Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

  address = {Paris, France},
  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}