  This paper addresses the task of joint multi-agent perception and planning,
especially as it relates to the real-world challenge of collision-free
navigation for connected self-driving vehicles. For this task, several
communication-enabled vehicles must navigate through a busy intersection while
avoiding collisions with each other and with obstacles. To this end, this paper
proposes a learnable costmap-based planning mechanism, given raw perceptual
data, that is (1) distributed, (2) uncertainty-aware, and (3)
bandwidth-efficient. Our method produces a costmap and uncertainty-aware
entropy map to sort and fuse candidate trajectories as evaluated across
multiple-agents. The proposed method demonstrates several favorable performance
trends on a suite of open-source overhead datasets as well as within a novel
communication-critical simulator. It produces accurate semantic occupancy
forecasts as an intermediate perception output, attaining a 72.5% average
pixel-wise classification accuracy. By selecting the top trajectory, the
multi-agent method scales well with the number of agents, reducing the hard
collision rate by up to 57% with eight agents compared to the single-agent
version.
