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Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing

Tim Brüdigam, Alexandre Capone, Sandra Hirche, Dirk Wollherr, Marion Leibold

This work has been accepted to the ICRA 2021 workshop 'Opportunities and Challenges with Autonomous Racing'

Abstract: 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.

Submitted to arXiv on 25 May. 2021