In this paper, we extend a framework that we developed earlier for coordination of connected and automated vehicles (CAVs) at a signal-free intersection to incorporate uncertainty. Using the possibly noisy observations of actual time trajectories and leveraging Gaussian process regression, we learn the bounded confidence intervals for deviations from the nominal trajectories of CAVs online. Incorporating these confidence intervals, we reformulate the trajectory planning as a robust coordination problem, the solution of which guarantees that constraints in the system are satisfied in the presence of bounded deviations from the nominal trajectories. We demonstrate the effectiveness of our extended framework through a numerical simulation.
@article{arxiv.2103.03313,
title = {Robust Learning-Based Trajectory Planning for Emerging Mobility Systems},
author = {Behdad Chalaki and Andreas A. Malikopoulos},
journal= {arXiv preprint arXiv:2103.03313},
year = {2022}
}