Distributed Multi-Target Tracking for Autonomous Vehicle Fleets
This addresses the challenge of efficient target tracking for autonomous vehicles in fleets, though it is incremental as it builds on existing distributed methods.
The paper tackles the problem of distributed multi-target tracking for autonomous vehicle fleets by proposing a scalable algorithm based on the alternating direction method of multipliers, which outperforms the Consensus Kalman Filter in approximating centralized estimates with fixed communication bandwidth, as demonstrated in simulations with 50 cars tracking 50 targets.
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.