Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

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 [1]. 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.

Related Publications:

  1. O. Shorinwa, J. Yu, T. Halsted, A. Koufos, and M. Schwager, “Distributed Multi-Target Tracking for Autonomous Vehicle Fleets,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 3495–3501. [pdf] [bibtex]

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