自動運転車群のための分散マルチターゲット追跡
Distributed Multi-Target Tracking for Autonomous Vehicle Fleets
車車間通信ネットワーク上でADMMを用い、測定値の共有なしに集中型MAP推定に近い追跡を実現する分散アルゴリズムを提案し、CARLAで50台の車両追跡を実証した。
著者: Ola Shorinwa, Javier Yu, Trevor Halsted, Alex Koufos, Mac Schwager
分類: cs.RO
原文アブストラクト
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.
