A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method to plan overtaking maneuvers in autonomous racing. A Gaussian process is used to learn the behavior of the leading vehicle. Based on the outputs of the Gaussian process, a stochastic Model Predictive Control algorithm plans optimistic trajectories, such that the controlled autonomous race car is able to overtake the leading vehicle. The proposed method is tested in a simple simulation scenario.
@article{arxiv.2105.12236,
title = {Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing},
author = {Tim Brüdigam and Alexandre Capone and Sandra Hirche and Dirk Wollherr and Marion Leibold},
journal= {arXiv preprint arXiv:2105.12236},
year = {2021}
}
Comments
This work has been accepted to the ICRA 2021 workshop 'Opportunities and Challenges with Autonomous Racing'