  The reliability of current autonomous driving systems is often jeopardized in
situations when the vehicle's field-of-view is limited by nearby occluding
objects. To mitigate this problem, vehicle-to-vehicle communication to share
sensor information among multiple autonomous driving vehicles has been
proposed. However, to enable timely processing and use of shared sensor data,
it is necessary to constrain communication bandwidth, and prior work has done
so by restricting the number of other cooperative vehicles and randomly
selecting the subset of vehicles to exchange information with from all those
that are within communication range. Although simple and cost effective from a
communication perspective, this selection approach suffers from its
susceptibility to missing those vehicles that possess the perception
information most critical to navigation planning. Inspired by recent
multi-agent path finding research, we propose a novel selective communication
algorithm for cooperative perception to address this shortcoming. Implemented
with a lightweight perception network and a previously developed control
network, our algorithm is shown to produce higher success rates than a random
selection approach on previously studied safety-critical driving scenario
simulations, with minimal additional communication overhead.
