### Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

@inproceedings{shorinwa_distributed_2020,
title = {Distributed {Multi}-{Target} {Tracking} for {Autonomous} {Vehicle} {Fleets}},
isbn = {978-1-72817-395-5},
url = {https://ieeexplore.ieee.org/document/9197241/},
abstract = {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.},
language = {en},
urldate = {2021-01-05},
booktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
publisher = {IEEE},
author = {Shorinwa, Ola and Yu, Javier and Halsted, Trevor and Koufos, Alex and Schwager, Mac},
month = may,
year = {2020},
keywords = {target\_tracking},
pages = {3495--3501},
month_numeric = {5}
}