  Fully autonomous driving systems require fast detection and recognition of
sensitive objects in the environment. In this context, intelligent vehicles
should share their sensor data with computing platforms and/or other vehicles,
to detect objects beyond their own sensors' fields of view. However, the
resulting huge volumes of data to be exchanged can be challenging to handle for
standard communication technologies. In this paper, we evaluate how using a
combination of different sensors affects the detection of the environment in
which the vehicles move and operate. The final objective is to identify the
optimal setup that would minimize the amount of data to be distributed over the
channel, with negligible degradation in terms of object detection accuracy. To
this aim, we extend an already available object detection algorithm so that it
can consider, as an input, camera images, LiDAR point clouds, or a combination
of the two, and compare the accuracy performance of the different approaches
using two realistic datasets. Our results show that, although sensor fusion
always achieves more accurate detections, LiDAR only inputs can obtain similar
results for large objects while mitigating the burden on the channel.
