative perception incorporates
information from spatially diverse sensors distributed around the environment
as a way to mitigate these limitations. This article proposes two schemes for
cooperative 3D object detection using single modality sensors. The early fusion
scheme combines point clouds from multiple spatially diverse sensing points of
view before detection. In contrast, the late fusion scheme fuses the
independently detected bounding boxes from multiple spatially diverse sensors.
We evaluate the performance of both schemes, and their hybrid combination,
using a synthetic cooperative dataset created in two complex driving scenarios,
a T-junction and a roundabout. The evaluation shows that the early fusion
approach outperforms late fusion by a significant margin at the cost of higher
communication bandwidth. The results demonstrate that cooperative perception
can recall more than 95% of the objects as opposed to 30% for single-point
sensing in the most challenging scenario. To provide practical insights into
the deployment of such system, we report how the number of sensors and their
configuration impact the detection performance of the system.
