  We present a scalable distributed target tracking algorithm based on the
alternating direction method of multipliers that is well-suited for a fleet of
autonomous cars communicating over a vehicle-to-vehicle network. Each sensing
vehicle communicates with its neighbors to execute iterations of a Kalman
filter-like update such that each agent's estimate approximates the centralized
maximum a posteriori estimate without requiring the communication of
measurements. We show that our method outperforms the Consensus Kalman Filter
in recovering the centralized estimate given a fixed communication bandwidth.
We also demonstrate the algorithm in a high fidelity urban driving simulator
(CARLA), in which 50 autonomous cars connected on a time-varying communication
network track the positions and velocities of 50 target vehicles using on-board
cameras.
